commodity investing and trading

Commodity

EDITED BY STINSON GIBNER

Investing and Trading

Com

modity Investing and Trading

Edited by Stinson G

ibner

For some, the trends - and volatility - in commodity markets in the 21st century can be summed up in one word: China. Yet those studying, trading and regulating these markets know that such deceptively simple descriptions cannot explain the subtle dynamics that drive supply and demand.

To be sure, China’s growth, industrialisation and consumerism have led to soaring demand for everyday commodities: China now accounts for over 40% of the demand of the world’s iron ore, copper, and other metals.

But commodity markets are now part of the integrated global financial system - buffeted by demand from growing emerging-market economies as much as by cash-rich funds eyeing commodities as an asset class.

Editor Stinson Gibner brings two decades of experience to Commodity Investing and Trading, having cut his teeth at Enron, Citadel, and Citigroup. He has assembled a team of industry experts whose contributions give the reader a unique view of the commodity markets.

Chapters focus on the fundamentals of major, key markets: • oilandpetroleum• metals• naturalgas• power• weather• grainsandoilseeds• coal.

Subsequent chapters detailing risk management, trading, and market insights including:• structuralalphastrategies• energyindextracking• enterpriseriskmanagement• CVAforcommodityderivatives• thefutureofmarketsinChina.

Contributors include:Michael Haigh Société Générale, Kamal Naqvi Credit Suisse, Mark Hooker State Street Global Advisors, Carlos Blanco NQuantX, LLC and Wang Xueqin Zhengzhou Commodity Exchange.

Commodity markets are an indelible element of financial markets and of society. For thousands of years they have shown themselves to be the most efficient way to assign the elemental resources necessary to advance. This fundamental quality has not changed.

What has changed is the breadth, depth and complexity of markets.

PEFC Certified

This book has been produced entirely from sustainable papers that are accredited as PEFC compliant.

www.pefc.org

Commodity Investing and Trading.indd 1 01/10/2013 15:36

Commodity Investing and Trading

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Commodity Investing and Trading

Stinson Gibner

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Published by Risk Books, a Division of Incisive Media Investments Ltd

Incisive Media32–34 Broadwick StreetLondon W1A 2HGTel: +44(0) 20 7316 9000E-mail: [email protected]: www.riskbooks.comwww.incisivemedia.com

© 2013 Incisive Media

ISBN 978 1 906348 84 7

British Library Cataloguing in Publication DataA catalogue record for this book is available from the British Library

Publisher: Nick CarverAssociate Editor: Alice LevickManaging Editor: Lewis O’Sullivan

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Warning: the doing of any unauthorised act in relation to this work may result in both civil andcriminal liability.

Every effort has been made to ensure the accuracy of the text at the time of publication, thisincludes efforts to contact each author to ensure the accuracy of their details at publication iscorrect. However, no responsibility for loss occasioned to any person acting or refraining fromacting as a result of the material contained in this publication will be accepted by the copyrightowner, the editor, the authors or Incisive Media.

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While best efforts have been intended for the preparation of this book, neither the publisher, theeditor nor any of the potentially implicitly affiliated organisations accept responsibility for anyerrors, mistakes and or omissions it may provide or for any losses howsoever arising from or inreliance upon its information, meanings and interpretations by any parties.

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About the Editors ix

About the Authors xi

Introduction xvii

PART I: COMMODITY MARKET FUNDAMENTALS

1 The Impact of Non-fundamental Information on Commodity

Markets 3

Michael S. Haigh

Société Générale Corporate and Investment Bank

2 The North American Natural Gas Market 25

Stinson Gibner

Whiteside Energy

3 A Day in the Life of Commodity Weather 65

Jose Marquez

Whiteside Energy

4 Oil and Petroleum Products: History and Fundamentals 75

Todd J. Gross

QERI LLC

5 Wholesale Power Markets 113

William Webster

RWE Supply and Trading

6 The Metals Markets 133

Kamal Naqvi

Credit Suisse

Contents

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7 Grains and Oilseeds 165

David Stack

Agrimax

8 Coal 207

Jay Gottlieb

PART II: TRADING AND INVESTMENT STRATEGIES

9 Farmland as an Investment 229

Greyson S. Colvin and T. Marc Schober

Colvin & Co. LLP

10 Agriculture Trading 249

Patrick O’Hern

Sugar Creek Investment Management

11 Quantitative Approaches to Capturing Commodity Risk

Premiums 295

Mark Hooker and Paul Lucek

State Street Global Advisors and SSARIS Advisors

12 Structural Alpha Strategies 307

Francisco Blanch; Gustavo Soares and Paul D. Kaplan

Bank of America Merrill Lynch; Macquarie FundingHolding Inc. and Morningstar, Inc.

13 Energy Index Tracking 337

Kostas Andriosopoulos

ESCP Europe Business School

PART III: MARKET DEVELOPMENTS AND RISK MANAGEMENT

14 Enterprise Risk Management for Energy and Commodity

Physical and Financial Portfolios 371

Carlos Blanco

NQuantX LLC and MTG Capital Management

COMMODITY INVESTING AND TRADING

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15 Credit Valuation Adjustment (CVA) for Energy and

Commodity Derivatives 389

Carlos Blanco; and Michael Pierce

NQuantX LLC and MTG Capital Management; NQuantX LLC

16 The Past, Present and Future of China’s Futures Market:

Trading Volume Analysis 409

Wang Xueqin

Zhengzhou Commodity Exchange

Index 439

CONTENTS

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Stinson Gibner is an analyst at Whiteside Energy, having worked inenergy risk management and trading since the early 1990s. He previ-ously headed the quantitative analytics team as a managing directorfor Citigroup Global Commodities, supporting offices in Houston,London and Singapore. Before joining Citigroup in 2005, Stinsonserved as a director at Citadel Investment Group LLC, where he wasresponsible for developing models and systems used for energytrading and risk management. Between 1992 and 2001, he worked inthe quantitative modelling group at Enron Corp. Stinson received hisBA in physics from Rice University and a PhD from Caltech.

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About the Editor

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Kostas Andriosopoulos is executive director of the Research Centrefor Energy Management at ESCP Europe Business School. Hisresearch interests include price modelling, financial engineering andthe application of risk management techniques and innovativeinvestment strategies in energy, shipping and agricultural commodi-ties markets, and international trade. Kostas is the associate editor forthe International Journal of Financial Engineering and Risk Managementand has organised numerous international conferences. He holds aPhD in finance from Cass Business School, London, an MBA andMSc in finance from Northeastern University, Boston, and a bach-elor’s degree in production engineering and management from theTechnical University of Crete, Greece.

Francisco G. Blanch is managing director and head of globalcommodities and derivatives research at Bank of America MerrillLynch, where he is also a member of the research investment andexecutive management committees. Prior to joining Merrill Lynch,he was an energy economist at Goldman Sachs and consulted for theEuropean Commission. Francisco holds a doctorate in economicsfrom Complutense University of Madrid and a masters in publicadministration from Harvard University, where he was also ateaching fellow in financial markets.

Carlos Blanco is managing director of NQuantX LLC, and director ofrisk management at MTG Capital. He is also a faculty member at TheOxford Princeton Programme, where he heads the CertificateProgramme on Derivatives Pricing, Hedging and Risk Management.

Greyson S. Colvin is founder and managing partner of Colvin & Co,an agriculture- focused investment manager. Previously, he was aresearch analyst at Credit Suisse in the Portfolio Management Groupand at UBS Investment Research. Greyson has been featured in

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About the Authors

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numerous publications and is co- author of the Investors’ Guide toFarmland. He received a BA in financial management from theUniversity of St. Thomas and an MBA in finance and investmentbanking from the University of Wisconsin, Madison.

Rita D’Ecclesia is a professor at Sapienza University of Rome andvisiting professor at Birkbeck College, University of London. She isalso a director of the PhD programme in Economics and Finance atSapienza, as well as the director of the International Summer Schoolon Risk Measurement and Control, chair of the Euro Working Groupfor Commodities and Financial Modeling and associate editor ofseveral scientific journals. Rita teaches courses at graduate and PhDlevels on quantitative models, finance and asset pricing. Rita'sresearch activity focuses on optimisation techniques and modellingfinancial and energy commodity markets. She is active within theResearch Centre for Energy Management at ESCP Europe.

Jay Gottlieb led development of the first coal derivatives instru-ment, the NYMEX CAPP coal futures contract, while a director in theExchange's Research Department. Jay was also instrumental in thelaunch of instruments and over the counter clearing for the electricityand emissions markets, and exchange traded funds for gold and oilmarkets. He has served as a member of the board of directors of theNew York State Energy Research and Development Authority andthe Coal Trade Association. He holds an MBA from Stanford, a BSfrom Huxley College of the Environment, and a BA from St. John'sCollege, Annapolis.

Todd Gross is chief investment officer, managing member andfounder of QERI LLC, a New York commodity trading firm whichinvests client assets in liquid, fundamentally-based strategies.Throughout a 25-year career Todd has been dedicated to under-standing the nuances and inefficiencies of the commodity space withparticular emphasis in Energy. He began his career at Cooper Neff &Associates, moved on to manage derivatives in Morgan Stanley'sGlobal Commodity Group, and founded and ran Hudson CapitalGroup LLC, before launching QERI LLC in 2012. Todd received a BSin economics from Wharton and a bachelor of applied science insystems engineering from the Moore School of Engineering.


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Michael Haigh is managing director and global head of commodi-ties research for Société Générale, based in New York City, managinga team of commodity analysts in Singapore, Paris, London and NewYork City. Prior to joining Société Générale, he was global head ofcommodities research at Standard Chartered Bank in Singapore.Michael has also held the position of managing director at K2Advisors, and spent several years as the associate chief economist atthe US Commodity Futures Trading Commission and as a tenuredassociate professor of economics at the University of Maryland. Heholds a PhD in economics with a minor in statistics from NorthCarolina State University.

Mark Hooker was most recently senior managing director of StateStreet Global Advisors and head of its Advanced Research Center,where he was responsible for the worldwide development andenhancement of SSgA’s quantitative investment models. Prior tojoining SSgA in 2000, Mark was a financial economist with theFederal Reserve Board in Washington, and before that an assistantprofessor of economics at Dartmouth College. He earned a PhD ineconomics from Stanford University and a bachelor’s degree with adual concentration in economics and mathematics from theUniversity of California at Santa Barbara.

Paul D. Kaplan is director of research for Morningstar Canada and asenior member of Morningstar’s global research team, as well as aqualified CFA. He is responsible for many of the quantitativemethodologies behind Morningstar’s fund analysis, indexes, advisortools and other services. Paul’s research has appeared in manyprofessional publications, including his book, Frontiers of ModernAsset Allocation. He received his bachelor’s degree from New YorkUniversity and his masters and doctorate in economics fromNorthwestern University.

Paul R. Lucek is the chief investment officer, Hedge Fund Group,and a member of the Hedge Fund Investment Committee at SSARISAdvisors. Prior to joining SSARIS, he developed quantitative algo-rithms for trading stock index futures, and in 1996 he co- foundedSITE Capital Management. He made the transition to moneymanagement from the MD/PhD programme at Columbia

ABOUT THE AUTHORS

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University, College of Physicians and Surgeons, where as aresearcher he pioneered the use of neural networks in the analysis ofcomplex genetic inheritance in humans. Paul earned his bachelor’sand master’s degrees in biology from Harvard University, and amaster’s degree in genetics from Columbia University.

Jose Marquez is a meteorologist for Whiteside Energy. Since 2000,his meteorology experience has been focused on the energy industry,where he has held positions as senior meteorologist at Total Gas &Power, Citigroup, Citadel Investment Group and Enron NorthAmerica. After graduating from the Navy’s Meteorological andOceanographic training school, he served in the US Navy, and hewas also director of meteorology for the Latin America WeatherChannel. He has a BS in environmental sciences from the Universityof Puerto Rico and an MS in atmospheric sciences from the GeorgiaInstitute of Technology.

Kamal Naqvi is a managing director, global head of metals and headof commodity sales across Europe, the Middle East and Africa in theinvestment banking division of Credit Suisse, based in London. Hehas been working in the resources industry since the early 1990s,having also worked in commodity sales and commodity researchpositions at Barclays Capital, Macquarie Bank and the TasmanianState Government. Kamal holds degrees in law and in economics(hons) from the University of Tasmania.

Patrick E. O'Hern is the managing partner and co-founder of SugarCreek Investment Management, an actively managed commoditytrading and alternative investments advisor in Chicago. Patrick isalso head of portfolio management for the Meech Lake InvestmentGroup, a commodity trading asset manager. Previously, Patrickheld the position of senior analyst in the funds group at FourWindsCapital Management in Boston. Prior to joining FourWinds Patrickspent his early career in trading and brokerage on the floor of theChicago Mercantile Exchange, where he traded in the livestock anddairy pits. Patrick has a bachelor's degree in agriculture businessfrom Western Illinois University.


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Michael Pierce is co-founder and director of Financial Engineeringat NQuantX LLC a financial engineering firm which develops soft-ware for portfolio valuation and risk management. He also workedwith Platts as the lead financial engineer and analytics softwaredeveloper. He is a former senior financial engineer at FinancialEngineering Associates (a MSCI/Barra company), where he wasresponsible for front-line development of numerous software prod-ucts over an eight-year period. Michael has a master's degree inmathematics from the University of California at Berkeley.

T. Marc Schober is a director at Colvin & Co and managing editor of“Farmland Forecast”. He has been featured in numerous publica-tions and is co- author of the Investors’ Guide to Farmland. Growing upon a Wisconsin farm, the Schober family has owned and managedfarmland in Wisconsin for over 40 years. He received a BS in businessmanagement from the University of Wisconsin, Eau Claire, and isalso involved in a number of cancer fundraisers, including theOconomowoc LakeWalk.

Gustavo Soares is part of the Commodity Investor Products Groupat Macquarie Bank, where he is responsible for designing investablestrategies and indexes in commodities. He joined Macquarie in 2012,having spent several years at Bank of America Merrill Lynchworking as a commodity strategist. Gustavo holds a BA/MA ineconomics from Universidade de São Paulo, Brazil and a PhD ineconomics from Yale University.

David G. Stack is managing director of Agrimax, a commoditymarket consulting firm. He has worked in the commodities industrysince the late 1980s on all aspects of the energy and agriculturalmarkets. David is experienced in all parts of the physical and finan-cial space, and specialises in derivatives, with clients ranging fromthe smallest producer to the largest consumer, including hedgefunds and NGOs. Having previously worked at Barclays, LouisDreyfus, Bunge, Enron and BP, he is also MD of the CommodityTrading Room at ESCP Europe, and develops trading and riskmanagement software with riskGRID.

INTRODUCTION

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William Webster is head of EU power market design for RWESupply and Trading. He began his career in the UK GovernmentEconomic Service, ending with a period at UK water regulatorOfwat, where he was a team leader. William joined the EuropeanCommission in 2000, working in both DG Energy and Competition,and introduced competition into electricity and gas markets. In 2007,he joined RWE and ran two major strategy projects for RWE powerbefore starting his current role in 2010. William read economics atCambridge University, has an MA from the College of Europe and isa member of the Chartered Institute for Securities and Investment.

Wang Xueqin is a senior specialist of the Zhengzhou CommodityExchange, where his major research areas are market development,new products and commodity options. He previously worked forthe International Department of China Securities RegulatoryCommission, as well as the working taskforce for China’s prepara-tions for launching CIS 300 at CFFEX. Wang was the first fromChina’s futures industry to research options as a visiting scholar atCBOE and IIT, and he has worked for Zhengzhou Grain WholesaleMarket, the precursor of the China Zhengzhou CommodityExchange.


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Strong gains in commodity prices since the early 2000s created agrowing interest in the asset class. The financial industry respondedwith many products, including new hedge funds, index funds, commodity- linked fixed income products and exchange- tradedfunds (ETFs). With oil and natural gas making a prominent peak in2008 and gold hitting a peak in 2011, many took this as a sign that thecommodity bull had run its course and expected that we wouldreturn to the normal long- standing trend of commodity price defla-tion.The deflationist camp notes that growth in China must slow

down, possibly to a dramatic degree, if imbalances in that economyare not handled carefully. Europe and the US continue their struggleto reignite sorely needed jobs growth in order to relieve high youthunemployment, while at the same time facing demographics thatlead to a shrinking labour force.However the world’s economic situation is resolved, commodities

and commodity flows will remain critical to the functioning of cities,states and economies. For this reason, a basic knowledge of thesupply and demand issues relevant to each commodity sectorprovides financial insights even beyond the commodity markets.This book therefore discusses the fundamentals of many of the majortraded commodities offering both an introduction and a referencefor all those interested in understanding and analysing thesemarkets.This book is divided into three sections. The first covers the funda-

mentals of the most important markets in energy, metals and

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IntroductionStinson Gibner

Whiteside Energy

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agriculture. Michael S. Haigh starts us off with an investigation intothe importance of non- fundamental information. He uses principlecomponent analysis to discover how commodity market behaviourhas changed over the years, and shows evidence that commoditymarket participants have adjusted their behaviour since the financialcrisis of 2007–08.Within the energy complex, crude oil, European power, North

American power, natural gas, liquefied natural gas (LNG), and coalare covered in separate chapters. Chapter 2 by Stinson Gibnerprovides an introduction to the fundamentals of the North Americanmarket for natural gas. Natural and economic forces impactingsupply are illustrated along with the annual rollercoaster of demand.The critical role of storage in balancing short- term and seasonalswings is explained, and key issues for the supply–demand balanceare discussed. Also within this chapter, Rita D'Ecclasia gives anoverview of the expanding global LNG trade.Relevant to all commodities, Jose Marquez discusses weather and

climate from the unique perspective of a working commodities mete-orologist. His chapter walks through the daily analysis andinformation flow that must be monitored to keep abreast of weatherimpacts on commodity demand and supply, while a panel discussesclimatology and its longer- term indicators of weather trends.In Chapter 4, Todd Gross tells the incredible story of how oil prices

climbed from US$17/bbl in 2002 to an amazing US$147/bbl a meresix years later. Todd also examines, as he puts it, “why the globealways seems to be running out of oil, and yet, so far, that fate has yetto be realised.” Unafraid of digging into the details, the analysis ofglobal refining capacity gives a great insight into the changingdemand for – and flows of – various types of crude. The impact oftransport bottlenecks within North America is also discussed.William Webster then explains the unique challenges of operating

a market for power, and explores the solution adopted by theEuropean power market. He explains the instruments traded andprice formation, before offering a historical perspective of pricing forthese markets. He concludes with thoughts about possible futuremarket trends and regulatory changes.Kamal Naqvi takes us on a whirlwind tour through the precious,

base and industrial metals in Chapter 6, in which he discusses thekey drivers for metals and offers insights as to which may outper-


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form going forward. In Chapter 7, David Stack provides a tour deforce discussion of the global grains markets, giving an overview ofthe markets for food grains, feed grains and oilseeds. Farming area,yield and production trends are discussed for major producers. Thechapter also reviews past import and export flows, as well as likelyfuture trends for global grain flows and crop rotation flexibility. Adiscussion of the coal markets in Chapter 8 completes the energycommodities. Jay Gottlieb lays out the fundamentals of the coalmarkets and discusses which trends are likely to dominate goingforward.Rounding out agricultural investments, Greyson Colvin and Marc

Schober open the second section of the book by explaining the basicsof agricultural land in Chapter 9, arguing that the fundamentalsbehind the rush to invest in farmland are likely to persist far into thefuture. Complementing the grains discussion, Chapter 10 by PatrickO’Hern explores the agricultural trading and hedging markets andgives an overview of the types of participants active. He providesseveral examples of trading strategies to illustrate intra- market and cross- market trade opportunities within the agriculture markets,and illustrates the diversification that may be provided acrosscommodities.The remainder of the section on trading and investing strategies

focuses on alpha strategies and index investing. In Chapter 11, MarkHooker and Paul Lucek present an interesting case study of whatthey call convergent and divergent strategies, concluding that usefulrisk diversification can be achieved through intelligent choice ofstrategies within a commodity portfolio. An overview of alphastrategies that could be used by either traders or index funds is givenby Francisco Blanch and Gustavo Soares in Chapter 12, which coversmomentum, roll yield and volatility methods. The accompanyingpanel by Paul Kaplan gives a short case study of active index fundsapplying these alpha concepts.In Chapter 13, Kostas Andriosopoulos finishes out the commodity

index investing discussion by bridging the gap between commodi-ties and equities. It presents his proposal to track a spot commodityindex by using a carefully selected portfolio of equities, and showshis tested selection methods and tracking results.The third section of the book opens with two chapters by Carlos

Blanco. In Chapter 15, Blanco and Michael Pierce present the choices

INTRODUCTION

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for performing a proper analysis of credit risks embedded in yourbilateral trade portfolio. The resulting credit value adjustment (CVA)provides a measure of expected future loss due to credit events.Taking a broader view of risk, Chapter 14, also written by CarlosBlanco, explains the structure for putting in place a system of enter-prise risk management and some possible pitfalls. In principle,everyone wants to have proper risk systems and structures in place;however, operational weakness is difficult to avoid as daily choicesmust be made between risk levels and the potential profitability ofthe enterprise. Carlos explains the challenges of the risk managerand offers advice about properly structuring a risk managementfunction.Wang Xueqin then reviews the rapid growth of commodities

trading in China in Chapter 16, and shows that although still largelyrestricted to domestic participants, the size of China’s commodityfutures markets now rivals commodity markets globally.


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Part I

Commodity MarketFundamentals

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Commodity markets can (and will) occasionally co- move withbroader macro markets for reasons beyond the physical fundamen-tals. The purpose of this chapter is to illustrate how differentcommodities are affected by non- fundamental factors (macro shocks,liquidity events, currency moves and broader market sentimentswings) that are normally considered exogenous to commodityfundamentals (eg,mine or oil supply). At certain points in time, espe-cially since the Lehman bankruptcy in September 2008, the non- fundamental influences on certain commodities have dwarfedthe impact of actual commodity fundamentals. Accordingly, under-standing this has brought obvious benefits for analysing howcommodity market price moves can be applied to trading strategies.The chapterwill examine this by focusing on energy (oil), basemetals(copper) andpreciousmetals (gold), and agriculture (soybeans).Until the late 1990s, commodity markets generally enjoyed excess

capacity as innovations and new discoveries resulted in greatersupply (think of how the US natural gas markets have evolved). Anysupply side shock that was persistent would result in commodityprice increases, higher inflation and a consequent decline in equitymarkets: hence the negative relationship with commodity pricemovements. However, by 2000, increased demand growth andunderinvestment in the supply chain meant the excess capacity wasslowly absorbed. By 2008, the underinvestment in activity becamemore evident as the credit crisis resulted in suspensions and cancella-

3

1

The Impact of Non- fundamentalInformation on Commodity Markets

Michael S. HaighSociété Générale Corporate and Investment Bank


tions of hundreds of commodity projects. As such, changes in globaleconomicactivity,PurchasingManager’s Index (PMI) strength,dollarstrength and changes in inflation expectations (resulting fromquanti-tative easing) are playing amuch larger role in commodities.Given the increased influence of “non- fundamental” information

on commodity markets, it is worthwhile to quantify as accurately aspossible the level of this influence, across commodities and acrosstime. To thoroughly assess the role of non- fundamentals duringepisodes of quantitative easing, we employ the principal componentanalysis (PCA) technique. Simply stated, PCA is the analysis of thecovariance matrix and can be used to analyse multi- assets: basketsmade up of commodities, other financial indicators, volatilities, etc.From the historical data, the analysis determines the principalcomponents of the covariance matrix – ie, the way in which the assetprice movements correlate, by order of importance. To conduct PCAon commodity prices, we analyse price data for a variety ofcommodities against a diversified basket of 28 assets across markets,including: volatility indexes (EU and US), credit (EU and US and HYversus IG), FX (dollar, yen, euro, carry trade (G10 and EM)), bonds(spreads, 10Y GVT and inflation break- even), equities (BRIC, Euro,emerging, EU and US) and global indexes. Factors are estimatedusing the 28-member basket, which means each factor is a weightedaverage of the 28 assets with different weights for each factor.The model estimated three main explanatory factors: macro,

dollar and liquidity. What is not explained by these factors (the resid-uals) is interpreted as the commodity fundamentals. Depending onthe commodity, the relative importance of each factor varies consid-erably, as does the influence on commodities of all the factorscombined. Moreover, extreme events (eg, Lehman Brother’s bank-ruptcy) have structurally altered the influence of the macro factor (inparticular) and largely demoted the dollar factor to a secondary“outside” influence on the commodity markets.

EnergyHere we focus on Brent and note that, unsurprisingly, Brent’s funda-mentals in terms of explanatory power began to deteriorate(consistently) in 2007 when the subprime crisis became a reality (seeFigure 1.1). In the early 2000s, fundamentals prevailed with 80–90%explanatory power (eg, in March 2002, dollar and liquidity explained


4


roughly 10% each of Brent’s price movements). The Lehman bank-ruptcy changed this, with fundamentals’ explanatory powerdropping to the 30–40% range, on average. Since 2013, we have seenBrent’s fundamentals progressively giving up explanatory power tothe macro influences as inventories increase, alleviating concerns of ashortage (see Figure 1.2). The dollar’s influence has latterly beenpractically irrelevant in determining the price path for Brent.

THE IMPACT OF NON-FUNDAMENTAL INFORMATION ON COMMODITY MARKETS

5

Figure 1.1 Non-fundamentals influence on Brent experienced a structural break in 2008, jumping from 20–30% to over 80%

Source: SG Cross Asset Research

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BasePrior to the Lehman crisis, the bulk of the explanatory power relatingto base metals price movement was explained by fundamentals (forboth copper and aluminium (not shown)), followed by movementsin the dollar. The role of fundamentals diminished post- Lehmanwith more explanatory power coming from the macro factors andmuch less from the dollar (see Figure 1.3). Copper is the one basemetal that is very exposed to the macro outlook, especially as pricelevels have become significantly higher than the marginal cost ofproduction. Not surprisingly, prices can be significantly influencedby other factors. In late 2012, the role of macro dropped in itsexplanatory power (Figure 1.4).

PreciousThe gold market remains an outlier among commodities (notsurprisingly), with the influence from non- fundamentals still comingfrom the dollar, and liquidity and macro factors jostling for secondplace in terms of explanatory. Since Lehman (Figure 1.5), liquidityhas improved in terms of extra explanatory power of gold pricemovements. Since late 2012, the “outside influences” have dimin-ished (see Figure 1.6), coinciding with gold prices plummeting inearly April 2013.


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Figure 1.3 Copper – the dollar has taken a back seat to macro since Lehman


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AgricultureThe market’s fundamentals (here represented by soybeans)accounted for approximately 70–95% of price volatility prior toLehman (see Figure 1.7). The remainder of the price movement wascaptured mainly by the dollar (after the early 2000 recession).Nevertheless, soybeans could not avoid the influence of the Lehman


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Figure 1.5 Gold: the dollar is usually the greatest influence


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crisis, as the percentage explanation coming from the macro factorsincreased immediately following that event. Since late 2012, soybeanfundamentals have returned, explaining almost 100% of the pricemove (Figure 1.8).In summary, the more supply constraints, the lower the invento-

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lesser the impact of “outside” factors on commodity markets.Agriculture continues to be the most independent of the markets(alongside natural gas), relying mainly on its own fundamentals.Structurally, we have seen a shift in all markets (except gold)whereby the role of the US dollar in terms of explanatory power hasdropped dramatically, to be replaced by the role of macro factors.

THE SG SENTIMENT INDICATOR VERSUS COMMODITIESThe job of assessing commodity price movements becomes difficultwhen macro, dollar and liquidity dominate. It becomes even moredifficult when prices are pulled around by market sentiment.Fortunately, we can assess the role of sentiment employing a senti-ment indicator – a tool used to gauge an average level of riskexperienced throughout the global markets. Although the method-ology is intuitive and simple, each step must be analysed to providea clear understanding.Our sentiment indicator is built in three steps. First, suitable finan-

cial market variables, expressing a clear connection with risk, areselected. The following variables have been selected as input riskfactors: equity volatility (VIX index), FX volatility (average of G4 3Mvolatility), interest rate volatility (average of G4 1m1y and 1y5yswaptions), credit spreads (iTraxx index), swap spreads (2y, G4average) and the ratio of gold to gold equity. Second, the scoring


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Figure 1.8 The drought of 2012 brings more explanatory power from soybean fundamentals on price movement


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technique is developed. Of the six variables selected, a score isassigned depending on the current value of the variable over thetime horizon. Each day, the variables are sorted based on the last 30days of data, and are assigned a score of one if they have the highestvalue in the past 30 days (extreme “risk off”) or two if they have thesecond highest value, all the way to 30 for the lowest value. Last, asimple weight of 1/6 is assigned to each of the variables. The averageof the six scores is linearly projected in the interval 0–1, withlow/high values representing risk aversion (“off”) if the sentimentindicator falls below 0.35, risk- seeking (“on”) sentiment (above 0.7)and risk neutral (between 0.35 and 0.7). These bands can be seen inFigure 1.9 and illustrate the strong connection between the Dow Jones- UBS (DJUBS) commodity index and the sentiment indicator.In addition to the 30-day sentiment indicator, here we develop a

100-day and a 252-day sentiment indicator for a commercial applica-tion. The methodology/scoring method is identical, but the lookback period is 100 days and 252 days, not 30. The reason for alonger look- up is intuitive. Imagine a scenario where commodityprices are trending down, say, for 60 days. The 30-day sentimentindicator has to turn upwards within those 60 days because thescoring is based on the last 30 days, and so even in a declining marketthe sentiment may rise. In this sense, the 30-day sentiment indicatoris a short- term indicator, and we develop the 100-day and 252-dayindicator to assess medium- and longer- term trends. Obviously, withthe 100-day indicator the sentiment is less volatile and would enable


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Figure 1.9 SG sentiment indicator and the DJUBS (5d ma) returns: a strong link


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an investor to hold positions for longer (as the investment is based onsentiment) and incur lower trading costs from rebalancing.Overlaying with an even longer timeframe (252 days) would add afurther layer of security, ensuring that in periods of extended riskaversion one does not see a return to risk seeking prematurely,which may be signaled by a 30-day indicator. Regardless of the lookback period, the methodology is simple and its relationship tocommodity prices extremely strong. Indeed, it is difficult to find adaily indicator with such a strong short- term relationship to almostevery commodity within the DJUBS (see below).

SENTIMENT CAUSES COMMODITY PRICES AND NOT THEOTHER WAY AROUNDOf interest is the question of causality and the speed of response ofthe DJUBS to changing sentiment. To this end, we estimated a reduced- form five lag VAR (vector- auto- regression) using daily(stationary) data from early 2007 to mid-2012 (technical detailsexcluded to conserve space). Resulting causality tests confirm at veryhigh levels of confidence (5%) that sentiment “causes” DJUBS pricemovements, and not the other way around. Here we can take thecausality analysis one step further with the assistance of impulseresponse functions. We shock our VAR model by one standard devi-ation (down) and trace out the influences of sentiment on the DJUBSprice path, and vice versa. Focusing on Figure 1.10, we see that a one


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Figure 1.10 Impulse response: a one standard deviation drop in sentiment drags down the DJUBS to its lowest level after five days


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standard deviation decline in sentiment results in a negative pricepath for DJUBS – ie, it also declines. What is interesting, however, isthe speed of that response and the time it takes for DJUBS to fullyincorporate the negative sentiment.The first day after the shock (day 1) DJUBS prices react, but by day

five, DJUBS has declined by the same amount, in percentage terms,as the negative sentiment. Beyond day five, DJUBS returns to its pre- shock level. The equivalent decline in DJUBS prices (one standarddeviation) does not have a significant influence on sentiment (butraises it modestly) – see Figure 1.11.

FOR ALMOST EVERY COMMODITY, 2008 RESULTED IN ASTRUCTURAL SHIFT IN ITS RELATIONSHIP WITH SENTIMENTMeasuring the relationship between variables at various points intime, rather than using a single correlation coefficient over the entiresample period, provides information on the evolution of the relation-shipdynamically. For this purpose, simple correlationmeasures suchas rolling historical correlations and exponential smoothing arewidely used. The rolling historical correlation estimator providesequal weights to newer and older observations, and raises issuessurrounding window- length determination. The exponential- smoothing estimator requires the user to adopt an ad hoc approach tochoosing the smoothingparameter. Thedynamic conditional correla-


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Figure 1.11 But a one standard deviation drop in the DJUBS does not influence sentiment


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tion (DCC)methodology developed by Engle (2002) helps to remedyboth of these issues.1 In the first step, time- varying variances are esti-mated using a general autoregressive conditional heteroscedasticity(Garch)model. In the secondstep, a time- varyingcorrelationmatrix isestimated using the standardised residuals from the first- stage esti-mation. Here, we use the DCCmethod because it has been shown tooutperform other widely used correlation structures in helping withportfolio investing decisions.2 To assess the relationship betweencommodities and sentiment, we correlate the rolling nearby futurescontract prices (using log returns) for each component of the DJUBSwith the 30-day sentiment indicator with daily data beginning inSeptember2006. Importantly, the results arequalitativelyverysimilarwhen we correlate the DJUBS component prices with the 100-dayindicator (results excluded to conserve space).Figures 1.12 and 1.13 plot the time- varying correlation of the log

returns of aluminium (LHS) and copper prices (RHS). September 15,2008 (Lehman bankruptcy) was a game changer – there is a notice-able shift in the relationship between the base metals markets andsentiment. For aluminium, the average correlation tripled (from 0.13to 0.38) with the maximum correlation post- Lehman reaching 0.59.The minimum correlation post- Lehman was 0.15, still higher thanthe average pre Lehman. In the case of copper, the correlationincreases from an average of 0.11 to 0.42, almost four times higherpost-2008. Interestingly, the volatility of the correlation of copper


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Figure 1.12 DCC between aluminium prices and sentiment


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and sentiment is half that of aluminium, post- Lehman. Turning nowto the energy markets, here represented by Brent and heating oil, theresults also illustrate a structural break. Pre- Lehman, the Brent corre-lation was a mere 0.08, post- Lehman it was 0.39 (see Figure 1.14). Forheating oil (Figure 1.15), we see the correlation rise from an insignifi-cant 0.04 to 0.38.At first glance, the results of gold and sentimentmay appear coun-

terintuitive, as their average correlation pre- Lehman was 0.06 (seeFigure 1.16). While low, their post- Lehman correlation (relative to


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Figure 1.13 DCC between copper prices and sentiment


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other markets) is not much higher at 0.18, on average. Interestingly,for both gold and silver, the Lehman event did increase the correla-tion, but itwasnot a structural change, in theway itwas for the energyand base metals markets. However, gold is a unique commodity,driven as much by sentiment, macro and the dollar as by its ownfundamentals (eg, central bank involvement, exchange- traded fund


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Figure 1.15 DCC between heating oil and sentiment


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Figure 1.16 DCC between gold and sentiment


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(ETF) volumes, jewellery and coin demand, mining and scrapsupply), soadecrease (increase) in sentimentmayresult inan increase(decrease) in golddemand, hence dragging their correlation lower.Gold is often negatively related in periods of extreme crisis, hence

fulfilling its role as a flight to safety. Post- Lehman, and after theAugust 2011 euro crisis, there has been a negative correlation withsentiment. This is less evident in silver (Figure 1.17), the more indus-trial of the two precious metals. Its correlation rose from an averageof 0.16 pre- Lehman to 0.30 post- Lehman.Not surprisingly, the role of sentiment is not as important to the

agricultural markets despite their reacting to the Lehman crisis in thesame way as the base metals and energy markets (albeit at a muchlower level). The scale of the axis hides the subtle nature of thechange: it was very low in the case of corn (Figure 1.18). From a pre- Lehman correlation of 0.13, we only see a rise to 0.16. Hardlysignificant, for coffee we see a rise from 0.10 to 0.22, a doubling of thecorrelation (Figure 1.19).Not shown (to conserve space) is the change in the relationship for

wheat. The correlation before Lehman was actually negative, onaverage; post- Lehman, it averages 0.19. Therefore, agriculture –which was less influenced, certainly in the short run, by the eurocrisis, or by a slowdown in Chinese demand which would influencethe more cyclical commodities – as with base metals and energy, isnot going to be as affected by things outside of its own fundamentals.As we have illustrated, agriculture markets are still positively related


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Figure 1.17 DCC between silver and sentiment


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Figure 1.18 DCC between corn and sentiment


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to sentiment and are still part of global benchmark indexes, but senti-ment’s influence on them is certainly lower.Last, we present a couple of examples of markets that did not

change after Lehman. US natural gas (a domestic rather than globalmarket) is the distinct outlier in that its correlation pattern did notchange at all with the structural change in 2008 (see Figure 1.20). Itsaverage correlation remained at 0.06, precisely the same value it hadbefore the crisis in 2008. Lean hogs is also independent of sentiment,having a very similar correlation value pre- and post- Lehman (0.06and 0.05). Its correlation can occasionally go negative (Figure 1.21).


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Figure 1.20 DCC between US natural gas and sentiment


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DO SOME COMMODITIES REACT MORE TO SENTIMENT IN “RISK- OFF” VERSUS “RISK- ON” ENVIRONMENTS?We conclude our analysis of the relationship between commoditiesand sentiment by digging deeper into the relationships duringperiods of “risk off”, “risk neutral” and “risk on” before the Lehmancrisis (see Table 1.1, left- hand side) and post- Lehman (right- handside). First, we present the rankings from the 30-day sentiment indi-cator in Table 1.1. Prior to the Lehman bankruptcy (left- hand side),the top 10 commodities most correlated with sentiment in “risk off”


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Sugar 15 17 12 Corn 15 16 16

Natural gas 16 13 14 Gold 16 15 15

Heating oil 17 15 17 Live cattle 17 17 17

Lean hogs 18 11 13 Sugar 18 18 18

Live cattle 19 20 20 Natural gas 19 19 19

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Table 1.1 Ranking of correlations (20 = least, 1 = most) between components ofthe DJUBS and 30-day sentiment (pre- Lehman, January 2006–September 2008, post- Lehman, September 2008–September 2012)


were represented by all commodity types: base metals, preciousmetals, agriculture and energy. In fact, energy only just makes thetop 10, with West Texas Intermediate (WTI) ranked ninth in terms ofits correlation with sentiment in “riskoff” environments.When we focus on the post- Lehman period, the patterns change

considerably. The top 10 most- correlated commodities with senti-ment are base metals and energy and silver (which one could argueis somewhat of an industrial metal). There are no more agriculturalcommodities at the top (with “risk off”) until we get to number 10:soybean oil (which moves into number nine (just) in “riskon” envi-ronments). Moreover, regardless of the environment, “risk on”, “riskneutral” or “risk off”, the rankings of commodities hardly change post- Lehman. The most “significant” change is aluminium, whichmoves from being the most correlated with sentiment in “risk off” tobeing the fourth most correlated in “risk off”: a relatively minorchange. Compare this to gold, for example, pre- Lehman. Its rankingchanges from the seventh most correlated with sentiment in “riskoff” to 18th in “riskon” environments. The bottom line is, withchanges in sentiment, base metals and energy are much more influ-enced by sentiment than other types of commodities post- Lehman.

BRINGING IT TOGETHER: A SIMPLE OVERLAY EXAMPLE TOTHE DJUBSIn this section, we will illustrate how to incorporate the main resultsfrom our research into a simple product for investors wishing tobenchmark against the basic DJUBS (excess return) long- only expo-sure. There are obviously numerous applications, but for clarity andsimplicity we focus on a simple overlay. We simply try to incorpo-rate a medium- run sentiment indicator (100-day) to help with re- weighting overlaid with a longer- term indicator (252-day) toprovide a further layer of insurance in periods where prices fall for along period of time. Critically, this is just an example and many otherapplications can be made. Here is the procedure.

First, we develop two sentiment indicators based on the princi-�

ples outlined in the previous section. One is the sentimentindicator based on the 100-day look- up period to signal re- weighting decisions (to reduce trading costs that occurs to shorter- run indicators). The second sentiment indicator has a


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252-day lookback period. The 252-day sentiment indicator isgoing to behave differently than the 100-day indicator as it incor-porates a much longer- term risk appetite. Therefore, the 252–dayindicator provides insurance for periods of extendeddeclines/risk aversion that even the 100-day indicator might notpick up on.Second, using the 100-day indicator, we focus on weight tilting�

among the components of the DJUBS. We have already identi-fied that base metals, silver, energy (excluding natural gas) – the“sensitive commodities” (of which we count nine) – have thehighest correlation to sentiment in both “riskoff” and “riskon”environments. Each day, depending on the sentiment indicatorvalue, the risk regime is established: “risk off” is where senti-ment is lower than 35%, “risk neutral” between 35% and 70%and “risk on” is greater than 70%. Imagine we start in the “riskneutral” environment: the index position is invested fully intothe DJUBS. The actual implementation occurs on the close of thefollowing day after sentiment is calculated (to fully account forthe commercial realism of this application). Then, on a day thatsentiment moves into, say, a “riskoff” environment, we re- weight the DJUBS out of the “sensitive commodities” and intothe remaining components commodities. We preserve the rela-tive weight ratios of the commodities in the DJUBS but distributethe “sensitive commodities” among the other commoditiesaccordingly to make the “sensitive commodities” weights equalto zero. Similarly, in “riskon” environments, we reduce theweights of the less- sensitive commodities to zero and re- weightinto the “sensitive commodities”, maintaining their relativeratios.Last, to provide a risk filter that accounts for a longer- term view�

of sentiment, the steps above are overlaid with a risk switch thatinvests in the weight- tilted index only if the 252-day sentiment isabove the threshold of 20%. If the indicator falls below 20%(extreme risk aversion), the DJUBS weights are set to zero (again,this is just one concept – investing in other products (alpha), forexample, could be an option). Moreover, this is an arbitrarythreshold that could vary depending on an investor’s risk toler-ance. However, the notion of achieving this is that, if there is longterm decline in commodity prices due to a crisis (for


21


example), the investor sits on the sidelines until longterm senti-ment improves (ie, passes through 20% to the upside), at whichpoint the risk- tilting mechanism kicks in once more.

Importantly, while we have isolated the commodities most affectedby sentiment as the ones to remove in “risk off” versus capture in“risk on”, even the less- sensitive commodities are positively corre-lated with sentiment. Therefore, one could argue to completelyremove all commodity exposure in “risk off”, but here we choose toremain invested instead of having extended periods of time sittingon the sidelines. Performance is clearly better overlaying the senti-ment (see Figure 1.22), a function of re- weighting and overlayingwith an extra layer of insurance (the 252-day window). Focusingpurely on the September 2008 onwards (47 months), the number ofpositive months increases from 27 to 32 and average annualisedreturn rises from –13.34% investing in the DJUBS (about –3.52%annualised) to 74.08% (about 15% average annualised) investing inthe DJUBS, weight- tilted 100-day sentiment indicator with the 252-day overlay. Ignoring the 252-day overlay (from sentiment) results ina return of 32%. Therefore, most reward from using the sentimentindicator comes from the performance attributed from shiftingweights based on the 100-day indicator (about 45% over the DJUBS),although the overlay (insurance) adds almost the same amount. Thenumber of times the portfolio is reweighted because of a change insentiment is approximately 25 times per annum. With the 30-daysentiment indicator applied (instead of the 100-day), the number oftimes is 46 – hence higher trading costs.

SUMMARYIt is clear that outside influences on commodities have picked upsince 2008. The role of macro, dollar and liquidity vary acrosscommodities and across time. Sentiment has made a substantialimpact on the commodities markets since 2008. Here, we have docu-mented the causal relationship (from sentiment to commodities) andreported that some commodities are more affected by sentiment thanothers. A ranking was established. We applied our research resultsby overlaying the DJUBS with the sentiment indicator signals, util-ising the rankings of the sensitive commodities by re- weighting in “riskoff” and “riskon” environments. The re- weighting alone


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Figure 1.22 DJUBS versus DJUBS-with-weight-tilt (based on 100-day sentiment) and 252-day sentiment indicator overlay


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significantly outperforms the long- only DJUBS exposure since 2008 –we achieve an extra 45% higher return over the period. However,overlaying with an extra layer of protection (a signal from a 252-daysentiment indicator) significantly protects returns from largedeclines in commodity prices – this adds an additional 42% on top ofthe 45%. Total returns using weight tilts and 252-day overlay equals74.08% since 2008, compared to –13.34% by investing the DJUBS.The purpose of this chapter is not to suggest fundamentals do not

matter – they do, but what is clear is that an analysis of commoditymarkets requires something more than counting barrels or bushels.Even basic applications of sentiment onto commodity markets addoutperformance and significant protection.

1 Engle, R., 2002, “Dynamic Conditional Correlation – A Simple Class of MultivariateGARCH Models”, Journal of Business and Economic Statistics, 20(3), pp 339–50.

2 Huang, J. Z and Z. Zhong, 2010, “Time Variation in Diversification Benefits of Commodity,REITs, and TIPS”, working paper, Department of Finance, Pennsylvania State University.


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This chapter will provide an overview of the most important supplyand demand developments for natural gas, beginning with a briefdiscussion on natural gas and how it is traded. The analysis of gasdemand fundamentals and gas production leads to an under-standing of the dynamics of the storage market for natural gas. Thegeographic distribution of sources and demand for gas will also beexamined, before we move on to price dynamics, aided by examplesof how many of these factors influence market prices for gas. Inconclusion, key factors that will determine the future evolution ofprices are identified.

OVERVIEWWhat makes the North American natural gas market unique? Themost important factor is that it is a self- contained system within theconfines of North America, apart from limited liquefied natural gas(LNG) import and export capability. Consequently, the market canby analysed by understanding supply, demand and storage stockswithin the US and Canada. LNG imports can be relevant, but havingbeen at less than 2% of the annual supply for many years, they havelittle market influence.Highly seasonal demand driven by winter heating and a lesser

peak from summer cooling loads combines with relatively constantproduction flows to require massive storage facilities that can injectgas during times when supply outpaces demand and withdraw

25

2

The North American NaturalGas MarketStinson Gibner

Whiteside Energy


when gas burn rises. Injections and withdrawals from storage facili-ties are surveyed and reported by the Energy InformationAdministration (EIA), part of the US Department of Energy (DOE),providing a closely watched weekly monitor of supply/demandbalance.The natural gas transported through long- haul pipelines is

primarily methane with a mixture of some ethane and smalleramounts of heavier hydrocarbon gases, and may contain a smallpercentage mixture of nitrogen and carbon dioxide. The averageheating value of gas consumed in the US is now about 1,025 Btu percubic foot or 1.025 million Btu (MMBtu) per thousand cubic feet(Mcf). This leads to an often- used rule of thumb conversion factorthat 1 Mcf approximately equals 1 MMBtu. Pipelines have specifica-tions for the range of gas quality acceptable for receipt. The heatingvalue of the gas accepted must typically lie within a range of, forexample, ~970–1,100 Btu per cubic foot. Some of the most commonnatural gas units of measure and conversions are given in Table 2.1.

GAS MARKETSBefore the 1990s, natural gas purchases and sales were predomi-nantly handled by longterm contracts for physical natural gas.Natural gas can still be traded by the purchase or sale of physical gaswhere the seller delivers and the buyer receives the molecules, andthere is also a liquid market where gas can be traded purely finan-


26

Table 2.1 Common units of measure and conversions

Common units of measure

MMBtu Million BtuMcf Thousand cubic feetBcf Billion cubic feet (1,000 Mcf)Tcf Trillion cubic feet (1,000 Bcf)Bcm Billion cubic metersMMT Million tonnesMMBOE Million barrels of oil equivalent

Conversions

1 Bcm = 35.3 Bcf1 MMT of LNG = 48.7 Bcf methane1 MMT of LNG = 1.38 Bcm1 MMBOE ~ 5.6 Bcf (conversion varies)


cially. Most financial market instruments derive from the tradedstructures in the physical gas market.The physical gas market traditionally trades gas for both monthly

and next day delivery. Purchases of monthly gas are for gas to bedelivered in approximately equal daily quantities over an entirecalendar month. The majority of these physical gas purchases andsales are made during “bid week”, the last week of each month. Gasalso trades in the daily market, with purchases and sales of gas typi-cally occurring during the morning hours prior to the gas flow datein order to allow time for proper nominations for gas flows on therequired delivery pipelines. Gas for the weekend and Monday aretraded on the preceding Friday.The Nymex natural gas futures contract was introduced in 1990,

and it rapidly grew in traded volumes. The contract can be physi-cally settled at the Henry Hub in southern Louisiana, which allowsfor the interchange of gas between 13 pipelines, or at an alternatedelivery point based on mutual agreement between the buyer andseller. Monthly futures contracts are listed, each contract unit repre-senting 10,000 MMBtu, with the contract price quoted in US$/MMBtu and having a tick size of US$0.001 (0.1 cent) per MMBtu.Although many months of futures are listed, liquidity concentratesat the front of the futures curve. In addition to trading on the ChicagoMercantile Exchange (CME), Henry Hub futures are listed on theIntercontinentalExchange (ICE).Of course, gas trades, both physical and financial, for many

delivery locations throughout North America other than HenryHub. In order to facilitate these transactions, a large number of gasprice indexes have been created. The primary publishers of theseindexes are Platts and Natural Gas Intelligence. Each of thesepublishers conduct daily and monthly polls of market participants inorder to estimate a representative market price transacted for naturalgas at a variety of geographical delivery areas. The published gasindexes allow for managing a financial exposure to the gas indexprice without transacting any physical natural gas. For example, Julygas at Chicago Citygate may be trading in the forward market forUS$5.00/MMBtu, and a financial buyer may enter a contract to payUS$5.00 and receive the Chicago Citigate index price after it ispublished at the beginning of July.

THE NORTH AMERICAN NATURAL GAS MARKET

27


DEMAND SIDE DYNAMICS FOR NATURAL GASMost consumption of natural gas falls into four of the categories usedby the EIA: residential, commercial, industrial and electric genera-tion. Residential and commercial use is primarily for heating, andboth sectors are characterised by a strong winter demand peak andvery flat demand in the summer. Industrial use has much lessseasonality, but about 10% does go toward heating demand inwinter. Electric generation burn peaks in the summer, when airconditioning loads are the highest. In 2011, residential plus commer-cial users consumed 32%, power generation 31% and industrial users28% of all gas consumed in the US.

Industrial useIndustrials use gas for space heating, process heat and also as a feed-stock. As can be seen in Figure 2.1, industrial demand in the USdecreased dramatically from 1997, dropping by a total of almost 5.5Bcf/day before bottoming in 2006. Since then, industrial use hasrallied by more than a Bcf per day, interrupted by the GreatRecession year of 2009.Figure 2.2 deconstructs industry demand by sector; we find that,


28

Figure 2.1 Average annual industrial gas use (Bcf/day)

Source: EIA

US$0.00

US$4.00

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Henry hub annual avg. spot price (right axis)

Indu

stri

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eman

d (B

cf/d

ay)


from 1998 to 2006, there was declining use in every significant sectorexcept food and non- metallic minerals. The largest losses in usewerein chemicals manufacturing (down 2.59 Bcf/day), primary metals(down 0.82 Bcf/day) and refining (down 0.43 Bcf/day). Within thechemical sector, nitrogenous fertilisers alone accounted for almost0.75 Bcf/day loss of demand over this time period due to productionmovingoffshore. Imports of anhydrous ammonia grewby4.4millionshort tons, equating to 0.45 Bcf/day of domestic gas demand loss.Since 2006, industrial use of gas has begun to grow again. Of

course, the deep recession between late 2008 and early 2010 created aloss of demand of around 1.5 Bcf/day in 2009. However, growth ofindustrial demand has started to accelerate due to low natural gasprices, which looks to continue into the future, driven by a resur-gence in the chemical and refining sectors. Domestic ammonia


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Figure 2.2 Largest industrial consumers of natural gas (Bcf/day)

Source: EIA

0.0

1.0

2.0

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4.0

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6.0

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ls

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leum re

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etals

Other c

atego

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Pape

r

Nonmeta

llic m

ineral

s

Fabr

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meta

ls

Bcf

/day

1998 2002 2006 2010


production has been stepped up again, and a number of corporationshave announced plans to build new chemical plants to take advan-tage of the low energy prices in the US. There have even beenannouncements of new metal-processing plants to be built,expanded, or reopened. It appears likely that 2013 industrialconsumption will be at least 2 Bcf/day above the levels of 2006, andgrowth should continue to be robust for a number of years as newuse facilities come online.

Power generationBecause power generation is a large and growing source of demandfor natural gas, an understanding of the power markets is critical inanticipating future levels of gas demand. Increasing use of gas forpower generation has provided the largest increase of any sector.Figure 2.3 shows monthly average gas burn for power generationand the upward trend in demand since the early 2000s. Figure 2.4shows that this steady growth in gas burn for generation continuedeven through years of little or no growth in total power demand.This trend is poised to continue as the phasing in of air pollutionstandards for coal plants leads to continued coal plant retirements.Figure 2.3 shows that monthly gas burn also comprises strongseasonality of gas generation burn with the distinct summer “airconditioning” demand peak and the much smaller winter heatingdemand peak that has emerged.Most of the growth in gas burn for power since the early 2000s has

come at the expense of decreasing coal- fired generation. Figure 2.5shows the annual mix of generation sources for the 11-year periodending in 2012. During this time, the percentage of generation fromnuclear plants and from sources other than coal, gas and nuclear(which leaves hydroelectric, other renewables and liquid fuels) hasheld roughly constant, so there has been an almost one- to- one trade- off in loss of coal generation with gain in gas generation. Gasgeneration has grown from 17.9% in 2002 to 30.4% of total US gener-ation in 2012, while coal has fallen from 50.1% to 37.4% over thattime. We should note that 2012 was an exceptionally high year forgas burn due to conditions that may not recur in the near future.In fact, power generation provides one of the few demand sectors

that can significantly change the fuel mix based on short- term fuelprice levels and economics. During the period of cheap oil in the


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AMERICANNATURALGASMARKET

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Figure 2.3 Monthly average gas use for electric generation (Bcf/day)

Source: EIA

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1990s and early 2000s, fuel oil was sometimes economically competi-tive with natural gas, so during times of high gas prices there couldbe an economic incentive to turn on oil- fired generation – which, inturn, liberated gas for higher value heating use. With the advent ofoil prices near US$100+/bbl, natural gas has remained much lessexpensive and oil use for generation has fallen from the already lowlevel of 2% of total generation in 2002 to 0.3% in 2012.


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Figure 2.4 US annual power generation (million GW hours)

Source: EIA

3.70

3.75

3.80

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4.20

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Mill

ion

giga

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thou

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Figure 2.5 Percentage of annual power generation by energy source

Source: EIA

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60.0%

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Coal

Natural gas

Nuclear

All other


Latterly, coal- to- gas substitution has become a key factor to watchfor understanding demand trends for natural gas. The relative costsof generating power from coal and gas drive substitution economics.To calculate the cost of generation, we must know how much coal orgas it takes to generate a megawatt (MW) of power. The amount offuel required per unit of power generated is called the heat rate. Foractual generation plants, the heat rate will depend on a number offactors – including type of equipment, generation level (% ofmaximum capacity) and ambient air temperature. After estimatingthe heat rate, fuel cost and variable operating and maintenance cost,the marginal cost of power production can be calculated for a plant.Many analysts construct “stack models”, in which plants are

stacked in order of their production costs, then the market’s marginalcost of production can be found for a given level of net powerdemand, and the amount of expected gas burn and coal burn can becalculated. Of course, there are many additional details involved inthis process, including estimation of load served by nuclear andrenewable sources, forecast of power imports and exports toconnected regions, plant maintenance and forced outage rates, andthe influence of operational optimisations to minimise start costs. Inpractice, stack models are difficult to calibrate for accurately fore-casting future market prices, but they can be quite useful in morequalitative analysis of market trends and behaviour.Figure 2.6 tells of an interesting chapter in the natural gas demand

growth story. US power load growth accelerated in the mid-1990s atthe same time that uncertainties about market deregulation andabout future coal plant environmental regulations led to a reluctanceto build additional coal- fired generation. The market reacted bybeginning an unprecedented build of new gas- fired generation,which can be seen by the huge increases in gas capacity as new plantscame online in 2002 and 2003. The build rate slowed but hascontinued through the last decade. In addition to making more gas- fired generation capacity available, the new and more efficient plantshave lowered the average heat rate of the available gas- fired genera-tion fleet. The average heat rate of gas generation has dropped fromjust over 10 MMBtu/MWh in 2001 to 8.15 MMBtu/MWh in 2011,and the US continues to build new combined cycle gas turbines withheat rates near 7 MMBtu/MWh.The shift away from coal toward gas generation is set to continue,


33


with over 30 gigawatts (GW) of additional coal plant retirementsplanned between 2013 and 2018. In that time period, combined cyclegas generation capacity may grow by almost 60 MW if all plannedunits are permitted and built.

Residential and commercial demandWhile the residential and commercial (rescom) use of gas has notdisplayed the growth seen in the generation sector, there are substan-tial year- to- year variations in total use. The largest driver of demandvariability in rescomusearewinter temperatures,which influence theamount of gas needed for home and commercial heating during thecold months of the year. As can be seen in Figure 2.7, there has beenconsiderable variability in the weather- sensitive heating demandmonths, but no obvious trend or much change in summer demandlevels since theearly2000s.This suggests thatgrowth in thenumberofconsumers has been offset by conservation and heating efficiencygains, resulting in very little (if any) net demandgrowth.

ExportsThe US plans to begin exporting LNG from the Gulf Coast. SabinePass LNG facilities target around early 2016 for beginning LNGexports. With US gas prices likely to remain in the range ofUS$4.00–6.00 MMBtu, landed prices to Europe would likely be in therange of US$8.00–11.00 MMBtu. Export volumes are expected to


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Figure 2.6 Natural gas and coal generation capacity and gas average heat rate

Source: EIA

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Figure 2.7 Residential and commercial gas demand (monthly average in Bcf/day)

Source: EIA

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reach over 1 Bcf/day in 2016 and planned projects would growexports to over 3 Bcf/day by 2018, suggesting that the EIA’sprojected 2013–20 total production growth of about 5 Bcf/d (shownin Figure 2.11) may be low compared to the likely demand growth.

SUPPLY SIDE CONSIDERATIONSThe US meets its gas needs primarily with domestic production andimported gas from Canada. LNG imported by tanker from overseaslocations provides a third source of supply. Figure 2.8 shows histor-ical monthly production since 1993. Production grew slowly in the1990s and peaked in March of 2001. Production then began a series ofannual declines that led many to believe that domestic US gas supplymight be permanently headed in that direction. LNG imports wereseen as the solution to securing additional gas supply. In 2000, the UShad two operating LNG import facilities: Everett and Lake Charles.Two additional existing facilities, Elba Island and Cove Point, moth-balled in the early 1980s, were re- commissioned and began receivingdeliveries in 2001 and 2003, respectively. In addition, the FederalEnergy Regulatory Commission (FERC) granted authorisations forseveral additional import terminals that were completed andcommissioned in 2008–11. However, most of these new facilitieshave not yet seen heavy use due to the strong resurgence in domesticproduction that began in 2007.

Shale gasThe driver of this reversal in fortune for natural gas production wasa combination of new technologies and higher natural gas prices,which allowed shale gas to be produced economically in high quan-tities. Conventional gas production came largely from gas trapped insandstone formations with high porosity and permeability, allowingthe gas to flow through the formation to the wellbore. It had longbeen recognised that natural gas was also trapped in many shaleformations, but shale is characterised by much lower porosity andpermeability that limits the movement of the trapped gas. MitchellEnergy began to experiment with a combination of horizontaldrilling and hydraulic fracturing to produce gas from the northTexas Barnett Shale. After Devon acquired Mitchell in 2002, theBarnett drilling programme accelerated and, by 2007, the BarnettShale produced 1.1 Tcf of gas equivalents – making it the second-


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Figure 2.8 US domestic production (Bcf/day)

Source: EIA

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largest producing field in the US (see Joel Parshall, 2008, “BarnettShale Showcases Tight- Gas Development”, JPT, September). Afterthis success in the Barnett, many shale fields began to contributesignificantly to US production, and Fayetteville, Haynesville,Marcellus, Bakken and Eagle Ford all became well- known names inthe oil and gas E&P sector. Shale gas grew from less than 3% of USgas production in 2003 to more than 40% at the beginning of 2013.Figure 2.9 shows this growth in production from shale gas fields.Figure 2.10 shows that the number of drilling rigs directed

towards natural gas production more than doubled from ~700 in2003 to a peak of almost 1,600 near the beginning of the financialcrisis and recession of 2008–09. Then, in spite of the gas- directed rigcount plunging back to the 700–1,000 levels, natural gas productioncontinued to grow as shale gas production growth accelerated in2010 and 2011.The continued growth in production even with lower gas directed

rig counts can be attributed to a combination of factors, including theshift of drilling towards horizontal shale wells, improvements indrilling efficiency and growth in associated natural gas production.From September 2008 to September 2010, the number of gas- directedrigs fell from roughly 1,600 to 1,000; however, the number of hori-


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Figure 2.9 US shale gas production (Bcf/day)

Source: EIA

0

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30

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Bcf

/day

Other US shale gas

Bakken (ND)

Eagle Ford (TX)

Marcellus (PA and WV)

Haynesville (LA and TX)

Woodford (OK)

Fayetteville (AR)

Barnett (TX)

Antrim (MI, IN, and OH)


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Figure 2.10 Count of rigs drilling for oil and gas in the US

Source: EIA

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COMMODITYINVESTINGANDTRADING

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Figure 2.11 Annual US gas production by source

Source: EIA, “Annual Energy Outlook 2013”, early release

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zontal drilling rigs directed towards gas actually increased from~500 to about 650 over the same period. In other words, horizontaldrilling grew from one- third of gas rigs to almost two- thirds by late2010. The much higher average initial production rates from hori-zontal wells allowed continued production growth with lower rigcounts. At the same time, drillers were learning and improving theefficiency of their shale- drilling operations, leading to shorterdrilling time and more wells drilled by each active rig, a trend whichcontinues.The rapid deployment of oil- directed drilling rigs beginning in

July 2009 can be clearly seen in Figure 2.10. According to the EIA,natural gas associated with oil was about one Bcf/day higher in 2012than in 2010, thus adding to natural gas production growth. It shouldalso be noted that the distinction between drilling categorised as oil- directed as compared to gas- directed is somewhat imprecise.Additionally, new natural gas production lags drilling activity,

especially in the new shale production fields, because wells oftenmust wait for infrastructure to catch up with drilling – whereas oilproduction can, if necessary, be moved by truck or rail. The onlyeconomically feasible way to move natural gas production from thewellhead is by pipeline. Therefore, new fields must wait for therequisite gathering pipeline systems to be constructed to deliver gasto users and to the long- haul pipeline system. In addition, wet orsour gas production may need to wait for processing facilities thatremove liquids and impurities before the gas can be delivered to amajor pipeline.Robust production growth plus the warm winter of 2011/2012 led

to a supply surplus, driving prices down to below US$2.00 for thefirst time in years. Gas- directed rig counts plummeted to near 400rigs, the lowest level in a decade, as many shale gas fields becameuneconomic at the low price levels.In the long run (but hopefully before we are all dead), one would

expect that natural gas prices should gravitate towards a price levelthat makes marginal production economic. However, limited trans-parency of drilling costs and uncertainties in well productionprofiles and estimated ultimate recoveries (EUR) make estimatingproduction costs difficult. Also, costs and efficiencies change contin-ually, making drilling economics a moving target. In addition, theproportion of associated liquid hydrocarbons influences the overall


41


economics as the liquids sell at a premium to natural gas.Conventional wisdom recognises the Marcellus shale as the lowestcost of the gas shales, with production costs below US$3.00/MMBtuin the prime locations. With strong crude oil prices, gas- drillingreturns may have to compete with oil- drilling economics whenexploration and production budgets are decided.

Weather impacts on supplyCertain types of weather events can influence production as well asdemand. The clearly noticeable production drops in August andSeptember 2005 and September 2008 were caused by hurricanes inthe Gulf of Mexico, where there is substantial offshore gas produc-tion. Hurricanes Katrina and Rita were both Category 5 storms asthey crossed the production area in 2005, and hurricanes Gustav andIke were both Category 4 storms. Smaller hurricanes, and even trop-ical storms, may cause some disruption to supply as personnel areevacuated from the storm path and some production platforms maybe shut- in as a precautionary measure. Rita and Katrina shut- inalmost 520 Bcf of production, and the 2008 storms caused a loss ofabout 340 Bcf of production. Offshore gas production has been indecline but remains above 4 Bcf per day. Because hurricanes needvery warm water temperatures to power them, the Gulf hurricaneseason runs June–November, with August, September and Octoberbeing the most active months.The production decline seen for February 2011 in Figure 2.11

resulted from very cold temperatures in Texas and nearby states. Gasproduction declined from wells freezing off and from conditions thathampered the ability of pumpers to maintain production. Severelycold temperatures happen rarely enough in these production areasthat many wells do not have protection against cold temperatures,allowing water vapour in the natural gas stream to freeze andconstrict flow from the wells. Thus, when unusually cold weatherinvades southern and southwestern production areas, freeze- offs area danger to production.

Ethane rejectionNGLs, which are comprised of ethane, propane, butane and heavierhydrocarbons, enhance production value when stripped from thenatural gas stream and sold separately. The stripping of wet gas,


42


carried out by fractionation facilities, may also be necessary to bringthe liquid content of the gas down to standards required bypipelines. For example, 1.25 MMBtu/Mcf gas may yield around 0.12bbl of liquids per Mcf. At an average liquids price of US$25.00/bbl,the liquids alone are worth US$3.00/Mcf and may comprise nearlyhalf of the value of production.The lowest value liquid in this stream, ethane, may fall below the

value received by leaving it in the delivered gas stream. In thesecases, the ethane can be rejected during the fractionation process andeffectively increases the net amount of delivered natural gas. That is,when we say that ethane is rejected, we mean that it is left in the gasstream with the methane. Ideally, economics will dictate the ethanerejection decision; however, with the rapid growth of new gasproduction in some regions, the infrastructure is sometimes notsufficient to process all of the produced gas. The total amount ofethane being extracted from the US gas stream had a heating equiva-lent value of about 3 Bcf/day of gas in late 2012, and the historicallevels of ethane extraction suggest that varying ethane rejectioncould impact net gas deliverability by up to 0.5–1 Bcf/day.

STORAGEThere is a mismatch between highly seasonal demand as comparedto production which, in the absence of disruptions, trends moreslowly over the years. The large seasonal variability of demandrequires gas to be stored in the low- demand months and withdrawnin times of high demand. There are over 400 natural gas storage facil-ities in the US to support this balancing need. Most use depleted gasreservoirs as the storage space, but leached out underground saltdomes provide almost 8% of the storage capacity and another 8% isprovided by aquifer storage. Reservoirs take many months to fill andso can be cycled only once a year, although there is usually some flex-ibility in scheduling the injections and more flexibility in the timingof withdrawals. Salt domes require much less time to fill, perhapsone month or less, and so can be cycled many times per year if thereis an economic opportunity to do so. During the 2010/2011 heatingseason, a net amount of about 2,200 Bcf was withdrawn from USstorage and then about the same net amount injected during thesummer; however, gross injections plus withdrawals for the year ranwell above the annual net injections plus net withdrawals, showing


43


that many short- term storage injections and withdrawals are madeto support the daily physical market balancing, as well as the annualseasonal cycle of demand.Gas storage nomenclature denotes working gas capacity as the

amount of storage gas that can be cycled in and out of storage facili-ties as part of normal operations. An amount of base gas must bemaintained in the storage facility at all times to maintain the integrityof the facility. Base gas plus working gas added give the total storagecapacity. Most analysts of supply and demand are mainly interestedin watching the level of working gas in storage, as this represents thegas available to withdraw for market needs.As of early 2013, the EIA estimated that US facilities have the

ability to store 4,558 Bcf of working gas. However, the most workinggas actually in storage at any one time was 3,929 Bcf, in autumn 2012.The EIA also calculates the “demonstrated peak working gascapacity” by adding the non- coincident maximums for each facilityto get 4.24 Tcf, 94% of the design capacity. Latterly, additionalstorage has been added at a rate of around 75 Bcf of working gas eachyear. The maximum working gas capacity becomes quite relevant tothe market in years such as 2012, when the market was oversuppliedand excess production needed to find a home. In spring and earlysummer 2012, prices collapsed on fears that storage might fillcompletely, but low prices solved the problem as power producersturned off coal plants and turned on combined cycle gas turbine(CCGT) plants to burn the inexpensive gas.Analysts speculate on what minimum amount of working gas the

market “requires” at the end of the injection season. As can be seen inFigure 2.12, endof- season fills since the early 2000s have rangedfrom just under 3.2 Tcf to just over 3.9 Tcf. Because of the growth inuse, many believe that the market will now want to be near the highend of this range to ensure winter reliability of supply.Each Thursday, the EIA releases a weekly report giving their esti-

mate of the amount working gas in US storage as of the previousweek. This widely anticipated publication gives the single mostimportant short- term data point about the current supply anddemand balance, and often incites a strong price response from thenatural gas markets. Because of the high importance of the reportednumber, fundamental analysts labour daily to forecast it. The EIA’snumber itself is based on a statistical model that they use to extrapo-


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Figure 2.12 Working gas in storage (Bcf)

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late from their population of storage survey respondents to a total USstorage amount, and so has some level of uncertainty itself. Thisnumber represents the net injection or withdrawal summed over allUS storage facilities. Net injections typically begin in late March orearly April, making March the last month of net injections and Aprilthe first month of the year with net withdrawals, except in extremeconditions such as the warm March of 2012, which left that monthwith net injections. During autumn, November is usually the firstmonth to see weekly withdrawals, although there have been netwithdrawals as early as the last week of October or as late as the firstweek of December. In 2006, summer gas demand for power genera-tion was sufficiently strong and production low enough that therewere net withdrawal weeks in late July and early August.Because of this seasonality of injections and withdrawals, the

natural gas year is divided into the summer (injection) months ofApril–October, and the winter (withdrawal) months of November–March. This seasonality of storage manifests itself in the gas marketsas well. The volatility of the price spread between the October andNovembercontracts, and thevolatilityof theprice spreadbetween theMarch and April futures, are often the highest of all the sequentialmonth spreads. Also, the term structure of options volatility typicallyhas localmaxima for options on theOctober andMarch contracts.


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Table 2.2 US production and estimated net exports, demand and imports

Production Exports Demand ImportsState or region (Bcf/d)

Texas 20 11Louisiana 8 5Oklahoma 6 4Gulf of Mexico, Federal Offshore 4 4Arkansas 3 2Rockies (NM, CO, UT, WY) 15 13Marcellus (Northeast States) 7 12 4Midwestern States 1 12 11California 1 6 5Florida 3 3Southeast 1 7 6


GEOGRAPHY OF PRODUCTION AND DEMANDA large portion of the US gas supply has come from the Gulf Coastand midcontinent. Texas has the largest gas production at about 20Bcf/day. Neighbouring Louisiana produces ~8 Bcf/day, and gasfrom the Gulf of Mexico Federal Offshore areas comes ashore topipelines in Texas, Louisiana and Alabama, and adds another ~4Bcf/day of supply, although this is less than half of the offshoresupply levels seen in the early 1990s. Additional supply comes fromOklahoma (~6 Bcf/day) and Arkansas (~3 Bcf/day). There are twoother large supply areas outside of the Gulf coast /midcontinent.The Rocky Mountain states of New Mexico, Colorado, Utah andWyoming combine to produce about 15 Bcf/day of gas, andMarcellus Shale and other production in Pennsylvania and nearbystates adds about 12 Bcf/day.Texas,LouisianaandOklahomaalsoconsumelargeamountsofgas

for industrial use and power generation. Other demand centres arethe highly populated states of the northeastern US, the midwesternstates and California; Florida and the southeastern states use signifi-cant gas generation to serve summer cooling load. Table 2.11 showsproduction and estimated net exports for the main supply areas anddemand and estimatednet imports for the topdemand areas.An extensive pipeline network provides for the movement of gas

from the supply to the demand areas. Many pipelines have beenbuilt from the traditional Gulf Coast and midcontinent supply areas.Multiple pipelines, including Texas Eastern Transmission Company(TETCO), Transcontinental (Transco) and Tennessee Gas PipelineCompany were built to transport gas from the Gulf states to demandareas in the northeast. Some of these pipes are now backhauling gasfrom the shale fields of the northeast back towards the Gulf. FloridaGas Transmission and Sonat carry gas to Florida. Northern NaturalGas, Panhandle Eastern Pipeline Company, ANR and Natural GasPipeline Company of America (NGPL) deliver gas to the midwest. ElPaso Natural Gas and Transwestern Pipeline take gas west to theCalifornia market.The Kern River pipeline to California and the more recently built

Rockies Express Pipeline, which can move gas east to Ohio, providetwo primary outlets for gas produced in the Northern Rockies, whileTranswestern Pipeline can take San Juan Basin gas from NorthernNew Mexico and southern Colorado to Arizona and California.


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Figure 2.13 NG price (average of front 12 months) and storage levels relative to five-year trailing average (right axis)

Source: EIA for reported storage

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PRICE DYNAMICS OF GAS FUTURESFigure 2.13 presents historical natural gas prices since 2002, andrelates how fundamental drivers of supply and demand have trans-lated into changes of price regime. The figure shows the averageprice of the front 12 futures months in order to remove seasonalityfrom the prices. We have reviewed earlier the most important factorsinfluencing the supply/demand balance which, in turn, creates pres-sure on natural gas prices.Let us look at some of the fundamental drivers of price levels. On

the demand side, there is:

weather (winter heating, summer cooling loads);�

generation burn, gas versus coal price competition; and�

industrial demand changes.�

On the supply side, there are:

production trends (rig counts, drilling efficiency, pipeline infra-�

structure);weather- induced production disruptions (hurricanes, well�

freeze- offs);Canadian imports (Canadian production, demand);�

ethane rejection (ethane price uplift, infrastructure); and�

LNG imports, future exports.�

Figure 2.13 also shows how storage levels have varied over the years.It shows, for each weekly storage report, the surplus or deficit of thatweek’s storage levels relative to the previous five- year’s averageworking gas in storage for the same week of the year. In this way, wecan see on a seasonally adjusted basis how the gas storage levels arechanging. Because storage acts as the balancing mechanism for themarket, falling storage surpluses indicate tight (undersupplied)markets, and rising storage indicates loose (oversupplied) markets.We can see from Figure 2.13 that, for most periods having large

price movements, the storage moves inversely to price, as expected.This can be seen quite clearly during the run up in prices from nearUS$7.00 at the beginning of June 2005 to over US$12.00 by Septemberof that year. Storage surplus during this same period dropped from300 Bcf to 40 Bcf. Other clear examples of this inverse relationshipcan be seen in 2008 and 2012/2013. For many of the large price move-ments shown, the weather played a role, and several of the


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Figure 2.14 Prompt month price and front-year contango

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Figure 2.15 The annual evolution of the natural gas futures curve

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influencing weather events are noted on the figure. A cold 2002/03winter pushed gas storage down to very low levels and prices upabove US$6.00, before a mild summer in 2003 helped storage levelsrecover, and gas sold back down to below US$5.00. Similarly, anextremely cold January in 2008 started gas on its run towards priceswell over US$10.00. The recession of 2008 destroyed industrialdemand and sent gas prices back down, and this trend was exacer-bated by mild summer weather in 2008 that further decreased gasburn for power generation.Some time periods, however, show gas prices trending generally

upward while storage also builds, such as March 2004–December2004. For most of 2006 and 2007, storage levels trended, on average,lower, but prices graduallymoved lower aswell. The samehappenedmid-2009 to end-2010. Referring back to Figures 2.8 and 2.11, we seethat production was on a downwards trend from 2001 to 2005, sopricesmoved higher to drive out demand. Perhaps the storage buildsin 2004were not taken as a sign of structural surplus but a temporaryrespite from the tightening supply balances. In contrast, productionbegan its spectacular rebound in 2006, and the market took severalyears to understand and digest the implications of the shale gas revo-lution. Market prices were adjusting downward even during timeswhen the storage surpluswas reverting to near historical levels.Figure 2.14 shows historical prices for the prompt (ie, front)

natural gas contract. The figure also shows a measure of thecontango (slope) of the futures curve, calculated here as the price ofthe 14th contract less the price of the second contract – in otherwords, the oneyear contango of the futures curve starting at thesecond to expire contract. Clearly, the level of curve contango has astrong inverse relationship to the front month price level for most ofthe 11 years of price history shown.Because the contango of the price curve is quite volatile, traders are

attracted to trades sensitive to changes in the slope of the price curve.Many trading strategies attempt to profit from changes in calendarspreads by taking spread positions, shorting one month and goinglong a different month. Because of the seasonal nature of gas use andstorage, certain calendar spreads tend to have more trading interestand thus higher liquidity. Many of the favourite spreads involve thekey storage season months of October, March and April. TheMarch/April spread, sometimes referred to as the “widowmaker”,


52


trades actively, as do the April/October and October/Januaryspreads. Two other favourites are the January/March and January/April spreads,whichhavehigh sensitivity towinterprice seasonality.In addition to these seasonal spread favourites, the prompt/prompt+1 month spread is active, as is the second/third futuresmonth spread, as index fundmanagers andothermarket participantsare active in rolling forward their nearbymonthpositions.Figure 2.15 shows the evolution of the front 60 months of the

natural gas futures curve. Historical curves for each year, 2002–2013,are shown as of late March of each year, when the April contract isprompt. A number of interesting features can be seen from thisevolution. The front of the curve tends to lead in most price move-ments. Therefore, the curve will often go into backwardation whenprices move sharply higher, and contango steepens when pricesmove rapidly lower. The winter to summer month spreads clearlywent higher during the high gas price environment of 2005–2008, butcollapsed to very low levels in 2012 and 2013.

CONCLUSION: KEY ISSUES FOR THE COMING DECADESince the early 2000s, the natural gas market has moved from aperiod of declining production and use into a new period of produc-tion growth so rapid that it managed to push prices back belowUS$3.00, a price level that few in 2006 or 2007 ever expected to seeagain. These lower prices have encouraged drillers to concentratemore on crude oil production and less on dry gas, and at the sametime engendered a renaissance of gas- intensive industrial demand.Increases in gas demand for industrial use and power generationshould require additional gas production, and potential exports ofLNG will accelerate demand from around 2016. At what point intime will growth in associated gas production fail to keep up withdemand growth, requiring prices to rise to a level that will encouragemore drilling directed towards dry gas? How long will drilling effi-ciency gains continue to push down production costs, and willproduction costs begin to rise dramatically when the best shaleprospects have been produced? Even with renewed gas- directeddrilling, will production growth manage to keep up with the large price- induced demand growth that we are witnessing?All of these interesting questions will require constant reevalua-

tion over the coming years.


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54

GLOBAL LNG

Rita D'EcclesiaSapienza University of Rome

Global LNG flows reached over 200 MMT in 2012, the equivalent ofalmost 10 Tcf of gas or about 8% of world gas production. This panel willdiscuss major exporters and importers and possible trends going forward.

ExportsBy 2012, LNG exports represented about 30% of international gas flows.Global LNG exports grew from 117 MMT in 2005 to 203 MMT by 2012,an average annual increase of 10%. Table 2.3 shows the LNG exports forthe 10 largest exporters since 2005 including Canada scheduled to be amajor player by 2020.The biggest LNG exporters in 2005 were Indonesia (17%), Malaysia

(15%), Algeria (14%) and Qatar (14%), accounting for 60% of worldexports. By 2012, the balance had shifted and four countries – Qatar(39%), Malaysia (13%), Australia (11%) and Indonesia (10%) – accountedfor 73% of the total exports, with Algeria having heavily reduced its share.During this period LNG exports grew by 56 MMT. In terms of

geographic distribution the Middle East was the fastest growing exporter,growing from 38 MMT (28% of total) in 2005 to 85 MMT (43% of total) in2012, while the Atlantic Basin reduced its exports from 44 MMT in 2005to 37 MMT in 2012.Exports are tied to the liquefaction capacity of each country, therefore

we need to look at the existing plants and those planned for the nextdecade. In Table 2.4, the evolution of liquefaction capacity between 2000and 2012, and an estimate for 2020, is provided. The list of exporters withmore than 10 million metric tonne per annum (MMTPA) of liquefactioncapacity is short and rapidly changing. There are 20 countries exportingLNG and five major re- exporters (Belgium, Brazil, Mexico, Spain and theUS). Liquefaction capacity utilisation around the world averages 90%, andso its growth is critical to expanding volumes, whereas global utilisation ofregasification is only 35%.In 2001, the US was expected to become a major importer of LNG, but

by 2012 a resurgence in US gas production lead to the prospect of the USbecoming a major exporter once liquefaction trains become operational,expected to begin around 2016.Because of the high infrastructure costs of creating and delivering LNG,

most projects require longterm contracts that lock in the destination ofLNG produced. An estimated 25% of these flows are now short- termcontracts (less than four years in duration), and an increasing amount ofLNG flows are in the hands of international oil and gas companies (IOCs– see Table 2.4) with more destination flexibility.From 2008 to 2012, IOCs increased their share of export capacity by 45


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Table 2.3 10 largest exporters of LNG 2005–2015 (MMT)

2005 2008 2009 2010 2011 2012 D(2012–2005) 2015 D(2015–2012)* 2020 D(2020–2015)*

Algeria 15.9 15.9 15.7 14.3 12.5 11.2 –4.7 19.3 8.1 19.3 0.0Egypt 4.3 10.6 10.2 7.1 6.3 4.7 0.5 4.9 0.1 4.9 0.0Nigeria 8.0 16.7 11.6 17.9 18.9 19.6 11.6 14.2 –5.4 14.2 0.0Oman 5.7 8.6 8.1 8.6 8.1 8.2 2.4 8.3 0.2 8.3 0.0Qatar 16.8 30.0 36.9 56.2 75.4 76.4 59.6 75.3 –1.1 75.3 0.0Australia 9.2 15.0 17.9 18.8 19.5 20.9 11.7 21.7 0.8 77.3 55.6USA 1.1 0.8 0.6 0.6 0.3 0.2 –1.0 9.9 9.7 80.8 70.9Indonesia 19.5 20.1 19.3 23.5 21.9 19.0 –0.5 13.6 –5.4 15.1 1.5Malaysia 17.6 22.1 22.3 23.2 24.9 24.9 7.3 25.9 1.0 25.9 0.0Russia 5.0 9.9 10.6 10.9 10.9 9.6 –1.3 9.6 0.0Canada 16.9 0

World total 117.0 139.8 147.5 180.0 173.5 195.9 56.0 202.6 22.6 347.5 144.9

Major exporters D(2012–2005) D(2015–2012)* D(2020–2015)*

Maj Pac Basin 57.2 64.5 75.3 76.9 75.6 18.4 70.7 –4.6 144.8 74.0% of total 41% 44% 42% 44% 39% 35% 42%

Middle East 38.6 45.0 64.8 83.5 84.5 45.9 83.7 18.9 83.7% of total 28% 31% 36% 48% 43% 41% 24%

Maj Atl Basin 44.0 38.0 39.9 38.0 35.7 –8.3 48 8 119 70.9% of total 31% 26% 22% 22% 18% 24% 34%

* Estimates by GIIGNL.

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billion cubic meters per annum (bcm/a) from 85 to 130 bcm/a, led byShell, Exxon Mobil, Total, ConocoPhillips, Woodside and Chevron.National oil and gas companies (NOCs) increased by 74 bcm/a, from 137to 211 bcm/a. Trading houses, LNG importers, financial institutions andlocal companies represent the balance, 33 bcm/a in 2008 and 48 bcm/aby 2012. NOCs have an obligation to satisfy domestic demand, thereforeRussia, Nigeria and Indonesia are increasingly focused on the price gapbetween their domestic market and export prices. In general, IOCs aremore responsive to market conditions, and bring advantages in terms ofintegrated project development. European utilities with considerable LNGstrategies include GDF- Suez, EdF, E.ON and RWE.In 2012, Qatar dominated global export capacity with a 39% market

share and 84 MMTPA of liquefaction (see Table 2.5). The other MiddleEast exporters, including Abu Dhabi, Oman and Yemen, have no reportedplans to expand their liquefaction capacities. Qatar is a true swingexporter and, in the period 2008–12, sent on average 35% to Europe, 5%to the Americas and the rest to Asia (of which 33% was to Japan, 25% toeach of India and South Korea, 10% to Taiwan and 7% to China). Asiandemand growth is impressive (see Table 2.6). China has grown fromnothing in 2005 to 5 MMT in 2012, India from 6 to 10 MMT, Japan from 8to 16 MMT and Taiwan from 1 to 6 MMT. South Korea is the only stagnantAsian importer, with 9 MMT in 2005 and 11 MMT in 2012. Most of theLNG from Abu Dhabi, Oman and Yemen flows to Asia.The Pacific Basin liquefaction capacity stands at 92 MMTPA, repre-

senting 38% of the world total. It is expected to increase by 2020 as manylarge Australian and Canadian projects come online, and Australia isexpected to tie with Qatar’s liquefaction capacity. Indonesia has beenexperiencing domestic production outages, and is therefore planning toexpand its liquefaction capacity to send out 40% of production to thedomestic market. In addition, adding new liquefaction capacity in 2014,Indonesia is converting two ageing liquefaction plants to regasification.Malaysia has had a series of outages on liquefaction maintenance and hasminor plans for floating liquefaction in the future.Australia and Canada are positioned to be key exporters in this basin. In

the period 2005–12, Australia added 24 of the 26 MMTPA Pacific Basinliquefaction increase. According to planned new liquefaction plants,Australia will increase its capacity by 60 MMTPA, and Canada is expectedto build 17 MMTPA of liquefaction capacity by 2020, estimated as 50% ofthe 34 MMT of filed projects.The major Atlantic Basin exporters hold 23% of liquefaction capacity.

From 2005, Algerian capacity has remained unchanged at 19 MMTPA,still recovering from the 2004 explosion at Skikda that kept capacityoffline in the 2008–12 period. New capacity additions for Algeria havebeen quoted at US$1,000/MT capital costs. Egypt started as an exporter in2004 and has 12 MMTPA of capacity. Its economic growth has createdmore domestic demand, and it is planning to build regas capacity. By


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Table 2.4 Liquefaction capacity (MMTPA) (estimates by the author).

Country Basin 2000 2005 2008 2009 2010 2011 2012 2015 2020

Algeria Atlantic 19.4 19.4 19.4 19.4 19.4 19.4 19.4 8% 24.1 9% 24.1 6%Egypt Atlantic 0 12.2 12.2 12.2 12.2 12.2 12.2 5% 12.2 5% 12.2 3%Nigeria Atlantic 9.6 9.6 21.8 21.8 21.8 21.8 21.8 9% 21.8 8% 21.8 5%Oman Middle East 7.1 7.1 10.7 10.7 10.7 10.7 10.7 4% 10.7 4% 10.7 3%Qatar Middle East 16.1 25.5 36.9 60.3 75.9 83.7 83.7 35% 83.7 33% 83.7 20%Australia1 Pacific 0 12.1 19.8 19.8 19.8 19.8 24.1 10% 24.1 9% 85.9 21%USA2 Atlantic 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1% 10.4 4% 85 21%Indonesia Pacific 26.5 26.5 26.5 34.1 34.1 34.1 34.1 14% 33.95 13% 37.75 9%Malaysis Pacific 15.9 22.7 22.7 22.7 24.2 24.2 24.2 10% 26.07 10% 26.07 6%Russia Pacific 9.55 9.55 9.55 9.55 4% 9.55 4% 9.55 2%Canada3 Pacific 16.9 4%

100% 100% 100%

TOTAL 96.0 136.5 171.4 212.0 229.1 236.9 241.2 256.6 413.7

2005–00 2008–05 2009–08 2010–09 2011–10 2012–11 2015–12 2020–15

Capacity change 40.5 34.9 40.55 17.1 7.8 4.3 15.42 157.1and percentage

share

Capacity by area 2000 2005 2008 2009 2010 2011 2012 2012–2005 2015* 2015–2010 2020* 2020–2015

Maj Pac Bas 42.4 61.3 69 86.15 87.65 87.65 91.95 26.35 93.67 1.72 176.17 82.5% of total capacity 44% 45% 40% 41% 38% 37% 38% 37% 43%

Middle East 23.2 32.6 47.6 71 86.6 94.4 94.4 54 94.4 0 94.4 0% of total capacity 24% 24% 28% 33% 38% 40% 39% 37% 23%

Maj Alt Bas 30.4 42.6 54.8 54.8 54.8 54.8 54.8 12.2 68.5 13.7 143.1 74.6% of total capacity 32% 31% 32% 26% 24% 23% 23% 27% 35%

1 Adds 10 MMTPA capacity, or more.2 134.2 Mmtpa fled with FERC.3 Canada is expected to build in 2015, the 50% of 34Mmtpa in liquefaction capacity (estimates by the author).

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Table 2.5 Regasification capacity by country, 2000–20 (MMT)

Country 2000 2005 2008 2009 2010 2011 2012 2015* 2020*

Belgium 4 4 4 4 4 4 4 9 10France 7 7 7 11 11 11 11 20 22Italy 2 2 2 5 5 5 5 16 27Netherlands 5 5 15 20Spain 19 22 27 27 27 27 27 27 27Turkey 3 3 6 6 6 6 6 6 10UK 9 11 24 24 24 24 24 27Big 7 total 34 47 57 77 77 82 82 117 143

Europe 34 47 59 77 77 84 88 125 150

USA 3 8 46 53 78 83 83

Americas 3 8 50 60 100 110 112

China 8 10 10 12 14India 5 5 6 8 8 8Japan 104 108 115 115 116 117 118South Korea 44 55 55 55 55 55 55Taiwan 4 4 4 7 7 7 7

Asia 152 168 175 177 178 179 207

Middle East 4

Total 189 223 284 314 355 373 411

* Estimates by GIIGNL.

many estimates, Egypt will not be an exporter by 2020. Its utilisation ofliquefaction has dropped from almost 90% in 2008 to 40% in 2012 (seeTable 2.11). Nigeria holds 22 MMTPA of liquefaction, more than doublingsince 2000, but suffers considerably from political unrest and infrastruc-ture construction delays. Despite having the greatest gas capacity in theAtlantic Basin, it continues to struggle to perform. The US is expected tooperate 85 MMT of liquefaction capacity by 2020 out of the 135 MMT offiled projects, according to the author’s estimates.

Importers and import growthImport demand is relatively simple to analyse in the LNG market, giventhe different regional demand drivers. Asia depends heavily on oil, andLNG increasingly flows to the industrial complexes on the southern coastof China. China’s natural gas assets are in the Northwest, and while thetrans-China gas pipelines will inevitably be built, LNG is at least the short-



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Table 2.6 LNG imports by country (MMT)

2005 2008 2009 2010 2011 2012 D(2012–2005)

Belgium 2 2 5 5 5 3 1France 8 9 10 10 11 7 –1Italy 2 1 2 7 6 5 4Netherlands 1 1 1Spain 14 22 20 21 17 15 2Turkey 3 4 4 6 5 5 2UK 0 1 7 14 19 10 10Big 7 Total 28 40 48 62 63 47 19

Europe 30 42 52 65 65 49 20

USA 11 7 10 9 6 4 –8

Americas 12 11 16 21 19 18 6

China 3 6 10 13 15 15India 3.7 8 9 12 12 13 9Japan 47.2 69 66 72 78 87 40South Korea 18.8 29 21 28 35 37 18Taiwan 5.9 9 9 11 12 13 7

Asia 75.7 118 113 132 153 166 90

Middle East 0 0 1 2 4 3 3

TOTAL 117.7 195 181 220 241 236 119

term supply choice. Coastal India is another big importer, where GDP iscrimped by a lack of energy, and rolling brown- outs are common.Asia more than doubled imports in the period 2005–12, with Indonesia

and Taiwan starting to import in 2005, with Japan, China and South Koreaalso increasing their volumes. Europe and the Americas reduced theirLNG imports in 2010–12, despite increasing between 2005 and 2010(Table 2.6), due to factors such as price, the economic downturn andincreasing US domestic production.Asia is the largest importing region, with almost 65% of total world

imports. In 2008–12, Asia imported an average of 136 MMT (63% ofworld total imports). Of these, 55% was delivered to Japan, 22% to SouthKorea, 6% to China and the remaining 17% to India, Taiwan andIndonesia. Imports in Asia have staged a recovery after a contraction in2009 (–7%, see Table 2.7).European imports increased by 70% during 2005–12. In 2012, they

accounted for 21% of global imports. The largest importer is Spain (31% in2012), followed by France (15%), the UK (21%), Turkey (11%), Italy



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Table 2.7 LNG imports by country (growth rate %)

2008/2005 2009/2008 2010/2009 2011/2010 2012/2011

Big 7 Europe 40% 22% 29% 0% –25%

Europe 43% 23% 25% 0% –24%

Americas –2% 37% 33% –8% –5%

China 68% 70% 37% 13%India 119% 13% 35% –1% 5%Japan 45% –4% 9% 9% 12%South Korea 53% –27% 32% 26% 4%Taiwan 54% –3% 21% 9% 8%Asia 56% –4% 17% 16% 9%

Total 67% –7% 21% 9% –2%

(10%), Belgium (6%) and the Netherlands (1%). These six countriesaccount for the lion’s share of demand (96%).Imports by the Americas accounted for an average 17 MMT over 2005–

12, and in 2012 were a mere 7% of world LNG imports. The USaccounted for 44% of the volume followed by Mexico (19%), Argentinaand Chile (9% each), and 6% for Brazil and Canada.Two countries in the Middle East (Kuwait and Dubai) started to import

LNG in 2009 and in 2012 were an insignificant 1% of global imports.Imports of LNG are linked to the regasification capacity of the various

importing countries (see Table 2.8). In 2012, there were 93 LNG regasifi-cation terminals operating in the world including 11 floating facilities.There are two possibilities for significant regasification capacity growth

around the world. Both China and India have considerable plans toexpand LNG imports. The GIIGNL 2012 Annual Report lists eight projectsunder development in China that are expected to add some 15 MMT ofregas capacity. This would double Chinese import capacity. By 2020,China could be importing as much as South Korea. India similarly lists12.5 MMT of capacity under construction, likely to continue to behampered by logistical issues, and also lists a variety of terminal and distri-bution projects. This would more than double Indian import capacity intothe early 2020s.Regasification in Europe is mainly concentrated in the seven largest

European importers which have 82 of the total 88 MMTPA of regasifica-tion capacity. The utilisation rates swing depending on the LNG price. Forexample, Spain imported 22 MMT in 2008 and only 15 MMT in 2012. Theflexibility of imports in Europe is a reflection of its market maturity and effi-ciency. The LNG demand in Europe has been growing at a fast pace over2005–10, with an annual average growth of 19% to 2011, and declined



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heavily in 2012 (–25%, partly due to relative price and partly economyshrinkage). The large reduction of LNG demand is in line with the heavyreduction of natural gas demand in 2011–12 in Europe. In the period2005–08, virtually every European country, from Lithuania to Ireland,added regas capacity.In the US during 2005–08, a lot of regas capacity was built, but subse-

quently was not needed, so US utilisation rates are abysmal.Regasification global usage is only 35%, but capacity utilisation varies

widely by region. Regas capacity can provide flexibility and security ofsupply. Utilisation rates change as capacity is added and as other energyflows dictate. For example, between Spain and France energy may flow asgas or be wheeled as electricity.Table 2.9 lists regasification capacity utilisation rates. Italy’s data is diffi-

cult to follow, with listed additions apparently running earlier than officialopenings. India suffers from the same problem. Russia and China do run atexcess of nameplate capacity. Taiwan’s import data is suspect. All figurescome from GIIGNL.

Table 2.8 LNG regasification capacity by country (percentage of utilisation)

2008 2009 2010 2011 2012 2015* 2020*

Belgium 56 121 112 115 73 95 95France 132 89 96 97 68 96 96Italy 72 42 130 122 98 93 93Netherlands 13 10Spain 81 75 76 62 56 70 70Turkey 70 73 100 85 98 85 85UK 7 30 60 79 44 44 44

Big 7 total 70 63 81 76 57 69 69Europe 71 68 56

USA 16 18 11 7 4 11 11

Americas 16

China 41 56 95 110 107 82 82India 154 155 157 156 164 157 157Japan 60 57 62 67 74 64 64South Korea 52 38 50 64 66 54 54Taiwan 211 130 158 172 186 171 171

Asia 80 85 90

Middle East 72 75 80

Total 35 40 40

Source: Author’s estimate



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LNG supply chainThe cost of gas is critical to the analysis of future export availabilities, espe-cially for US shale gas. The cost of building liquefaction has risendramatically:

the variable costs of liquefaction in the US are approximately�US$2/MMBtu;transatlantic freight is approximately US$1/MMBtu; and�regas costs are US$0.50/MMBtu.�

This means a built- in supply cost which must be added to the natural gasprice (Henry Hub) of US$3.50/MMBtu for gas landed into Europe. Thisnatural gas chapter estimates the price at which we will continue toexpand US shale gas at US$4.00–5.00/MMBtu leaving us with a landedEurope price of US$7.50–8.50/MMBtu. Notwithstanding this high price,we expect to see a continued healthy European demand, especially if GDPgrowth can recover.Assuming Japan has an incremental freight cost of US$2/MMBtu the

natural gas price for Asia may reach US$10.50/MMBtu. The liquefactioncost in Canada, after the building of the planned liquefaction plants, isexpected to be close to US$1.70/MMBtu, and these volumes are directedto Japan.Terminal expansion is lowering costs along with expanding fleet and

vessel size. The global fleet is 378 vessels and 54,000,000 m3, includingfloating storage and regasification units (FSRUs). Only two vessels wereadded to the fleet in 2012, compared to 16 in 2011, three ships werescrapped and one was converted to an FSRU. More than 40 vessels in thefleet have been used for over 30 years and more than 250 vessels areunder 10 years old.

Table 2.9 European natural gas supply and demand in the European Union (bcm)

2000 2005 2008 2009 2010 2011 2012

Production 23193 21198 19328 17426 17779 15793 14965

average (2008–12) 1706

Consumption 44029 49613 49729 46512 50289 45305 44388

average (2008–12) 4724

Russian pipeline imports 19390 15128 18099 16429 18634 17856 18590

average (2008–12) 1792

Excess demand (BCM) 1447 13288 12302 12657 13876 11656 10833

average (2008–12) 1226

Excess demand in LNG equivalent (MmT) 1070 9833 9103 9366 10269 8626 8016

average (2008–12) 908



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The order book was 78 vessels at the end of 2012 and 27 new orderswere added in the year, of which 23 were LNG carriers ranging from150,000–172,000 m3, two FSRUs, one regasification vessel (RV) and onefloating liquefied natural gas (FLNG) carrier (210,000 m3). ICIS Heren hasforecasted that additional expansion is needed for the fleet in order toretire older ships in 2015–20.What may be more important for estimating future shipping flows is the

ever- increasing share of flows to the Pacific basin, rather than the Atlanticbasin, lengthening tonne miles. The future growth of European demand,on the other hand, depends mostly on building storage and distributionassets, where environmental and other compliance issues will be consid-erably more expensive than in emerging or frontier markets. Concernsover emissions seem to be curtailing European demand for LNG andcompressed natural gas (CNG) as a truck fuel.

Future LNG flow considerationsLiquefaction plant build costs in the early 2000s (such as Egypt’s US$250–350/MMTPA and Oman’s US$200/MMTPA) were comparatively low.Qatar RasGas II and III build costs were around US$350/MMTPA, whileQatargas IV was close to US$750. Australian Pluto was estimated atUS$800 and the Russian Sakhalin capacity got deferred on an estimatedUS$1,000. Geography, climate and political risks drive constructioncosts. An ever- increasing amount of gas trying to come to market fromemerging countries (Equatorial Guinea, Yemen, Peru, Angola, PNG, Libyaand Iran) will not help lower costs of future liquefaction capacity addition.This will make it increasingly easier for an IOC to get involved, comparedto an NOC.More generally, domestic gas demand is growing in many producing

countries – for generating power and water, fuels and petrochemicalproduction, as well as reinjection to oilfields.In terms of major exporters, we note that Qatar, who have paused lique-

faction at current levels, actually have approvals in place to expandliquefaction up to 105 MMT. This represents an opportunity. Nigeria stillhas considerable waste between gas field and liquefaction, and an uncer-tain future for further developing gas pipelines within Africa. The US has amajor opportunity to capture export market share, but energy exports haveno great historical precedent within the world’s largest energy consumer.Russia will inevitably add more LNG capacity for Asia.Europe has experienced a reduction in natural gas production since

2000, from 232 bcm in 2000 to 150 bcm in 2012 (see Table 2.9). Thedemand for natural gas reached a high of over 500 bcm in 2010, but by2012 was back near the 2000 level of 440 bcm. Russian natural gaspipeline exports to Europe have declined since 2000 bringing an increasein other import demand from 14.5 bcm in 2000 to 108 bcm by 2012. Thisequates to a European LNG demand of 80 MMT in 2012.



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Table 2.10 Total LNG capacity holders (bcm/year)

2008 2012 D(2012–2008) D%

IOC’sShell 19.3 27.4 8.1 42BP 15.3 17.3 2.0 13BG 9.7 9.7 0.0 0ExxonMobil 9.3 20.8 11.5 124Total 7.9 14.6 6.7 85ENI 6.3 7.3 1.0 16Repsol/Gas Natural 4.7 5.9 1.2 26ConocoPhillips 4.0 7.2 3.2 80Marathon 3.4 3.4 0.0 0Woodside 2.7 9.6 6.9 256Chevron 2.7 6.3 3.6 133

TOTAL 85.3 129.5 44.2 52

NOC’sPertamina (Indonesia) 39.6 39.6 0.0 0Qatar Petroleum 27.8 84.0 56.2 202Sonatrach (Algeria) 27.8 33.9 6.1 22Petronas (Malaysia) 25.4 26.5 1.1 4NNPC (Nigeria) 14.8 14.8 0.0 0StatoilHydro (Norway) 1.9 1.9 0.0 0Gazprom 0.0 10.0 10.0 10+

TOTAL 137.3 210.7 73.4 53

Table 2.11 Percentage plant utilisation

2008 2009 2010 2011 2012 2015 2020

Algeria 82% 81% 74% 64% 58% 80% 80%Egypt 87% 83% 58% 52% 39% 40% 40%Nigeria 77% 53% 82% 87% 90% 65% 65%Oman 81% 76% 81% 76% 76% 78% 78%Qatar 81% 61% 74% 90% 91% 90% 90%Australia 76% 90% 95% 98% 87% 90% 90%USA 56% 43% 44% 21% 12% 95% 95%Indonesia 76% 57% 69% 64% 56% 40% 40%Malaysia 97% 98% 96% 103% 103% 99% 99%Russia 0% 53% 103% 111% 114% 100% 100%Canada 100% 100%

Total 100% 100%

Source: Author’s estimate


This chapter will offer insight into the role of a commodity meteorol-ogist and how they aid our understanding of risk within commoditymarkets. Primary sources of information, methods of interpretationand strategy considerations are given from the perspective of anenergy trading firm. Weather linkages in other commodity marketsare also briefly discussed.

Weather drives daily volatility demand for natural gas. Weatherinfluences residential, commercial and electrical power end users,natural gas is burned in the winter for heating and electrical genera-tion requirements in summer. Regional demand differences andseasonality ultimately affects natural gas futures pricing andregional basis hubs. In natural gas markets, cold weather can forcepeak day demand events where price-induced curtailments mayoccur to non- temperature sensitive clients (ie, reduction of industrialload) in order to ensure that needed gas is available to residential andcommercial consumers. Residential and commercial sectors require-ments peak during the heating season, and gas must be stored tomeet the winter demand.

Weather is a constant source of short term volatility in natural gasdemand and price expectations. Therefore, a solid understanding ofthe relationship between weather and natural-gas fundamentals isimperative.

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3

A Day in the Life of CommodityWeather

Jose MarquezWhiteside Energy


WEATHER DATA BASICSMeteorologists working for commodity trading firms have long beenutilised in agriculture markets, where extreme weather conditionsaffect diverse crops throughout the year. The US National WeatherService (NWS) and several weather consulting firms providedweather information and forecasts for 1–5 and 6–10 day periods.Meteorologists then enhanced this information through furtherinterpretation and acted as a quality control for the weather forecastsprovided by these external sources. In the early 1990s with the dereg-ulation of natural gas, Enron was the first energy merchant to utilisemeteorologists on staff to expedite and maximise the accuracy ofweather forecasts. The company understood the significant correla-tion between temperature and natural gas demand, and that beingahead of the pack at incorporating incoming temperature changeswould help maximise profits on their large natural gas portfolio. Forexample, buying or selling natural gas molecules ahead of othersgave the ability to profit from expected increase or decrease indemand, which then moves price on a regional or national basis. Ofcourse, such methods to create a trading edge do not last forever.Soon, many other energy trading firms maintained their own staffsof in- house meteorologists. At one point, Enron had a team of sixpeople providing weather information to the trading desks.

The main daily source of weather information for everyone acrossthe globe comes from global weather models. Some models provideforecasts up to 10 days, others up to 16 days. In a nutshell, a globalweather model is a sophisticated mathematical model that uses a setof equations with diverse parameterisations that represent the Earthand atmosphere. Horizontally and vertically, the Earth’s surface andthe atmosphere is divided into grids or pixels that interact with eachneighbouring point, ultimately allowing calculation of a forecast forthe future state of the atmosphere. The resulting forecast may stepthrough time, starting with three- hour increments increasing to 12-hour time steps after 192 hours. The size of the geographic andtemporal grids are tuned in order to optimise the balance betweenthe number of required computations and the grid resolution, sincemore calculations are required using the higher resolution gridcompared to the lower resolution grid. As an example, the grid sizeor pixel may vary from 35-kilometre spacing up to 70-kilometrespacing for forecast periods after 192 hours (8 days).


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Meteorological and oceanographic data to initialise the modelscome from across the globe: from air and land weather recordingstations, weather satellites and commercial and military pilotreports. This immense dataset is gathered, assimilated and fed intovarious global weather models. An initial condition or initialisationdefines the beginning state of the earth–atmosphere system, beforeforecasts with a defined time stamp are calculated by the models. Asyou can imagine, the amount of data and the computational powerrequired to run these models are immense, and to truly obtain anadequate global initial condition requires full access to global data(some data could be considered confidential). Consequently,specialised government agencies or research centres with specialinternational agreements for data sharing are the only entitiescapable of producing a meaningful and skillful global forecast.

Therefore, meteorologists across the world obtain their dailytemperature and weather changes from global weather modelsproduced by various institutions. In the energy industry, the mainmodels observed and analysed are the American Model (GFS), theEuropean Model (ECMWF) and the Canadian Model (GEM). Inaddition, and to a lesser extent, there is the NOGAPS (US Navy) and short- range models such as the NAM (up to 84-hour forecasts). TheAmerican model is run by the US National Weather Service'sNational Center for Environmental Prediction, in Washington DC,the European model is run by the European Centre for Medium- Range Weather Forecasts, located at Reading, UK, and the CanadianModel is run by Environment Canada (Canada’s National WeatherService).

TheUSNationalWeatherServiceprovidesdaily forecasts for the1–5, 6–10and8–14dayperiods.The informationcomes indataoutputorgraphical format.

A DAY IN THE LIFEEarly in the morning, multiple weather sources release informationwhich could be utilised by the markets. The changes on weatherinformation and data are compared to the previous trading daydetermining upcoming changes in natural gas demand and settingthe tone for traders early in the morning. Traders know that colderthan normal conditions in the highest population areas of the US,mainly East of the Rockies in the winter means higher demand

A DAY IN THE LIFE OF COMMODITY WEATHER

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for the US as a whole. In summer months, warmer than normaltemperatures in the East, especially Texas and Southeastern US,means more demand for air conditioning, of which a largepercentage is generated by natural gas- fired power plants.

Meteorologists on staff do not influence the market with theirinformation or have an influence on Nymex pricing. Their informa-tion is kept in- house. On the other hand, weather information andforecasts come from multiple sources, including global weathermodels which have a broad dissemination across markets. Thus,large changes to the forecasts can create a tangible reaction in theenergy markets.

Meteorologists have their own language to forecast or explainweather patterns and/or phenomena. They talk in terms of geopo-tential heights, vorticity and jet streams to mention a few. Energytraders talk in terms of Heating/Cooling Degree Days (HDDs/CDDs), increase/decrease demand, confidence level and risks.Therefore, the most important job of the in-house meteorologist is to"translate" the meteorology language into an energy trader'slanguage. They link the language of science to trading. The meteo-rologist on staff will gather all relevant information available frommultiple sources and streamline it in a way that is easily accessibleand understood by the trading desks. The meteorologist could comewith the following checklist: How is the weather pattern evolving forthe 6–10 and 11–15 day periods? What is my confidence level in theweather pattern? What is the risk of the forecasts to change direction-ally and temporally? The in- house meteorologist gives a sense ofconfidence level for the existing forecast. If the staff meteorologistfeels that the current forecast may change then forecasting how thatchange is likely to occur, in timing and direction, becomes critical.

First, the meteorologist on staff has their own view of the weatherpattern for the 6–10 and 11–15 day periods. When all the movingparts are in agreement, for example when diverse global weathermodels forecasts are aligned, the job for the in- house meteorologist isusually uneventful. However, when the in- house meteorologist is indisagreement with the diverse global model’s output, the situationcan be quite challenging.

Most of the time, the divergence in forecasts starts when globalweather models are differing in their output. For example, theEuropean model may be showing a cold wave in the Midwest while


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the American model does not show it for the same time period. So,there is no middle ground here and a forecast must be made. Doesthe Midwest have a cold event or not? Therefore, the in- housemeteorologist has to react with a highly accurate, timely responseand be prepared to accommodate many information requests fromtraders.

An important process after having a forecast view of the incomingweather pattern is anticipating how or when the forecasts fromvarious sources may change. This task is called "forecast the fore-cast". Overall, agreement or disagreement with the forecast's outputfrom various sources serves as a confidence level barometer fortraders. Situations arise when the Nymex price moves strongly dueto forecasts of impending cold or warm events, and traders can putimmense pressure onto the in- house meteorologist to either changethe internal forecast or to precisely time when the forecasts willchange. Therefore, it is the meteorologist’s job to make such infor-mation both accessible and easy to understand, and to be clear andconcise about the risks from a challenging forecast.

Following Keynes’ advice that “Wordly wisdom teaches that it isbetter for the reputation to fail conventionally than to succeed uncon-ventionally”, the easiest way out is to agree with the general weatherview of the markets, and when the pattern “surprisingly” changes,then point to the fact that global weather models were wrong. Toprovide true value to the firm, however, the meteorologist mustmake the best possible assessment of forecasting the forecast revisionand communicate that opinion along with the relevant risks to thetrading desks. The meteorologist should not get overly boggeddown in details but focus on the importance of getting the weatherpattern right first, and then worry about the details. Simpler is better.

After the early morning weather operations are finalised, severalweather updates will arrive during regular trading hours. As thenumerical models update, any significant change in the weatherpattern compared to early morning weather information could causeprice volatility. The NAM is the first one to update, although thisweather model only provides forecasts up to 3.5 days ahead. TheGFS is the first global weather model to update the 16-day forecasts.The GFS is immediately followed by its ensembles, a package of fore-casts that show the level of stability or instability of the currentsolution. Then, the ECMWF updates after the American models are


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done. The whole updating process of new weather informationconsumes the last three hours of the regular Nymex trading day.

TROPICAL WEATHERThere is a seasonal weather system that creates quite volatile priceaction during the summer months: hurricanes. The hurricane seasonruns from June 1 until November 30 in the Atlantic Basin. The mainthreat area is the Gulf of Mexico, specifically from Mobile to justnorth of Corpus Christi. Historically, close to 10% of total gasproduction in the US could be impacted. The National HurricaneCenter (NHC), is the official entity responsible for issuing tropicalforecasts, watches and warnings.

NHC establishes a tropical cyclone as an organized system ofclouds and thunderstorms with a low level circulation rotating anti-clockwise in the Northern Hemisphere. Tropical cyclones developover tropical or subtropical waters. They are classified as follows:

Tropical Depression: Maximum sustained winds of 33 knots or�

less;Tropical Storm: Maximum sustained winds between 34 to 63�

knots. At this level, tropical cyclones are named; andHurricane: Maximum sustained winds greater than 64 knots. �

A hurricane’s exact centre location can easily be identifiable viasatellite imagery because of the development of an eye. In addition, ahurricane wind scale called the Saffir–Simpson is used to classifyhurricanes into five categories depending on their wind intensity.Category 1 hurricanes are dangerous and create some damage, whilecategory 5 hurricanes are monster storms that create catastrophicdamage. A storm is classified as a major hurricane when it reachescategory 3 or higher. In terms of the energy markets, the biggestconcern is when the hurricane becomes a major hurricane. At thislevel, structural damage to energy infrastructure may occur bothoffshore and onshore. Rigs and platforms can be destroyed andsevere damage may be inflicted on onshore refineries. Underwaterpipelines can also sustain damage due to heavy wave activity.

The NHC naturally has human safety as its primary objective, andhas been designated at the one official source of forecasts in order toreduce possible confusion during hurricane events. History has


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shown that conflicting forecasts and "hype" from different mediaoutlets creates public confusion as well as potentially causing confu-sion in the energy markets. Imagine if there were several scientificand media venues with different forecasts and weather/hurricanemodel solutions showing landfall of specific hurricane ranging fromNorth Carolina to Tampico, Mexico. NHC is the liaison for all thedata gathering, scientific streamlining, government safety planningand coordination, and dissemination of information to keep thepublic alert and informed. When a tropical cyclone develops, theysend standard advisories every six hours, at 0300, 0900, 1500 and2100 UTC, that include up to five days of forecast information. Whenthe tropical cyclone reaches a level of tropical storm or hurricane andmay be impacting land in the next 48 hours, watches and warningsmay begin to be issued, and intermediate advisories are releasedevery three hours between the main advisories after a watch and/orwarning has been issued.

Imagine such a large system being modelled mathematically,trying to represent the entire structure and energy of the tropicalsystem. That is what global weather and hurricane models try to do.As would be expected due to limited numerical capacity andinherent model limitations, different models will show somewhatdifferent forecasts and, even worse, may show quite different fore-cast tracks for the storm. Global models may start by showing atropical system developing on day 16 off of the West Coast of Africa,and Nymex price action may start to be influenced by the forecast. Inthis scenario, three basic questions should be asked: Is the tropicalsystem going to develop into a hurricane? Will it be a threat to theGulf of Mexico? Most importantly, is it likely to grow into a majorhurricane that can damage infrastructure?

Therefore, from the NHC advisories and the constant flow ofupdated hurricane output solutions from the models, the marketsbecome quite jittery, reacting to the diverse information as it isrevealed. If all models show the hurricane moving to the open watersof the North Atlantic, the market will see that as a 0% chance ofimpacting production. However, if one of the global or hurricanemodels shows the hurricane moving into the Gulf, there is a chanceof a market- moving event which will be reflected in the priceaction.

The in- house met has to constantly monitor all the information,


71


analyse all the forecasts available and, of course, forecast the forecastof the official tropical NHC advisory. The time between a tropicalcyclone developing off the coast of Africa and reaching the Gulf ofMexico can take nearly ten days. High volatility of energy pricescomes packaged with these systems and persists over the lifetime ofthese tropical cyclones.

OTHER WEATHER IMPACTSWeather updates during regular trading hours provide energytraders with significant demand change expectations for NorthAmerica down to a regional and individual city level. In the summer,power traders are the most sensitive to small changes in tempera-ture, cloud cover, precipitation and wind. Sea breezes orthunderstorms over downtown cities create rapid and significantchanges in electricity demand. Therefore, meteorologists providinginformation to power traders have to be in tune with radar and satel-lite images on a constant basis during the trading day.

AgricultureReuters, May 2013: “After a cold and wet spring in most of the UScrop belt, farmers have seeded 28% of their intended corn acres, upfrom 12% a week earlier but far behind the five- year average of 65%,… Chicago Board of Trade corn and soybean futures were tradinghigher on Tuesday, due in part to the slow planting pace that threat-ened to trim 2013 production prospects.”

October 9, 2012, the Financial Times reported that hopes for bountifulcrops in South America fell after forecasts reduced the likelihood ofEl Niño conditions developing, reducing the probability of above- normal rains during the growing season.

Bloomberg reported on May 2, 2013, “Oklahoma wheat production,already expected to decline 45% from a year earlier, may fall furtheras freezing weather tonight threatens crops.”

September 12, 2012, The New York Times story, “US Lowers Forecastof Crop Yields for a 3rd Time as Record Heat Lingers,” reported thatthe USDA lowered forecast corn and soybean yields as record heatadded to drought damage.

The normal daily meteorological operations used in the energy busi-ness can be extrapolated to other commodity markets for whichweather changes/influences the supply or demand for a commodity.The most obvious are the agriculture markets. The planting season


72


for corn or soybeans could be delayed or run ahead of timedepending on spring temperatures and rainfall. Too much rainfalldoes not allow planting processes to take place on muddy fields. Inaddition, corn needs a minimum of 50°F and adequate moisture forgermination. If soil temperatures remain below 50°F after planting,damage to the corn seed can be severe. Therefore, the germinationprocess could be curtailed. A cold spring, such as the spring of 2013,will delay the planting season and make the corn more susceptible tosummer heat during pollination. In the summer, drought conditionsand temperatures above 95°F with low humidity can cause damageto the exposed silks, potentially damaging pollen. During thisperiod, weather forecasts of potential heatwave across the US CornBelt can create a quite volatile price action in the corn market.

Transport

On January 4, 2013, Time reported that drought conditions coulddisrupt barge traffic on the Mississippi river, disrupting corn,soybean and grain transport.

Drought conditions in the Midwest and Ohio Valley can affect theriver levels at the Mississippi and Ohio rivers. Coal and agriculturalbarges might be restricted from travelling across the low levels ofthese rivers. Supply of coal and agricultural goods could be affectedon a regional basis due to transportation restrictions. Even nuclearpower plants can be affected by drought conditions: nuclear facilitiesneed large amounts of water for cooling purposes. After the waterhas been utilised in the plant, it is discharged back to a nearby bodyof water at a higher temperature. State and federal regulationsprohibit nuclear plants from continuing operations once the watertemperature reaches a certain threshold. There is a two- fold issuehere: it compromises the reactor safety and affects aquatic life.

LivestockJanuary 2013, Bloomberg reported, “Hogs futures climb as US coldmay hinder supply”, noting that Northern temperatures of –10–15°Fmight disrupt the movement of animals to market.

May 2, 2013, Farmers Weekly reported that UK livestock deathsexceeded 100,000 because of March blizzards and extreme freezingweather.


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A cold wave creates stress in cattle, despite the bovine beingextremely tolerant to low temperatures. An adequate winter coatand body condition in addition to availability of food and water helpthem to withstand the cold. However, the bovine will lose body fatduring a cold event and in many severe cold temperature events,hypothermia and death can occur. Newborn calves are also at highrisk of death during cold weather events. The cattle markets typicallyreact in quite a volatile way when these weather events occur in theTexas/Oklahoma Panhandle and lee side of the Rocky Mountains.

SoftsMay 29, 1997, The New York Times reported that “Fears of Freeze inBrazil Push Coffee Prices to 20-Year High.”

July 3, 2009, Bloomberg reported that cocoa crops in Indonesia andEcuador could be damaged by El Niño conditions, bringing lowerrainfalls.

Coffee futures can become quite volatile if strong cold events affectSouthern Brazil. Brazil is the largest coffee producer and the only onethreatenedby frosts. The coffeeplant cannot tolerate frost.Dependingon frost intensity, the flowers get killed or the entire tree candie. If theplantdies, thennewplantsneed tobeplanted–and it can takearoundthree years for them to bear coffee cherries. Vietnam is another largeproducer of coffee but the main weather threat to coffee production isthe landfall of typhoons into that country. Cocoa futures have theirmainweather risk indroughts.WesternAfrica, especially IvoryCoastand Ghana, are the largest producers of cocoa in the world. Lack ofsufficientmoisture causes the buddingpods towither.

CONCLUSIONThe basic tools of operational weather forecasting for the commoditymarkets are essential as an invaluable source of information fortraders. All these operations can be reduced to one goal: the bestweather fundamentals for forecasting supply and demand changes.


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In this chapter, the conversation on crude oil will be broken into twomain parts. The first section will cover the basics and mechanics ofthe current global market, while the second will address historicalprice perspective and why the state of the price exists as it does. Inthe first section, the basic fundamental and seasonal price drivers ofthe new global marketplace for crude oil will be examined.Subsequently, the chapter identifies the tendencies of crude oilpricing based upon supply and demand processes that effectuateseasonal price movements. Some details on the characteristics ofcrude oil that can drive price, including quality, grade, location andtransportation, will be next. Finally, the section will conclude with adiscussion of pricing and trading.The second part will discuss price perspective. It will address how

a US$17/bbl commodity in 2002 could become a US$147/bblcommodity by only 2008. It will question why the globe alwaysseems to be running out of oil, while, so far, that fate has yet to berealised.

WHY OIL?Critical fuel and elasticityWhat can you use crude oil for? This question has a strange, some-what counterintuitive, answer: not much! However, when crude oilis delivered to and processed through a refinery, this answerbecomes very different.

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4

Oil and Petroleum Products:History and Fundamentals

Todd J. GrossQERI LLC


Crude oil and its products are critical fuels to the world economyand have huge effects on our daily life. Whether you are using aplastic cup, filling up your car, heating your home during a coldwinter, or fuelling farm equipment to plant, harvest and bring cropsto market, petroleum plays an important role. The uses of petroleumproducts are generally linked to essential modern human needs, andthe demand for crude oil is generally inelastic.Examples can be too real for those who were waiting in queues in

the aftermath of Hurricane Sandy on the East Coast of the US inOctober 2012. Having unfortunately been affected first hand, thereturn of 2+ hour queues to fill your car or electric generator,rationing and police presence at stations resoundingly begs theinevitable question … why don’t we just use something else?Certainly those in New Jersey and New York City would haveinstantly shed their place in the queue for a readily available andcost-beating alternative, but they could not.There are many reasons for this, most of which point to the factors

of inexpensive cost and infrastructure. Crude oil and its productshave been the least- expensive source of energy across many areas ofthe economy for decades. This fact has led to an explosion of petroleum- related infrastructure that services most daily needswithout a reliable inexpensive alternative. Tankers, refineries,pipelines, trucks, stations and home furnaces point to a petroleuminfrastructure that makes our society reliant on them while offeringno credible alternative.These issues – infrastructure, price and convenience – have caused

a generally limited elasticity of downside demand, which issupported by the data. As Figure 4.1 shows, the drop- off inOrganisation for Economic Co- operation and Development (OECD)demand in 2008–09 was large in absolute terms, but less impressivein percentage terms: only a 6% decline during the worst recessionsince the 1930s. Furthermore, the West Texas Intermediate (WTI) oilprice could barely get back to 2004 levels of approximatelyUS$50/bbl on a quarterly average basis. This was a level that hadactually not been seen prior to 2004. Such an effect points to a gener-ally increasing price trajectory since the early 2000s. The elasticity ofdemand is roughly a 0.3 ratio to the change in GDP in OECD coun-tries. Essentially, if the OECD GDP increases by 1%, the demand forcrude should increase by approximately 0.3%.


76


This phenomenon is a stark contrast to non- OECD growth andelasticity. It is partially due to the fact that total US demand peakedin the 2004–05 time-frame. In Figure 4.2, a much higher elasticity ofdemand is indicated for these non- OECD countries. This ratio iscloser to 0.7. With the OECD and non- OECD countries accountingfor about equal amounts of demand, the average elasticity is approx-imately 0.5.However, Figure 4.2 shows another important point. Observe the

size and scale of the downturn in the non- OECD during the period

OIL AND PETROLEUM PRODUCTS: HISTORY AND FUNDAMENTALS

77

Figure 4.1 OECD liquid fuels consumption and WTI crude oil price

Source: US Energy Information Administration, Thomson Reuters

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

6 150

100

50

0

4

2

0

-2

-4

-6

Percent change (year-on-year) Price per barrel (real 2010 dollars)

OECD liquid fuels consumption WTI crude oil price

Figure 4.2 Non-OECD liquid fuels consumption and GDP

Source: US Energy Information Administration, IHS Global Insight

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

12

10

8

6

4

2

0

-2

-4

Percent change (year-on-year)

Non-OECD liquid fuels consumption Non-OECD GDP


we focused on in Figure 4.1: the 2008–09 period. The demand profileis skewed higher in the non- OECD countries. Growth rates arehigher and the recession area of 2009 is shallower. Does this come assuch a surprise considering Chinese growth rates of nearly 8%, alongwith the many emerging economies growing their manufacturingbase? Certainly not; all of these factors lead to limited elasticity ofdownward demand for crude oil.

SeasonalityCrude oil and its petroleum products also exhibit significant season-ality. In Figure 4.3, the monthly demand from 2008–12 along with theUS Energy Information Administration (EIA) projection for 2013shows that, even although each year exhibits a different slope(largely due to macroeconomic developments such as the economicdownturn at the end of 2008), the shape of the demand curves arenearly the same each year. Basic forces driving oil demand areapproximately the same from year to year. January is plagued by


78

Figure 4.3 World consumption patterns (2008–13) (in millions of barrels per day)

Source: US Energy Information Agency

82.00

83.00

84.00

85.00

86.00

87.00

88.00

89.00

90.00

91.00

92.00

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

World consumption 2011

World consumption 2008 World consumption 2012

World consumption 2009 World consumption 2013

World consumption 2010 World consumption average


some refinery turnarounds and holidays, such as the western NewYear and Chinese New Year. Also, it seems like a low level but, asdemand increases on average year on year, the January “low”demand is actually on the upswing from the previous November’strough. By February, some refineries return to service around theglobe to meet heating demand in the northern hemisphere. Then,there is the major second quarter fall off. As spring approaches, theglobal refinery complex goes into major turnaround mode.With major refining regions such as the US taking much of the

refining infrastructure down for maintenance ahead of theburgeoning summer seasonal usage, along with the moderation ofwinter temperatures across the northern tier, demand for petroleumtends to sag, culminating in the lowest demand period coming inMay. In the second half of the yearly cycle, demand escalates. USdemand for driving and transportation fuel picks up as many take tothe highways for summer vacation. The transportation fuel demandincrease is not only seen in the world’s largest oil consumer butgenerally around the globe, culminating in September. The strengthof demand in September is noticeable compared to many othermonths. Driving demand is still strong, early pre- winter seasonalrestocking of distillate and heating fuels in Western Europe is afootand the global refining industry has yet to go into its autumn mainte-nance mode. Finally, the waypoint of August and the third quarterhas exhibited stronger demand as air conditioning usage from devel-oping nations such as Saudi Arabia have kept demand strong whilemany are on holiday.Finally, as can be seen in Figure 4.4, the cycle is complete with the

second refining maintenance season in full swing across the globe aswe enter the fourth quarter. More vacuum distillation units (VDUs)and atmospheric distillation units (ADUs) are down for maintenancethis time around as opposed to fluid cat cracking units (FCCUs),which tend to monopolise the spring maintenance season.

Crude grades and locationsCrude oil, when it is taken out of the ground, either offshore,onshore, using traditional methods or with hydraulic fracturing(which has precipitated tremendous gains in onshore drilling, espe-cially in the US), can come with many different chemical make- ups.Based on the main use for crude oil of refining, each crude grade has


79


been tagged with two defining characteristics: light/heavy (based onthe number of carbon atoms) and sweet/sour (depending on thesulphur content). Grades are then given a name corresponding totheir respective production field name and/or geography, such asWTI, Brent Blend, Venezuelan Orinoco, Indonesian Minas,Malaysian Tapis, Saudi Arab Heavy, Oman, Ecuadorian Oriente,Nigerian Bonny Light and Dubai blends.The EIA defines light as crudes with an API gravity above 38,

heavy as crudes with an API gravity of 22 or below, medium as thosethat fall between 22 and 38 degrees, with 31.1 API as the dividing linebetween light and heavy.According to “Platts Energy Glossary”:

API gravity = (141.5/specific gravity at 60 degrees F) – 131.5.

As for sulphur content, the dividing line is approximately 0.5%sulphur, where a reading greater than 1.1% sulphur is consideredsour and a reading <0.5% is considered sweet. Figure 4.5 gives therelationship between most benchmark crudes and where they fall onthe light/heavy, sweet/sour spectrum.Generally, the process of refining is a chemical process that

enables the breaking and recombining of chemical structuresthrough heating (as in a VDU or an ADU) or by catalyst (such as anFCCU), in order to produce petroleum products for commercial use.Heavy crudes have a higher proportion of large molecules that areharder to break down, while light crudes have a higher proportion ofsmaller molecules. This leads to the lighter crudes that can produce


80

Figure 4.4 Seasonal world crude oil consumption


1.00

0.80

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

0.60

0.40

0.20

0.00

World crude oil consumptional seasonal 2008–2012


around 40% gasoline versus closer to 20% in the heavier crudes.These heavier crudes will, in turn, produce heavier distillates to thetune of around 60% of the barrel. These types of distillates can go tomake heavier materials such as asphalt. In refining, when burningcrudes in a refinery with heavy sulphur content, the output can emitsulphur dioxide (SO2) or hydrogen sulphide (H2S), a poison gas.Thus, to meet certain continually stringent sulphur specifications forpetroleum product production, a desulphurisation process hasbecome increasingly necessary in refineries. These processes help torefine more sour grades to meet specifications of products such asdiesel, low sulphur diesel or ultra- low sulphur diesel that have spec-ifications of >500 ppm, <500ppm but >10 ppm and <10 ppm,respectively (ppm = parts per million). The crude mix has beenmoving globally over time towards a heavier (higher) sulphur mix,which is why the longterm refinery strategy has been to upgradetheir refineries to be able to handle such lower- quality crudes.Upgraded refineries have been caught between more sweet crudesand condensates taken out of the ground, and the many globaldisruptions in locales such as Libya, which have been throttlingsupply.


81

Figure 4.5 Density and sulphur content of selected crude oils


20 25 30 35 40 45 50

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0.0

Sour

Sweet

Heavy Light

Sulphur content (percent)

API gravity (a measure of crude oil density)

Mexico – MayaSaudi Arabia – Arab Heavy

Kuwait – Kuwait

UAE – Dubai

Saudi Arabia – Arab Light

Iran – Iran LightFSU – Urals

Oman – Oman

Ecuador – Oriente

North Sea – BrentUnited States – WTI

Malaysia – TapisUnited States – LLS

Libya – Es Sider

Nigeria – Bonny LightAlgeria – Sahara

Blend

Iran – Iran Heavy

United States – Mars



82

Table 4.1 Monthly Imports from Ecuador to El Segundo, California

Date Company Commodity Entry port State of entry Origin BBLS SULPHUR API(000s)

August 2012 Chevron USA Crude oil El Segundo, CA California Ecuador 324 1 23.7August 2012 Chevron USA Crude oil El Segundo, CA California Ecuador 326 1 23.4August 2012 Chevron USA Crude oil El Segundo, CA California Ecuador 328 1 23.5August 2012 Chevron USA Crude oil El Segundo, CA California Ecuador 353 2.01 19.4August 2012 Chevron USA Crude oil El Segundo, CA California Ecuador 371 1 23.9August 2012 Chevron USA Crude oil El Segundo, CA California Ecuador 374 1 24


04 C

hapt

er C

IT_C

omm

odity

Inve

stin

g an

d T

radi

ng 2

6/09

/201

3 0

9:46

Pag

e 82

Furthermore, locations of crudes and their availability have ahuge impact on the type of refinery operations at a specific refinery.Although most refinery operations are very secretive about theirincoming crude oil slate, some refineries match very well with theirimport crude oil.Let us take Chevron and its assets in the western part of the US,

along with the corresponding crude oil inputs.Here is a good example. Table 4.1 comes from the EIA website that

tracks company- wide imports on a monthly basis. Why wouldChevron import medium- heavy oil (19–24 API degrees) that is medium- to- high sulphur (1–2.01% sulphur) from Ecuador to ElSegundo, California? In Figure 4.5, the specs fit Ecuadorian OrienteBlend well and, considering Chevron has E&P operations inEcuador, this would seem to make sense. Ecuador, being on thewestern side of South America, has relatively easy transport access toCalifornia, as opposed to the US Atlantic Coast or to many otherplaces. This crude is a natural fit for California, but why El Segundo?The answer makes the picture even clearer. Chevron owns and oper-ates the El Segundo refinery. The reason that the crude is a natural fitfor this refinery is no accident. Chevron intelligently spent quite a bitof money to upgrade this refinery to the specifications that wouldenable it to run such a complex refinery in the state of California (themost difficult place to refine in the US) and, at the same time, havethe capability to take the medium- heavy, medium- higher sulphurcrude oil of Ecuadorian Oriente.

Locations of major oil supply to demand and limitations of thetransport gridThe world’s oil supply has specific areas of concentration, with manyplayers moving in and out of prominance over decades. Their rele-vance is predicated upon their ability to cultivate reserves accordingto current economics (as in Venezuela and Iran, Brazil and Angola),on technology and each country’s willingness to adapt it (as in theUS, with hydraulic fracking) and on their ability to install an infra-structure that will enable production to grow and hasten its deliveryto market. On the other side of the coin is the ability for the crude oilbeing produced to match the corresponding refining capacity. Thismatch up enables the easy refining of demanded and legallypermitted petroleum products.


83


As we can see in Figures 4.6 and 4.7, production from the MiddleEast has historically been sent to the refining areas of the Gulf Coastof the US, as well as many other refining regions across the world.However, in response to refining capacity additions and subtractionsby region, oil trade flows have changed. The ability of westernnations to continue to compete globally on refining has becomesuspect. The US and Western Europe found their great refiningcentres under tremendous duress in 2011. Atlantic Basin crude oilprices, generally indexed to Brent and imported, were being used asfeedstock to produce higher and higher quality (lower sulphurcontent) products. These product specifications were mandated bythe EU and US governments. In addition, these refiners faced ever-more stringent quality standards on petroleum products whilehaving to combat a reduction in petroleum product demand since2008. Furthermore, higher fuel efficiency and the greater acceptanceof clean technologies have also cut into demand.The business case for continuing to produce petroleum products

in these two jurisdictions if a refinery was lower on the NelsonComplexity scale and its refining power was generally simple wasbecoming increasingly unprofitable. Examples of victims of thesetwo simultaneously negative phenomena were PetroPlus, the largestindependent refinery in Western Europe, Sunoco, the biggestrefining presence on the US East Coast, and ConocoPhillips, whodivested its downstream business and spun it off into Phillips 66


84

Figure 4.6 World oil production by region (millions of barrels per day)

Source: BP Statistical Review, 2012

Middle East, 27.7 Europe & Eurasia17.3

South & CentralAmerica, 7.4

North America14.3

Asia Pacific, 8.1

Africa, 8.8


(with this last case probably augmenting efficiencies in manage-ment). The replacements for these refineries were generally Indian,Chinese and Middle Eastern refining capacity additions. With fewerregulations for start- up, cheaper labour (none or fewer union labourconstraints) and closer proximity to the source of crude oil, thetrends depicted in Figure 4.7 are not only set to continue, but to beaccentuated in the coming years. After all, no major refineries havebeen built in the US since 1976. Capacity grew solely throughupgrades and increases in complexity. Meanwhile, Asian refiners,less hindered by regulation and clean air rules, have been buildingbrand new, more efficient refineries that can actually fill the gaps leftby those archaic, decommissioned refineries of the west.However, as this all seemed to be speeding out of control in the US

and Western Europe, a new technological breakthrough came alongjust in time to give at least a temporary reprieve for many US EastCoast refineries. Domestic crude oil, produced through techniques ofhydraulic fracturing from the interior of the US, has made its wayacross the country to the refining centres in a cost- efficient way. Thistrend has given some of these refineries hope. We will talk moreabout the phenomenon later in this chapter but, with the CarlyleGroup purchasing part of the Girard Point refinery from Sunoco andDelta Airlines purchasing the Conoco Phillips Trainer, PA refinery,the progression of this global change in regional refinery economics


85

Figure 4.7 Refining capacity market share evolution

Source: BP Statistical Review, 2012

24.21%

7.49%

30.19%

8.09%

3.80%

26.22%

North America Middle East

South & Central America Africa

Europe & Eurasia Asia Pacifc

22.99%

7.09%

26.42% 8.61%

3.57%

31.33%

2001 global capacity: 83.4mb/d 2011 global capacity: 93.0mb/d


seems to have been stayed. Additionally, the US Gulf Coast has beenimporting less and less crude to run its refineries, and within 2–3years the region should not need to import crude oil at all.1

The future trajectories of production, refining and storage arebeginning to change the market and may slowly change how crudeoil and petroleum are priced. Despite the extraordinary growth in USproduction, there is little risk to the status of the Middle East andformer Soviet Union (FSU) as major producers. The inclusion ofIraq’s huge reserves under a market- oriented and ambitious regimeprovides optimism for the continued strength of Middle Easterncrude oil production. With Brazil and Russia having gained ineconomic prominence, these countries will also have the ability tomarshal larger resources toward oil exploration and production(E&P) for increasing contributions in the global crude oil supply mix.Also, new technology and high prices have encouraged a renais-sance in US oil production.The real issues that have arisen from this sea change are logistical.

How does the crude get from production areas to the refiners, andwhat are the risks along these routes? The flow of crude oil fromMiddle Eastern countries to jurisdictions East of Suez has beengrowing for decades. However, with more and more of global oilproduction heading in this direction, the world oil transit choke-


86

Figure 4.8 Refinery additions (2010–35)

Source: OPEC World Oil Outlook, 2011

5

4

3

2

1

0US &

CanadaLatin

AmericaAfrica Europe FSU Middle

EastChina Other

Asia

Mill

ions

of b

arre

ls/d

ay

2030–20352025–20302020–20252015–20202011–2015


points should be examined, especially in light of the risks concerningthe Strait of Hormuz.

Crude transport and chokepoints2

There are seven major world transport and chokepoints for crude oiltanker movements (see Figure 4.9):

The Strait of Hormuz;�

The Strait of Malacca;�

Bab el Mandeb;�

Turkish Straits;�

Danish Straits;�

The Suez Canal/SUMED Pipeline; and�

Panama Canal.�

The number of barrels per day in transit is shown in Table 4.2.According to the EIA, about half of the world’s oil production

moves via maritime routes, the rest mainly transits throughpipelines. However, the Strait of Hormuz and the Strait of Malaccathat link the Indian and Pacific Oceans are by far the most strategic.


87

Figure 4.9 Potential chokepoints to global crude transport



Let us talk about the granddaddy of them all at first, the Strait ofHormuz, which is located between Oman and Iran and connects thePersian Gulf with the Arabian Sea. Here, roughly 35% of all seabornetraded oil and 20% of all oil traded worldwide passes through on adaily basis. More than 85% of these crude oil exports go to Asianmarkets such as Japan, India, South Korea and China. At thenarrowest point, the Strait is 21 miles wide and the width of the ship-ping lane in either direction is only two miles, separated by a two- mile buffer zone. The alternatives are woefully inadequate.Pipeline replacement capacity currently only offers 4–5 millionbarrels a day of unused capacity, and trucking would add only amaximum of a few hundred thousand barrels per day. Most tankersgoing through the Strait of Hormuz run greater than 150,000 dead-weight tonnage (DWT) – these are very large tankers. A block of theStrait of Hormuz would result in a shortfall of undelivered crude oilof perhaps up to 12 million barrels a day.The Strait of Malacca is the other main strategic point. It is

located between Indonesia, Malaysia and Singapore (where the bigPulau Bukom 500,000 barrel- a- day Shell refinery operates), andlinks the Indian Ocean with the South China Sea and Pacific Ocean.


88

Table 4.2 Volume of crude oil and petroleum products transported through worldchokepoints (2007–11)

Location 2007 2008 2009 2010 2011

Bab el Mandeb 4.6 4.5 2.9 2.7 3.4Turkish Straits 2.7 2.7 2.8 2.9 N/ADanish Straits 3.2 2.8 3.0 3.0 N/AStrait of Hormuz 16.7 17.5 15.7 15.9 17.0Panama Canal 0.7 0.7 0.8 0.7 0.8Crude oil 0.1 0.2 0.2 0.1 0.1Petroleum products 0.6 0.6 0.6 0.6 0.6Suez Canal and SUMED Pipeline 4.7 4.6 3.0 3.1 3.8Suez Crude Oil 1.3 1.2 0.6 0.7 0.8Suez Petroleum Products 1.1 1.3 1.3 1.3 1.4SUMED Crude Oil 2.4 2.1 1.2 1.1 1.7

Notes: All estimates are in million barrels per day. “N/A” is not available. The table does notinclude a breakout of crude oil and petroleum products for most chokepoints because only thePanama Canal and Suez Canal have official data to confirm breakout numbers. Adding crudeoil and petroleum products may be different than the total because of rounding. Data forPanama Canal is by fiscal year.Source: EIA estimates based on APEX Tanker Data (Lloyd’s Maritime Intelligence Unit);Panama Canal Authority and Suez Canal Authority, converted with EIA conversion factors


This is the key chokepoint in Asia. With 13.8 million barrels per day(mbpd) flowing in 2007, the Strait had ratcheted flows up to an esti-mated 15.2 mbpd in 2011. At the narrowest point, in the PhillipsChannel of the Singapore Strait, Malacca is only 1.7 miles wide. Ifthe Strait was blocked, nearly half of the world’s fleet would haveto reroute around Indonesia. With so much crude flowing throughthis waterway, it would not go undelivered as in the case of ablockage of the Strait of Hormuz; it would just have to be reroutedat greater costs and time to market.The rest have their strategic interests too. The Turkish Straits are

important for western pricing because it is a main thoroughfare thattransports Russian crude exports, as well as exports from Azerbaijanand Kazakhstan, to Western European refineries. Weather oftenimpacts transit in winter, forcing additional transit time of up toweeks in some cases. Finally, a Bab el Mandeb closure could keepPersian Gulf tankers from reaching the Suez Canal as it is locatedbetween Yemen, Djibouti and Eritrea, and connects the Red Sea withthe Gulf of Aden and the Arabian Sea. Most transit goes north todestinations in Europe, US and Asia. If impassible, it would redirect3.4 mbpd around the southern tip of Africa, a significant addition oftransit time.

Crude pricing and tradingNot all crude oil that is produced and delivered goes directly into therefinery for processing. The crude that awaits refining in any time-frame is held in storage. Storage is the most significant statistic ofover- or under- supply in the crude oil market.Most notable has been the effects of storage levels and capacity in

Cushing, OK, the delivery point of the CME/Nymex WTI crude oilfutures contract. Being a landlocked area with limited capacity andlimited transit to and from the storage tanks, the Cushing phenom-enon played a major role in the term structure of the Nymex futurescontract through 2010. As one can see from Figure 4.10, a significantamount of storage capacity has been added to Cushing inventoriessince the third quarter of 2010. This fact has alleviated some of therisks of storage congestion and stock- out phenomenon that hasplagued this storage area, and therefore the Nymex pricing ofprompt/term spread relationships.When storage levels approached high percentages of capacity, the


89


WTI market would go into a strong contango. Known as storagecongestion, this has not been overly studied in the “theory ofstorage” and academic circles. The volatility in the markets is usuallygreatest when the market is near stock- out capacity, and when thereis not a credible alternative to satisfy demand (Kaldor, 1939;Working, 1948, 1949). The opposite is also true. For certain commodi-ties, where storage is not universal and is limited, and there is littleoutlet for continued production, the price will pick up significantvolatility as full storage is approached. Spot prices will tend tobecome more volatile when storage operators are not seasonallyinvolved in the market or their facilities are near capacity (in a similarfashion to how front- month futures price volatility tends to increaseas expiration draws near (Samuelson, 1965)). As prices plummet andthe market becomes more volatile, the percentage movement inunderlying pricing and spreads can rival even the most extreme stock- out scenarios. Research from the likes of Carlson, Khokher,and Titman (2007) and Evans and Guthrie (2009) has suggested thatthere is more of a U- shaped relationship between spot price volatilityand the slope of the term structure of forward prices. Strangely, bothphenomena are less likely with greater storage capacity!Wecan see inFigure 4.11 the takeawaypoints forCushing crudeoil

and many additions to alleviate the so- called bottlenecks that inhibitthe transit of crudeoil to theGulf ofMexico’smajor refining area havebeen, and continue to be, implemented. However, generally, thereare inflows from local production and the incoming crudes off of the


90

Figure 4.10 Cushing storage


Sep 30, 2010 Mar 31, 2011 Sep 30, 2011 Mar 31, 2012

80

70

60

50

40

30

20

10

0

Shell capacity

Inventories

Workingstoragecapacity

Mill

ion

barr

els


Enbridge pipeline into the Cushing area. Outflows had been going tothe only consumers on the block, the local refineries. Other pipelinecapacity is in full swing, such as the Seaway pipeline reversal (it usedto bring crude up from the US Gulf Coast to the PADD II refineriesuntildomesticBakkenandSouthernCanadianproductionexploded),alleviating most bottlenecks. The consuming pipelines name thedestination. BP is flowing towards Chicago (or, more specifically,Whiting, Indiana) to its 410,000 bbl/dayWhiting refinery. Likewise,the Ponca line heads to the Phillips 66, Ponca City refinery (at 195,000barrels/day) and the Ozark pipeline takes off to St Louis area tosupply the Phillips 66 (formerly Conoco Phillips) Wood RiverRefinery at (300,000+ barrels/day). Also worth noting is CVREnergy’s 115,000 barrels/day Coffeyville, KS refinery, which has itsown line coming from the Oil Hub. Then there are other inputs, suchas the Enbridge Spearhead pipeline that delivers more Canadiancrude toCushing.Furthermore, Figure 4.11 shows existing and proposed pipeline

expansions, which continue to address transport issues of crude oil


91

Figure 4.11 North American oil pipelines

Sources: Map from Canadian Association of Petroleum Producers. TransCanada overlay from TransCanada Corp. Assembled for Watershed Sentinel by Arthur Caldicott.


from production areas in the north to the refining centres in the GulfCoast.Finally, Figure 4.12 points out the more important figures for

pricing the WTI near term structure. Two very different states of theworld existed for prompt second- month WTI spreads in September2008, and then quite the exact opposite in January 2009. OnSeptember 13, 2008, the 110 mph Hurricane Ike crashed into theHouston Gulf Coast, delaying crude oil imports and disruptinginfrastructure up the Houston Ship Channel and the Loop, to thepoint that Cushing inventories plummeted and the spike inprompt/second WTI spread blew out to US$29/bbl on expiration(See Figure 4.14). Then, only four months later, the opposite wastrue. Crude oil inventories were approaching a limit at 80% of thenstorage capacity at Cushing, Oklahoma, and global inventories weredramatically swelling. With capacity at just under 40 million barrelsin early 2009, the inventories ballooned to just under 35 million


92

Figure 4.12 Continuous prompt/second nearby spread WTI

Source: Thomson Reuters

Q3 09Q2 09Q1 09Q4 08Q3 08Q2 08Q1 08Q4 07Q3 07Q2 07 Q4 09 Q1 10 Q2 10 Q3 10 Q4 10 Q1 11 Q2 11 Q3 11 Q4 11 Q1 12 Q2 12 Q3 12 Q4 12 Q1 13A M J J A S O N DA M J J A S O N D MFJ A M J J A S O N DMFJ A M J J A S O N DMFJ A M J J A S O N DMFJ A M J J A S O N DMFJ AMFJ

10.5109.598.587.576.565.554.543.532.521.510.50-0.5-1-1.5-2-2.5-3-3.5-4-4.5-5-5.5-6-6.5-7-7.5-8.12

PriceUSDBbl

3/13/2007–4/23/2012 (NYC)Daily CL CL spread

Line, CL CL spread, trade price (last)12/13/2012, -0.52, +0.02, (+3.70%)


barrels (Figure 4.13). The ensuing change in prompt second- monthspread was dramatic.In the 2000s, the small glimmer of technological advancement in

hydraulic fracturing almost entirely captured headlines in thenatural gas arena as a production game changer. The realistic impacton global crude supplies was initally discounted because, althoughtechnology made additional US production theoretically possible,the barrels could not be moved from these interior locations due tothe lack of midstream infrastructure. Pipeline assets usually takeonshore crude oil from E&P areas to refinery gates for easy loadinginto the facility to make product. The completion of many new assetsto fulfill additional takeaway-capacity needs seemed several yearsaway. There was a trend towards the bankruptcy of East Coastrefineries that had similar issues to that of their cousins in Europe,and the stranded nature of this new crude production’s location. Theonly outlet seemed to be to get the oil to Cushing, OK. However,events have begun to change this state of affairs, although a lot moreneeds to be done to rectify the two main dislocations in the oil andproducts markets that are inexorably intertwined: the Brent/WTIpricing mechanism and the East Coast refinery market.The Brent/WTI spread market flourished because of two very

important attributes, that were both financial and physical in nature.


93

Figure 4.13 Crude oil stocks: Cushing, OK


40000

35000

30000

25000

20000

15000

10000

5000

0

Jan 04

, 200

8

Feb 0

4, 20

08

Mar

04, 2

008

Apr 04

, 200

8

May

04, 2

008

Jun 04

, 200

8

Jul 04

, 200

8

Aug 04

, 200

8

Sep 0

4, 20

08

Oct 04

, 200

8

Nov 04

, 200

8

Dec 04

, 200

9

Jan 04

, 200

9

Feb 0

4, 20

09

Mar

04, 2

009


The flagship contract on the Nymex was WTI, which fostered anactive, entrepreneurial place for hundreds of traders to provideliquidity for the crude oil futures contract. There was always a trans-parent price that could be transacted. Likewise, the counterpart inthe UK was the Brent Blend contract, which started on the IPE beforebeing owned by the ICE. The Brent contract had a little different make- up. Although not as liquid as a futures contract, it had theunique characteristic of being directly tied to Dated Brent, a morecommonly used benchmark for the spot price of crude oil.Furthermore, the the two contracts were easily linked as there was adirect route to get Brent to the same place as WTI. Take a loading ona tanker in the North Sea and drop it off at the Loop, the LouisianaOffshore Oil platform, or in the Houston Ship Channel. The crudecould then be piped onshore and up yet another pipeline, sending itnorth up to the Midcontinent and Cushing, OK (one of thesepipelines being Seaway). Brent typically traded at a discount to WTI,because most incremental refining barrels were absorbed by the USrefining machine and therefore Brent traded at a discount to Light


94

Figure 4.14 WTI – Brent price spread (January 2013 contract)

Source: Thomson Reuters2007 2007 2009 2010 2011 2012

Q4Q4 Q3Q2Q1 Q4Q3Q2Q1 Q4Q3Q2Q1 Q4Q3Q2Q1 Q4Q3Q2Q1

3210-1-2-3-4-5-6-7-8-9-10-11-12-13-14-15-16-17-18-19-20-21-22-23-24-25-26-27

.12

PriceUSDBbl

7/13/2007–12/18/2012 (LON)Daily WTCL-LCOF3

Line, WTCL-LCOF3, trade price (last)12/13/2012, -22.32, +0.41, (+1.80%)


Louisiana Sweet (LLS), generally by the cost of transportation to theUS Gulf Coast (USGC). LLS and WTI were generally linked by inex-pensive pipeline economics that would bring crude from the Gulf viathe pipeline. There existed a very liquid market in buying and sellingBrent cargoes, hedging with more liquid WTI futures and trading thespread back and forth actively.The spread (and its economics) were severely disjointed by the

unexpected explosion in Midcontinent supply coming from Bakkenshale in the US and production from Southern Canada. This increasecame at the same time that production in the North Sea wasdeclining to a point that there was a noticable drop in production outof the Brent, Forties, Oseberg and Ekofisk (BFOE) cocktail.Specifically, Nexen’s Buzzard field of 200,000 barrels per day,approximately 10% of the North Sea production, has had major,continuing maintenance problems. The BFOE cocktail that cargoeswere priced off of had compounding issues when Buzzard wasdown. Buzzard Forties production, one of the lowest- quality crudesin the cocktail, had a knock- on effect on price. As production wentdown, supply would shrink drastically. In addition, the cheapestelement of the cocktail was diminished, leaving even more expensivecrudes to make up the price. The cocktail had been priced on thecheapest-to-deliver crude. This phenomenon adds extra elasticity tothe Brent/WTI movement that had come to plague the market.Meanwhile, many remedies were being sought to alleviate this

price differential on Brent/WTI. While the East Coast and Gulf Coastrefineries were having their crude feedstock priced off of Brent, theMidcontinent PADD II refineries enjoyed the economics of land-locked Cushing pricing. There was financial incentive to redistributethe crude and try to alleviate bottlenecks. The sale of the Seawaypipeline and the reversal of flow has begun to help, but much moreneeded to be done. The Brent/WTI price differential (still at BrentUS$20 over) did not look like it would relent soon. The reversal ofSeaway, which has removed 150,000 bbls/day along with another250,000 bbls of throughput to be added in 2013, should help. Thepolitical football of the Keystone XL pipeline, as well as many lesser- known avenues, had been put to work to alleviate the glut and totake advantage of the US$20+ price differential. However, unfore-seen issues such as the limited storage tank capacity at Jones Creek,Texas, had diminished the Seaway pipeline’s effectiveness. This final


95


outlet for the Seaway pipeline only boasts 2.6 million barrels ofstorage and has limited the ability of Seaway to move all of its400,000 bbls/day capacity to the Gulf. These constraints hadprevented a resolution of the spread relationships.With many alternatives for crude transport unavailable, the

markets turned to an old school form of crude oil transportation: rail-roads. Rail loadings of oil have been soaring and the economics makesense. With many new terminals being built to handle much of thethroughput, the transport of crude via rail has been able to alleviatesome of the issues. This solution has changed the equation enough torationalise the economics of two East Coast refineries. With the hopeof getting Midwest crude oil, the business case has changed from anunprofitable venture such as those in Europe to big opportunities forthose including Monroe Refining (a division of Delta Airlines) andthe Carlyle Group. Both investors have bought two main East Coastrefineries previously set for closure because of poor economics. Theability to receive shale crude oil as feedstock has helped to make thebusiness case to keep these refineries open. Furthermore, in MonroeEnergy’s case, their supply chain of jet fuel in the New York marketand ability to supply competitors makes it a sound investment, withsome personnel who used to work at the refinery already being partof the Monroe team.


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Figure 4.15 Oil on rail transport

Source: AAR Weekly Railroad Traffic

2009 2010 2011 2012

2,86

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As Figure 4.15 shows, there has been a jump in railcar loadingswith petroleum. With growth near 45% for 2012, rail movement ofcrude oil is showing itself as the stopgap measure of choice betweenthe production and demand today and the time when lower trans-mission cost pipelines are built. Economics are showing the railing ofcrude from Bakken to the Gulf Coast as an approximate mid teensper barrel cost. These economics have enabled such railing. Risks tothis method have been highlighted with the crude oil rail tanker acci-dent July 6, 2013 in Lac-Megantic, Quebec.

Crude markets and tradingOil is traded physically in many corners of the globe. With bench-marks such as Dated Brent off the BFOE pricing, the Japanese CrudeCocktail (JCC) pricing many far eastern contracts, Oman/Dubaipricing a lot of the Middle East sour crudes and FSU Urals pricingmuch of Eastern Europe and distillate products, these crude oil


97

Figure 4.16 North Dakota railroad map

Source: www.Trainsmag.com (May 2012)


benchmarks are all interrelated. Global pricing is influenced by whatrefining capacity is operating or down for maintenance, and gener-ally if there are problems in loading (for example, in Nigeria whenthere are militant attacks). Maintenance in fields such as the Buzzardfield in the North Sea can also have an outsized influence on thesebenchmarks. Grades are crucial and which refineries take thosegrades can make the difference between a wide or tight sweet/sourspread differential. Crude oil is mainly moved on Dirty tankers with>150,000 DWT, or very large crude carriers (VLCC).Specifically for crude oil, Dated Brent is the most widely accepted

global crude oil benchmark, and always faces intense scrutiny fromproducers, end- users and regulators. Dated Brent is generally usedas a sweet crude benchmark and prices crude in the North Sea, WestAfrica, the Mediterranean, South and Latin America, Canada,Central Asia and Russia. More than 60% of the world’s internation-ally traded crude oil is priced against Dated Brent.3 Dated Brent isthe price assessment of physical cargoes of North Sea light sweetcrude oil. The term “Dated” refers to the physical cargo price forNorth Sea Brent light crude which has been allocated a specificforward loading date (10–25 days ahead). The North Sea light sweetcrude oil grades – Forties, Oseberg and Ekofisk – are also deliverableagainst the Dated Brent contract known as “alternative delivery”, asthe combination of all four crudes is known as BFOE. This combina-tion gives Dated Brent a supply of approximately 1.4 mbpd andprovides enough liquidity to sustain it as a benchmark. The windowfor pricing Brent occurs at 4:30 pm, London time. When prices ofDated Brent are high, the North Sea attracts crudes from WestAfrican and the Mediterranean, while when the benchmark price islow, North Sea pushes crudes to other places, such as the US GulfCoast. Historically, Malaysian Tapis and Indonesian Minas had beenthe benchmarks for sweet crude in the Far East and Asia. However,with production becoming smaller and smaller and fewer barrelsbeing available for export, the region has turned to Dated Brent formuch of its pricing, with even Indonesia pricing its barrels off thisglobal benchmark. The Asian version of Dated Brent is priced off aSingapore pricing window at close of business 4:30 pm, Singaporetime.As for sour crude oil, Dubai had historically been an Eastern

benchmark. However, as physical export supplies became scarce in


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the Asian/Pacific regions and import demand climbed to levelsgreater than 17 million barrels per day, these benchmarks needed alittle help. Therefore, the Dubai benchmark has added Oman and theDubai Mercantile Exchange has touted its Oman futures contractthat has a delivery point east of Suez and 860,000 barrels/day ofexport volume. As for the pricing of many grades of crude for export,these benchmarks enable pricing schemes, but vary based upondestination. For example, Saudi Arabia may price exports to Europebased upon the Brent Weighted Average (BWAVE) price, its exportsto Asia based upon Oman and Dubai and its exports to the USGCbased upon Argus Sour Crude Index (ASCI), an index of deliveredsour crude to the USGC.Product points have just as much relevance. With product trade

and transport becoming more of the global petroleum tradingmarket through the 2010s (International Energy Agency, IEA, 2012),one has to be cognisant of those that produce and those that willreceive. The ports of interest are mainly the USAC, USGC, SullomVoe terminal in the UK, Amsterdam, Rotterdam, Antwerp (ARA),Ras Tanura in Saudi Arabia, Singapore, Chiba in Japan, Shanghaiand the MED terminals in Fos Lavera near Marseille, France. Mostproduct pricing hubs are aligned with an important maritime port,usually one or many large refineries and, of course, most impor-tantly, storage facilities for petroleum products.Most petroleum products are moved on barges or clean tankers of

around 60,000 DWT. The terms FOB (free on board) and CIF (cost,insurance and freight) denote whether the pricing is based on thebuyer providing transport and the seller delivering the barrels “onboard”, or the seller covering transportation and insurance costs todeliver the cargo to the buyer’s destination port.On a global basis, the trend is for less refining activity out of

Western Europe and for those losses to be supplanted by gains inIndia and China. The growth of the giant Jamnagar complex in India,along with the upgrade of the Essar Oils refining complex from300,000 to 600,000 barrels per day, has shown India’s high- profilestrength in the refining sector. China has added a multitude ofrefineries since the late 2000s. These refineries have been located inmany different areas of China, and although not aggregated into asingle massive refining complex, they represent huge additions inrefining capacity.


99


As this was taking place, the bankrupcy proceedings of PetroPlus’mainland European holdings was happening. These older, lesscomplex refineries, which originate crude oil from long distances,and which are forced to deal with organised labour issues, havebecome less competitive on the global landscape. The final straw wasthe recent Libyan revolution that took away the much- needed sweetcrudes that some southern European refineries had used for feed-stock, not easily replaced by the sour FSU Urals blend that was themost readily available swing supply at the time.As refining moves East, pricing and benchmarks for the world’s

refineries will change. Figure 4.17 shows many of the benchmarks forcrude oil and the pricing points. As the North Sea faces continuingdecline in output capacity, the US production pushes Nigerian andAngolan crudes to the East, and refining interests procure moremarginal barrels from Middle East sources, some of the refiningbenchmarks may move towards the Oman contracts on the DubaiMercantile Exchange.The US is a different matter. With its strong refining base in the

USGC, its excess capacity has been mobilised to export products tocertain markets, many located in South America. As South Americandemand for products has continued to climb, along with the closureof the Hovensa refinery on St Croix, the Valero Aruba refinery andthe chronic maintenance needed at the giant Paraguana refining


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Figure 4.17 Pricing benchmarks for global crude oil

Source: US Energy Information Administration

Malaysia - TapisEcuador - Oriente

United States - Mars

United States - WTIUnited States - LLS

Mexico - Maya

Nigeria -Bonny Light

Libya - Es Sider

Algeria - Sahara Blend

FSU - Urals

North Sea - Brent

Saudi Aradia - ArabLight

Saudi Aradia - ArabHeavy

Kuwait - Kuwait Iran - Iran HeavyIran - Iran Light

UAE - Dubai

Oman - Oman


complex in Venezuela, US Gulf Coast refineries have been recruitedto export products to meet demand in the south.Several commodity exchanges offer futures contracts, some which

may be settled by physical delivery of the underlying crude orproduct, and some that may be settled financially. These futures arewidely used by producers, refiners and large consumers of crudeand products for price risk management, and are also traded byspeculators and investors who desire exposure to energy prices. Themain futures and options markets are traded on the CME and theIntercontinental Exchange. The products listed are WTI, Brent, Ultra-Low Sulfur Distillate (which was previously known as Heating Oil),Gasoil, RBOB and many other locational contracts (such as flat- priced delivery points of USGC, ARA or Singapore) that are listed onICE or cleared on CME Clearport. Although Heating Oil and Gasoilhave been the mainstay for pricing of global distillate demand sincethe early 1980s, these contracts are slowly being replaced by theirlower sulphur counterparts that are becoming a larger segment ofdistillate demand, with the ICE and CME adding Low SulphurGasoil contracts since 2012.

A HISTORICAL PRICE PERSPECTIVEFigure 4.18 illustrates a historical perspective of oil prices and someof the major effects since the early 1970s. The first commerciallydrilled oil well was drilled near Titusville, Pennsylvania, in 1859 byEdwin Drake. Even although kerosene production from crude oilgoes back to the Babylonians’ uses of petroleum, the implementationof the combustion engine and later uses in transportation were themain drivers of the pursuit of crude oil production. Early on in petro-leum history, 90% of the world’s crude oil was in Baku, Russia, andafter a century and a half Russia has once again become the largestproducer of crude oil, but, according to the IEA report of October2012, by 2017 the US will resume its place as the world’s largest oilproducer.However, the history of modern oil pricing really started in 1960

with the birth of Organization of Petroleum Exporting Countries(OPEC). During that era, Western demand for oil was mostly met byinternational oil companies (IOC) and production was mandated byquotas set by the Texas Railroad Commission. What followed was aseries of events that turned the price and availability of oil upside


101



102

Figure 4.18 Oil disruptions, OPEC spare capacity and crude prices

Source: The Rapidan GroupPrices: ‘72–’73 Arab Light, ‘74–present US refiner average imported crude cost.

Market fears ofan Iran-relatedHormuzdisruptionfaded afterApril

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Iran

Threatened oil supplyDisrupted oil supplySpare capacity (EIA)Crude oil price (RHS)

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down. Back in 1956, M. King Hubbert’s presentation to the AmericanPetroleum Institute suggested a peak in US production that actuallytook place (albeit for the time being) in 1970. Then, in March 1972, theTexas Railroad Commission declared that having quota restrictionson production was not necessary because demand had outstrippedsupply and that producers could produce at their capacities. Thisevent ushered in the shift of power over global pricing of crude oilfrom the West to OPEC. Shortly thereafter, there was the YomKippur War, and, with the West’s support of Israel, the ensuing Araboil embargo that lasted from October 1973 to March of 1974. Pricesskyrocketed.Once again, Hubbert’s ideas of a global production peak had

permeated into the market, now pointing to global productionpeaking around 1995. With the growth in production coming fromthe Middle East, and the economic changes and expansions on thehorizon set for what would become the nations of the G7 and eventu-ally the G20, the secular movement of Western economic powerstaking from the Eastern producers became the emerging status quo.In 1979, the Iranian revolution added another jolt to the spot crude

oil price. In late 1978, a strike by foreign workers who later fled thecountry during the 1979 revolution, helped Iranian productiondecline from more than six million barrels a day – from which theproduction has yet to recover. The 1979 revolution led into the 1980Iran/Iraq war, signalling a second oil price spike in a decade.However, with the resurgence of Soviet Era assertion for energy

dominance and new technologies for exploration and production,first the USSR and then Saudi Arabia in the 1990s stepped in to fillthe gap to become the number one and number two global oilproducers. With the emergence of a general global peace, excesssupply and better technology, Hubbert’s predictions looked implau-sible. In fact, in the late 1980s there were several events that helped tovault prices lower. In 1986, with Mexico becoming a strong regionalplayer in the West, the Mexican government offered to price crudedelivered on a netback basis. This meant that they would price crudeoil based upon the price at which products could be sold. As refinerswere basically guaranteed profits, they produced until there wasmajor oversupply, which pushed WTI prices on Nymex toUS$9.75/bbl (meaning that prices were down by 80% in a few shortyears). The lower price regime continued generally through the late


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1980s. A perfect example was a pre- OPEC meeting headline in TheNew York Times Business Section in November 1988, which read:“Three Cheers for US$5 Oil”. At the time, Kuwait was a chronic over-producer and kept the prices down. The Saudis suggested that theycould just flood the market with oil and be the last one standing...atUS$5/bbl. This particular dynamic seemed to replay over the nextfew years as a recurring theme, even although OPEC was able tocome out with an agreement in November 1988.Kuwait’s overproduction was not such a black and white case.

During this era, OPEC quotas were actually important. They werehard to enforce, but markets did enforce them, as otherwise the pricewould plummet, and OPEC ministers were forced to act. Kuwaitwas coming into its own at that time in oil production. The countrywas able to invest in production and grow its production capability,but they wanted to sell this new capacity. These aspirations eventu-ally caught the ire of Saddam Hussein and Iraq. The Iraqis believedthat Kuwait was originally (and still was) the 18th province of Iraq,and, due to its chronic overproduction, Kuwait was blamed forkeeping oil prices low.Much to the disbelief of the West (even although Iraq had amassed

hundreds of thousands of troops on the border days before), Iraq


104

Figure 4.19 Crude oil production trends (since 1960)


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01960 1970 1980 1990 2000

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invaded Kuwait on August 2, 1990 – and away went the price ofcrude oil. The price peaked in October of 1990 and, when the US leada coalition to free Kuwait in January 1991, it ushered in an era of astronger Western military presence in the Gulf region, relativelyunchecked after the break up of the Soviet Union in 1989. The resultwas a stable environment for oil prices throughout the 1990s. ExcessOPEC capacity trended higher throughout the decade as OPECnations added capacity faster than demand, and this excess capacityreached a level not seen since the Iran/Iraq war.In 1998, with the price of WTI trading near US$10/barrel, there

was once again trouble in OPEC. The supply situation had placed Sunni- lead Saudi Arabia at loggerheads with Shi’ite Iran based onpumping. By default, Saudi Arabia had become the swing oilproducer in times of market shortfalls, tightening their new allianceswith the consuming nations in the West – they had become the defacto central bank of oil. With overproduction coming from Iran andVenezuela, the balances were once again hard to maintain. Themarket found a bottom, but not until a real resolution on productionand quotas were reached by these countries.This market downdraft was not without casualties. With

Hubbert’s predictions about 1995 all but forgotten, perhaps the best“trade” of the decade happened with oil near US$10/bbl. Exxonbought its largest rival, Mobil, in 1999 at the bottom of the market.Hubbert’s global production assessments were not off, but some-what delayed by the one thing that has also reemerged in theprevious decade: technology. The ability to leverage existing oilfields by pumping large amounts of water into a field and thusexpanding its production capacity, enabled big oil fields, such as theGhawar oil field in Saudi Arabia, to increase or sustain its productioncapability when it should have begun to decline. Saudi Aramco,boasting the best technology of any oil “company” in the world, wasdefying production constraints with new technology.Finally, around the new millenium, some old predictions began to

take hold. After the economic downdraft in 2002 precipitated bySeptember 11th and the South American debt crisis, the growth ofemerging economies became noticeable. The Brazil, Russia, Indiaand China (BRIC) economies began to grow to a point where theconsumption of crude oil and refined products were overwhelm-ingly dependent on the ability to find oil.


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The era of finding onshore super- giant oil fields was gone. Thenew cost of finding fields and extracting was increasingly beingfocused on deep- water offshore finds that were expensive and riskyto excavate. With very large fields such as Mexico’s Cantarell indecline, and the West feeling the pinch of the fall- off in productionfrom the Hugo Chavez regime in Venezuela, there was great concernin the race for the marginal barrel. Areas such as the North Sea hadbegun a decline that continues to the present day. The one majorbright spot that Figure 4.19 does not point out is the upswing in USproduction that now boasts greater than 7 mbpd, reversing thedownward trend which was intact since 1970.Let us now look at Figure 4.20, which illustrates the growth in

consumption of the largest driver of the decade, China. Amazingly,since China became a net importer of crude oil, its shortfall hasgrown substantially to make it the second largest consumer of crudeoil after the US. This rapid growth and migration of the populace to amiddle class that is a global consumer of crude oil products has hada profound effect on price and excess capacity (as shown in Table 3).According to EIA projections, this trend will continue going

forward through to 2035. With much of the future growth in liquidsconsumption coming from China, India, other non- OECD Asia andthe Middle East, much of the supply growth will have to come fromsomewhere. Interestingly, OPEC is showing a growth in marketshare from about 40% to 42%. Therefore, the promise of Iraqi growthmay have some lasting effects on keeping OPEC share growing.Meanwhile, as shown in Table 4.4, with the production declines inthe OECD countries, the lone shining star is the US thanks to theshale production boom that may even supercede the estimates whichmay crowd out some OPEC production growth. The IEA claims thatthe US will be the world’s largest oil producer by 2017. This implies astaggering growth rate, which may be difficult to achieve given thetypically high decline rates for most new wells in the Bakken andEagle Ford shales.There are a few things to note based upon the overall trends.

Looking once again at Figure 4.18, the tightness of the supply–demand balance that ushered in this new era of prices largely tookeffect when the excess OPEC capacity shrank back below 3% ofglobal production (about 2.7 mbpd). At the same time, there was asecond stage ramp- up in Chinese demand (as shown in Figure 4.20)


106


at the acceleration point around 2003. Thus, the new price regimeentered the markets. With similar shortages during the first Gulfwar, the nominal price reached US$41 in late 1990. Contrast that timewith early 2009, in an oversupplied environment of having 6%+excess capacity the price was only able to fall to US$32/bbl. Thisprice action speaks to a new price regime.Note the price assumptions listed by the EIA in Table 4.4. These

price assumptions show a steady growth. The answer is sensible. Asexcess capacity continues to be very low, price needs to ration themarket’s demand. To get 109.50 million barrels of oil out of theground in 2035, many new fields, unprofitable at today’s prices,would require the ability to contribute to the global liquids produc-tion mix. Before the great recession that collapsed the markets in2008, price raced towards US$147/bbl, an incredible feat for acommodity that hit a low of around US$17/bbl in 2002. There wasthe push of Chinese demand, the faster decline in Mexican, NorthSea and US production, and a dwindling of excess capacity to a pointwhere only 800,000 bbls/day was projected to stand between easilyfunctioning markets and an aggregate stock- out.


107

Figure 4.20 Chinese net oil consumption


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The demand destruction that ensued from the recessiontemporarily changed the equation; however, does this risk still exist?Just as Hubbert predicted, in early 2008 an almost universal feelingof peak oil and high prices were beginning to be the norm. Then


108

Table 4.3 International liquids supply and disposition summary (million barrels perday)

2009 2010 2015 2020 2025 2030 2035 Annualgrowth (%)

Liquids consumption 2010–35

OECDUS 50 states 18.81 19.17 19.1 19.02 19.2 19.47 19.9 0.10US territories 0.27 0.28 0.31 0.32 0.34 0.36 0.36 1.00Canada 2.16 2.21 2.15 2.21 2.25 2.29 2.35 0.20Mexico and Chile 2.35 2.34 2.39 2.43 2.5 2.6 2.68 0.50OECD Europe 14.66 14.58 14.14 14.43 14.65 14.76 14.74 0.00Japan 4.39 4.45 4.51 4.6 4.62 4.51 4.42 0.00South Korea 2.15 2.24 2.25 2.35 2.46 2.53 2.56 0.50Australia and NZ 1.16 1.13 1.11 1.14 1.17 1.21 1.23 0.20

TOTAL OECD 45.94 46.4 45.95 46.5 47.19 47.72 48.24 0.20

NON- OECDRussia 2.73 2.93 3.02 2.94 2.91 2.94 2.97 0.10Other Europe andEurasia 2.15 2.08 2.3 2.35 2.45 2.55 2.63 0.90

China 8.33 9.19 12.1 14.36 16.03 17.65 18.5 2.80India 3.11 3.18 3.7 4.58 5.4 5.79 5.8 2.40Other non- OECD Asia 6.43 6.73 7.28 7.95 8.85 9.4 9.89 1.50Middle East 6.84 7.35 7.78 7.69 8.16 8.98 9.49 1.00Africa 3.23 3.34 3.3 3.37 3.57 3.8 4.09 0.80Brazil 2.52 2.65 2.84 2.94 3.15 3.47 3.8 1.50Other Central and

South America 3.07 3.19 3.49 3.66 3.81 4.05 4.09 1.70

Total non- OECDconsumption 38.41 40.65 45.82 49.83 54.32 58.62 61.26 1.70

Total liquidsconsumption 84.35 87.05 91.76 96.33 101.51106.35 109.5 0.90

OPEC Production 33.34 34.58 37.3 39.23 41.91 44.05 45.89 1.10 Non- OPEC production 51.01 52.47 54.46 57.1 59.6 62.3 63.61 0.80New Eurasia exports 10.25 10.53 11.11 12.6 13.94 14.85 15.54 1.60OPEC market share

(percent) 39.5 39.7 40.7 40.7 41.3 41.4 41.9

Source: EIA, “Annual Energy Outlook 2012”, Table A21



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Table 4.4 Production

2009 2010 2015 2020 2025 2030 2035 Growth(%)

Crude prices (2010 US$/BBL)Low sulphur light 62.37 79.39 116.91 126.68 132.56 138.49 144.98 2.40Imported 59.72 75.87 113.97 115.74 121.21 126.51 132.95 2.30Crude oil prices (NOM)Low sulphur light 61.65 79.39 125.97 148.87 170.09 197.1 229.55 4.30Imported 59.04 75.87 122.81 136.02 155.52 180.06 210.51 4.20

Petroleum liquids productionOPECMiddle East 22.3 23.43 25.46 27.16 29.77 32.07 33.94 1.50North Africa 3.92 3.89 3.62 3.42 3.37 3.31 3.27 –0.70West Africa 4.16 4.45 5.09 5.35 5.4 5.31 5.26 0.70South America 2.43 2.29 2.13 1.97 1.92 1.79 1.72 –1.10

Total OPEC prod 32.8 34.05 36.3 37.91 40.46 42.48 44.19 1.00

Non- OPECOECDUS 8.27 8.79 9.82 10.73 10.53 10.57 10.15 0.60Canada 1.96 1.91 1.79 1.82 1.82 1.81 1.78 –0.30Mexico and Chile 3 2.98 2.65 1.97 1.58 1.65 1.68 –2.30OECD EUROPE 4.7 4.36 3.7 3.33 3.15 3 2.83 –1.70Japan 0.13 0.13 0.14 0.15 0.15 0.15 0.16 0.70Aust and NZ 0.65 0.62 0.55 0.54 0.54 0.53 0.53 –0.60TOT OECD PROD 18.71 18.8 18.65 18.54 17.78 17.72 17.14 –0.40Non- OECDRussia 9.93 10.14 10.04 10.54 11.06 11.62 12.16 0.70Other EUR AND EURASIA 3.12 3.22 3.67 4.01 4.37 4.52 4.54 1.40China 3.99 4.27 4.29 4.46 4.79 4.93 4.7 0.40Other Asia 3.67 3.77 3.79 3.55 3.38 3.17 3 –0.90Middle East 1.56 1.58 1.43 1.31 1.18 1.06 0.97 –1.90Africa 2.44 2.41 2.4 2.54 2.68 2.7 2.68 0.40Brazil 2.08 2.19 2.72 3.34 3.87 4.21 4.45 2.90Other Central and South American 1.9 2.01 2.29 2.32 2.47 2.67 2.65 1.10

Total non- OECD prod 28.69 29.59 30.63 32.07 33.8 34.88 35.15 0.70

Total liquids prod 80.21 82.44 85.58 88.52 92.04 95.08 96.47 0.60

Other liquids prodUS 0.75 0.9 1.05 1.34 1.62 2.08 2.59 4.30Other North American 1.69 1.93 2.51 3.08 3.75 4.46 5.16 4.00OECD EUROPE 0.22 0.22 0.23 0.24 0.26 0.27 0.28 1.00Middle East 0.01 0.01 0.17 0.21 0.24 0.24 0.24 14.50Africa 0.21 0.21 0.28 0.37 0.38 0.39 0.4 2.60Central and South American 1.14 1.2 1.78 2.31 2.61 2.9 3.17 3.90Other 0.12 0.13 0.16 0.28 0.61 0.92 1.18 9.10

Total other liquids prod 4.14 4.61 6.18 7.82 9.47 11.27 13.02 4.20

Total production 84.35 87.05 91.76 96.33 101.51 106.34 109.5 0.90

Source: EIA, “Annual Energy Outlook 2012”, Table A20


came the recession, and one can see in the consumption numbers inTable 4.3 that very little (if any) growth is expected between 2008 and2015. The shale revolution coming from the US and southern Canadathen appeared. At US$50/bbl, these technologies are not financiallyviable, but, at US$70–80/bbl, they are profitable. Once again, thepeak oil whispers have faded because of technology and may stayquiet for a while if this technology becomes a universally acceptedmeans of production. However, our new pricing regime is in place.The price assumptions made by the EIA exist so that the market staysbalanced. This theme is an important one. As we move from oneprice regime to another, the effects of the market pricing is to rationdemand (as it has already done in many OECD countries since 2008)and to price in new technologies for production that become finan-cially viable at higher price points.

CONCLUSIONIn summary, the global landscape of the market for crude oil hasmany intricate influences, stemming from grade, location, politicsand its reception from its downstream counterparts at the refinerylevel. The growth in emerging economies have shaken the stability ofthe existing supply/demand balances, but have also ushered in anew era boasting new methods of combating the continuous strugglefor the globe to be well supplied with crude oil. However, even asHubbert had predicted back in 1956, the decline of crude oil as ourmain source of energy has been wildly overestimated. The cost andthe technological breakthroughs continue to preserve thiscommodity as a large part of our daily lives.

1 International Energy Agency, 2012, “Oil Market Report, November.2 US Energy Information Administration, 2012, “World Oil Transit Chokepoints”, August 22.3 Platts, 2011, “Dated Brent: The Pricing Benchmark for Asia–Pacific Sweet Crude Oil”, May.


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REFERENCES

Carlson, M., Z. Khokher and S. Titman, 2007, “Equilibrium Exhaustible Resource PriceDynamics”, Journal of Finance, American Finance Association.

Evans, L. and G. Guthrie, 2009, “How Options Provided by Storage Affect ElectricityPrices”, Southern Economic Journal, 75(3), January, pp 681–702.



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Hubbert, M. King, 1956, “Nuclear Energy and the Fossil Fuels”, Shell DevelopmentCompany, Publication Number 95, presented before the Spring Meeting of the SouthernDistrict, American Petroleum Institute, San Antonio, Texas, March.

Kaldor, N., 1939, “Speculation and Economic Stability”, The Review of Economic Studies.

Oliver, M., C. Mason and D. Finnoff, 2012, “Pipeline Congestion and Natural Gas BasisDifferentials: Theory and Evidence”, University of Wyoming.

Samuelson, P., 1965, “Proof that Properly Anticipated Prices Fluctuate Randomly”Industrial Management Review, 6.

Working, H., 1948, “Theory of the Inverse Carrying Charge in Futures Markets”, Journal ofFarm Economics, 30, pp 1–28.

Working, H. 1949, “The Theory of Price of Storage”, American Economic Review, 39, pp1,254–62.


The objective of this chapter is to provide an understanding of howthe wholesale electricity market functions, and to explain its specialfeatures compared to other commodity markets. Despite the liber-alised electricity markets having their first beginnings as far back asthe 1990s, there probably remain few people outside of the industrywho conceive of electricity as a traded commodity. This can be easilydiscerned from political discussions where there is regular pressureon government and regulators to intervene in the setting of elec-tricity prices.

However, an unhindered liquid wholesale market that sets pricesis an essential component of a competitive market for electricity.Otherwise new suppliers and new generators cannot enter themarket independently. This means all the usual components ofcommodity markets need to apply: the free interaction of supply anddemand, development of forward markets, the participation of adiverse range of traders with different motivations and strategies,and the provision of platforms offering a range of matching, clearingand settlement services. This chapter will describe how these basicbuilding blocks of traded commodity markets are applied in the elec-tricity sector, and examine some of the outcomes.

The following section will explore some of the special features ofelectricity and how they have influenced the development of whole-sale markets, before we look at how electricity is traded in practiceand introduce some of the products and markets that are typicallyfound. We will then examine the behaviour of different marketparticipants and explore some trading strategies, as well as review

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5

Wholesale Power MarketsWilliam Webster

RWE Supply and Trading


the development of market prices over time in some importantEuropean markets. The chapter will also seek to identify some keyissues that might affect electricity trading over the next decade, andend by considering some of the main sources of information on elec-tricity wholesale markets.

ELECTRICITY AS A COMMODITYThe unique characteristics of electricityThe scientific laws of electricityThe power market has particular characteristics that distinguish itfrom other commodity markets. These characteristics are mainly aconsequence of the scientific laws of electricity production, transmis-sion and consumption.

These laws mean that, for example, it is not straightforward totrace the production and use of individual electrons across the trans-mission and distribution networks. Likewise, these laws mean thatthe whole system has to be maintained at a constant frequency forpower plants and appliances to continue to function. There is there-fore an interdependency between market participants that is notseen in other sectors.

However, as with the peculiarities of other commodities, it ispossible to develop a traded market by introducing some approxi-mation around the consequences of these physical laws. Just as themarket for crude oil is able to deal with, for example, differentquality grades and delivery locations, so it is also possible to getaround the specificities about electricity as a product. So, althoughthe electricity system as a whole has to balance on a second- by- second basis, traded markets usually allow for market participants tobalance over a 15- or 30-minute period. These issues will bediscussed in more detail in the remainder of this section.

Dispatch arrangementsFirst, compared to other commodities, delivery of electricity isstrongly time dependent. It must be produced and delivered exactlyas it is used. This contrasts with other commodities that can be storedto a greater or lesser extent. Electricity is also unlike most othercommodities in that it has a dedicated delivery network: the trans-mission system. For electricity provision as a whole to continue tofunction, there must be equilibrium between the network, produc-tion and consumption in real time.


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Second, if there is a failure in the overall system, it will affect abroad range of users, and not necessarily those that caused thefailure. There is therefore a strong public-good element in electricitysupply. In particular, the electricity network can be characterised as “non- exclusive”. If the system as a whole works, it is there for every-body and nobody can be excluded from using it. However, electricitysupply is not a pure public good, in that it is not “non- rival” (in thesame way as, for example, street lighting). It is therefore competitivewith respect to supply and consumption in that the same MWhcannot be used twice. This means that a market structure can func-tion in the sense that the use of electricity can be rationed through theprice mechanism.

The main issue raised by these two points is, therefore, more aboutthe extent to which producers and consumers can interact directly, asin other commodity markets, or whether there needs to be a specifiedregulated intermediary.

In some jurisdictions, regulators impose a strong role for the trans-mission system operator (TSO) in overseeing the market process,and even in operational decisions. Under such arrangements, gener-ators feed in all their technical and pricing information to the TSO,who then calculates prices using this information and assumptionsabout demand. Such market arrangements are characterised as “central- dispatch” because the system operator decides how allgeneration plant is dispatched on the basis of the prices and technicalinformation that is submitted. In effect, the TSO buys electricity onbehalf of retail suppliers and their consumers.

Meanwhile, market arrangements where producers andconsumers (or usually their retail suppliers) interact independentlyare termed “self- dispatch”. In these cases, generators negotiate indi-vidually with retail suppliers via traditional traded wholesalemarkets structures – ie, a variety of traded platforms and exchangesas well as voice- broking services. The system operator then takes aresidual role in that they may adjust generation output via balancingactions and “re- dispatch” if this is necessary to ensure the overallsecurity of the system.

A simplified summary of these terms is provided below.

Central dispatch�

Generators provide price and technical information (eg, rampingparameters, start costs) to the system operator. The system

WHOLESALE POWER MARKETS

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operator compiles an efficient dispatch schedule on the basis ofthis information and expected demand. Generators run to thatschedule. The TSO calculates a price for each (eg, hour) and alltrading is based around that price (eg, Ireland, England & WalesPool).Self- dispatch�

Retailers contract in the market with producers to meet the needsof their portfolio of customers. Generators offer prices to themarket based on their plant characteristics and conclude transac-tions on a bilateral basis or through an anonymous exchange.Trading is continuous and dispatch decisions can be continuouslyupdated until a “gate closure” specified by the TSO. At gateclosure, a final dispatch schedule is notified by the generator to thetransmission system operator.Balancing actions and re- dispatch�

If, on the basis of the aggregate of final notifications, the system isout of balance or internal security limits are breached, the systemoperator will require some generators to change their actualoutput from the final notification amounts. This is usually basedon priced offers by generators to increase/decrease productioncompared to notified amounts.

Locational issuesThe production and consumption of electricity also has a locationalelement. However, it could be argued that this aspect is less impor-tant for electricity than for other commodities. Depending on thecharacteristics of the transmission network, it is not always necessaryto deliver electricity exactly to the point of consumption. Providedthe network is meshed enough, it is normal for most trading to beconducted around particular “hubs”, or on a zonal basis.

With a zonal market, common in Europe, the assumption is that�

transmission capacity is always available to deliver the energy tothe customer, wherever it is in that zone. This often requires“remedial actions” by system operators, such as re- dispatch(discussed above). But, as long as these do not become toofrequent or costly, these actions can take place outside themarket without upsetting trading.The main alternative, used in North America, is a nodal market�

where each node in the transmission network has a separate


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individual price. A hub price may then develop based around aset of nodes that usually end up with the same price, and whichare then treated as a price zone by market participants. In thismodel, market participants carry the locational basis risk ofpossible price changes between nodes. However, system opera-tors also sell transmission rights between these nodes to help themarket manage these risks.

Electricity “quality”Unlike many other products, electricity has the same “quality” foreach unit of production. One megawatt hour (MWh) is exactly thesame as another – unlike, for example, natural gas where a cubicmetre of gas might have a different calorific content. However, thisphysical reality has latterly been changed by environmental consid-erations. Consumers and governments may now place a highervalue on units that are renewable or low carbon. This is alreadystarting to make the trading of electricity more complicated. Forexample, under so- called “green certificate” schemes, retail suppliershave to purchase such certificates alongside the electricity they needin order to serve final consumers. Likewise, under other supportschemes, renewable energy might be sold in wholesale markets on a “must- run” basis, even if prices are zero or even negative. The factthat a section of the electricity market is asked to behave in a non- commercial manner makes it more difficult to form expectationsabout spot prices and discourages forward trading.

Electricity “market design”Overall, electricity markets are probably more complicated thanother commodity markets. This often raises the question aboutwhether they are, in fact, too complicated to allow for a normal stan-dardised and commoditised set of products to develop. Electricitymarkets are already not particularly liquid compared to othercommodities. If the market becomes further fragmented intodifferent time, location and quality characteristics, the future forstandardised trading begins to look rather uncertain.

In the meantime, these features normally mean that the wholesalemarket for electricity is, to an extent, something of an abstract regula-tory construction. Academic and regulatory literature often speaksof “market design” for electricity, which is not a term commonly


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used for other commodity markets. Nobody ever talks about crudeoil “market design” in a regulatory sense. Complications such asfreight costs and quality standards are left up to the market partici-pants to sort out for themselves.

Part of the challenge in electricity market design is getting thebalance right between the role of the market and that of governmentand regulators. Policymakers continue to struggle with this chal-lenge, even in the most mature electricity markets. Indeed, there is anobservable cycle backwards and forward between more regulatedand more marketbased policy frameworks.

Where can functioning wholesale markets be found?At the time of writing, there are several functioning and reasonablyliquid wholesale markets that perform the central tasks of pricediscovery, offer hedging opportunities and give signals to marketparticipants for efficient operational and investment decisions.Liquid wholesale power markets exist to a greater or lesser extent inseveral areas of the European Union, in parts of North and SouthAmerica, and in Australia and New Zealand. Traded electricitymarkets are also coming into existence in other countries. Thischapter will concentrate on the development of wholesale powermarkets in Europe, particularly in Germany and Britain (GB).

HOW POWER IS TRADED – THE CHARACTERISTICS OFEUROPEAN ENERGY MARKETSEuropean market design principles: The importance of thebalancing regimeEuropean market design is based on self- dispatch rather thancentralised dispatch of power production – unlike, for example, mostNorth American markets. It is therefore a bilateral two- sided marketin that generators sell into, and retailers buy from, wholesale markets.

As discussed, this means that the system operator’s role isrestricted to dealing with residual imbalances in the system as awhole and resolving any locational constraints. This takes place after“gate closure”, which is normally one hour before real time opera-tion. However, in reality, system operators sometimes have to beginto take some action before gate closure if plant expected to be usedfor balancing or re- dispatch needs to be ramped up or warmed inadvance.


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Market participants on both the generation and the retail side haveto balance at gate closure across a so- called “settlement period” ofeither 30 minutes or 15 minutes. Those market participants whoseactual measured injections do not match their consumption are saidto be “out of balance” and are subject to imbalance charges. Theyhave to pay the system operator for the actions required to balancethe system. This payment is governed by the national regulator in thecountry concerned. It is usually based on the costs to the TSO ofresolving imbalances, although the formula used varies in eachcountry. Balancing arrangements are increasingly marketbased,with the settlement price based on bids and offers from those gener-ators with spare capacity, or alternatively demand- side offers.

An important consequence of this market design is that trading ofelectricity and also price formation is strongly driven by the desire ofmarket participants to avoid the consequences of being out ofbalance. If a company goes into gate closure with a short position,they are potentially exposed to very high imbalance prices at partic-ular times. Likewise, being long at gate closure is not without riskseither, particularly if imbalance prices can go negative, which is apossible outcome. The balancing mechanism is therefore at the heartof European market design.

Day ahead and intraday marketsThe other main reference price in European markets comes from the “day- ahead markets”. These are largely two- sided cleared auctionsoperated by dedicated market operators. For example, in Germanyand France the auction is run by EPEX Spot (a joint venture betweenEEX and Powernext). Meanwhile, day- ahead auctions in GB and inNordic markets are operated by Nord Pool Spot. The Dutch day- ahead auction is operated by APX- ENDEX (now a subsidiary ofICE), who also operates a day- ahead market in GB.

Day- ahead exchanges are not usually compulsory marketplaces.However, there is a strong regulatory push to ensure these marketsare liquid. In the draft European network code on capacity allocationand congestion management (CACM), it is envisaged that these day- ahead exchanges will play a central role in allocation of cross- bordertransmission capacity. This process is known as market coupling.The CACM network code was slated to become binding Europeanlegislation in 2014.


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As well as the day- ahead markets, there are various platforms forintraday trading. Unlike day- ahead, which is almost exclusively an exchange- based auction, trading in the intraday can either be exchange- based or a bilateral over- the- counter (OTC) market. Thisoften depends on the historical development of markets and regula-tory attitudes. For example, in the Nordic countries intraday tradingis exclusively via the Elbas platform, which is provided by NordPool, whereas the system used in Germany is a platform that allowsboth exchange- based trading and bilateral exchanges.

Forward marketsPhysical versus financialHowever, the day- ahead and intraday phases are only for fine- tuning positions. The vast majority of electricity is traded long beforethis point on a wide range of forward markets of different types.

Forward products may be either physical or financial. Financialtrading are contracts for difference that are based around a day- ahead reference price. With financial trading, a strike price is agreed(eg, €40/MWh). If the day- ahead price is above this – for example,€45 – then the buy-side counterparty will buy their power in the day-ahead market and the seller of the forward product will pay them the€5 difference. The buyer does not take on any obligations withrespect to balancing and nomination, as discussed earlier.

Physical contracts are used when both parties are already respon-sible for balance. Then the transaction is an obligation on the sellingparty to physically deliver the amount sold or else face the imbalancecharges on behalf of the buyer.

Brokers such as Trayport and Spectron offer a screen- based brokerservice based on physical delivery. Other products, such as thoseoffered by EEX, APX- ENDEX or Nasdaq, are financial trades basedon contracts based on the day- ahead prices. Voice- activated tradingis also possible.

Forward market productsThere is a range of possible products for forward trading. The mostliquid market is for baseload power, meaning a flat amount of powerover a 24-hr period. Baseload power can be traded weekly, monthly,quarterly, by seasons or annually. Trading may be either exchange- based and cleared, or through bilateral OTC transactions. Trading in


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seasonal and annual baseload products usually goes out to 2–3 yearsinto the future for both financial and physical settlement.

The other main product traded forward is peakload. This refers tothe period of 0700–2300 each day. Again there is a range of forwardpeakload products available. However, the forward curve is not asliquid as for baseload. Products are usually only available for 1–2years in advance of real time.

Finally, it is also possible to trade four- hour blocks in someEuropean markets, such as in the GB market. However, these areusually not available until some days/weeks before real time.

Spark and dark spreadsThe final complication to mention is that trading in baseload prod-ucts, in particular, is largely on the basis of “spreads”. For example,the “spark spread” is the difference between the electricity price andthe cost of producing that electricity from a certain standard effi-ciency gas- fired power plant, based on the prevailing gas prices. The“dark spread” is the same concept for coal. With the advent of carbontrading, indexes for “clean spark spread” and “clean dark spread”were developed which are popular forward products, particularly inthe GB market where both coal and gas have liquid reference prices.

HEDGING STRATEGIES AND PRICE FORMATIONMarket participants will usually have some pre- specified proceduresabout how they interact with wholesale markets. This will partly bedriven by the company’s risk controls. No company will wish to takeor maintain a position that will leave it too exposed to a disadvanta-geous movement in prices. In particular, taking on large exposedpositions requires the company to allocate risk capital to tradingactivity that is earmarked to cover possible adverse price move-ments. In addition, accounting rules, specifically the InternationalFinancial Reporting Standards (IFRS), may also discourage compa-nies from taking large positions since these have to be “marked tomarket” in a company’s account. This can result in a potentially largeimpact on the company P&L, with undesirable knock- on effects oncredit rating and market sentiment.

In general, the expectation is that retailers hedge the bulk of theirpositions in advance through trading of baseload and peakloadproducts. They will then use the short- term markets for fine- tuning


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their exposures. They may have some kind of target “hedge path” interms of what proportion of their consumers’ needs should becovered by a certain date – eg, that X% should be bought by Ymonths before consumption.

Likewise, generators will also sell the bulk of their generationcapabilities in forward markets in order to allow for effective busi-ness management. For example, the generation business will need toknow in advance how much revenue they are likely to collect in aparticular year. They will then be able to decide on a maintenancetimetable and other budgeting decisions. However, they will notnecessarily sell all potential volumes into forward markets since thisimplies a risk in the event of a generation failure.

In essence, price formation in forward markets, and thereforecustomers’ bills, is the consequence of how these decisions are takenabout how, and when, to buy and sell. For example, the more that thesupply–demand position is expected to be tight, the more thatretailers will tend to try and manage their exposure to short- termmarkets and seek to hedge earlier, pushing up forward prices.Conversely, if there is expected to be large margins of spare genera-tion capacity, retailers may be more content to delay buying volumesand wait for prices to fall. Similarly, generators may have to acceptselling at lower spreads if they see a lot of spare generation capacityaround and there is little prospect of prices increasing in spot markets.

HISTORICAL PRICE PERSPECTIVEGermanyFigures 5.1 and 5.2 show the main trends in electricity prices inGermany. The German electricity market is the most liquid inEurope, if not the world. Trading is based on a single Germany/Austria day- ahead reference price.

Initially, market opening between 2000 and 2005 led to significantreductions in wholesale market prices as more competition wasintroduced and trading became established. Prices graduallyincreased between 2005 and 2008, bringing considerable new invest-ment in generation. Some 10GW of new conventional plant beganoperation in the period 2010–13. However, the financial crisis andreductions in industrial demand have bought about significant pricereductions. This was only partly reversed by the enforced closure ofall German nuclear plants in 2011 after the Fukushima disaster.


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The other important feature in the German market is thecombined impact of increased renewable production and energyefficiency initiatives. From 2010, renewable production started tohave a profound impact on the market mainly due to the sheer scale


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Figure 5.1 Germany year-ahead forward prices (2005–13)

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of investment in this sector. Take- up of renewables has been rapid asproducers benefit from a guaranteed feed- in- tariff. Compared topeak consumption of around 80GW, there is now some 30GW ofwind production. Meanwhile, solar photovoltaics capacity increasedfrom 10GW in 2010 to 30GW in 2012. In Germany, renewableproducers do not themselves sell their own production. Neither dothey have to balance their portfolios like other market participants.Instead, the TSOs have to accommodate all renewable production,which they themselves sell on day- ahead and intraday markets. Thisis known as “priority dispatch”. The high installed capacity ofrenewables means there are now frequent incidences where most, orall, of electricity consumption is served by renewable production.

Understandably, this affects price formation on both spot andforward markets. Spark spreads have become particularly weak andhave been negative since the start of 2012. The impact has beenparticularly strong on peakload prices, with the difference betweenbaseload and peakload prices narrowing. This is because normalpeak periods have been offset by high levels of solar productionduring the afternoon period in some parts of the year. In general, asrenewable penetration continues to increase, the classic baseload andpeakload products may begin to lose their relevance and alternativeproducts may need to emerge in order for the market to fulfil itsfunctions effectively.

To an extent, periods with high renewable production can beoffset by imports and exports of power to neighbouring countries.Since 2009, Germany has participated in the central–western Europe(CWE) Market Coupling project. This uses the day- ahead powerexchanges to allocate cross- border capacity such that power auto-matically flows from low prices areas to higher priced areas. Thismay help the transition of markets to the high renewables world.

Great BritainFigures 5.3 and 5.4 illustrate similar data for the GB market. As forGermany, there is a single price zone that covers all of the island ofGreat Britain.

GB prices have followed a fairly similar pattern to those inGermany. The fall in demand in GB was, if anything, morepronounced than in Germany with an abrupt negative effect on cleanspark spreads. Capacity margins are such that forward prices at the


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time of writing do not show much sign of recovery despite the antic-ipated closure of some 10–15GW of generation capacity up to around2017.

Renewable production has not yet reached the same level of pene-tration as in Germany and its impact will continue to grow.However, a key difference in the GB market is that renewableproducers are, and will continue to be, responsible for selling theirown power and, other than the smallest facilities, are balance- responsible. This may prevent the impact on prices being of the samemagnitude. The subsidies for solar production and the extent of take- up, in particular, are markedly less generous.

Compared to total peak demand of some 60GW, there is around12GW of renewable production, a much lower percentage than inGermany. Only around 1GW of solar photovoltaics has so far beeninstalled in the GB market.

Wider relationships between European marketsEuropean markets are becoming increasingly correlated, especiallyas interconnection between EU countries increases and the existinginfrastructure is managed more efficiently via market coupling.However, there are still major locational issues and associated basisrisk that affects them.

The main locational features of European power supply is that,due to hydroelectricity resources, the Nordic countries usually havea year- round surplus of generation (unless there is a very coldwinter, preceded by very dry conditions). This often leads tocomparatively low wholesale prices in the Nordic system.

Both France and Belgium have high shares of nuclear power andthese countries have traditionally had low wholesale prices.However, the high level of peak heating demand increasingly meansthat these countries now import in the winter. During 2009–12, theprice differential between Germany and France closed and hasreversed to an extent that in France, prices are higher than those inGermany. Both GB and the Netherlands electricity prices are typi-cally driven by gas prices, and a locational spread with Germany willemerge if gas and coal prices deviate. Italy has typically had thehighest wholesale electricity prices in the EU.

Differences between these regions are maintained as a conse-quence of constraints in the overall European transmission network.


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Generally speaking, the construction of new transmission assets isvery slow as a consequence of local resistance to new lines beingbuilt. The main problems are objections to the visible appearance ofnew transmission lines. Transmission assets are normallyconstructed on a regulated basis, although there have been a few sub- sea merchant interconnectors, such as Britned (between GB andthe Netherlands).

At the same time, the local supply–demand balance also tends tomove rather slowly as new generation assets are added and othersclose. Overall, the extent of price differences between Europeanregions has tended to reduce slightly over time.

New developmentsPower prices are increasingly driven by regulatory interventions, inparticular the objective of European Union countries to extendrenewables and to decarbonise. As already noted, the significance ofthe traditional baseload and peakload divisions of wholesale prod-ucts is beginning to be questioned. Locational issues are alsobecoming more complex as there will no longer be price areas thathave low or high prices throughout the years or seasons. Instead, thevariations will tend to be increasingly seen in short- term markets.

Another regulatory development may come from possiblechanges to the price zones. At the time of writing, the EU is devel-oping network codes that will embed the methods of marketcoupling that have already been in use for some time. Part of thisdiscussion, however, is about whether the price zones as of 2013,mainly based on national borders, accurately reflect the real trans-mission constraints in the network. This raises the prospect of pricezones being split, or indeed merged, in the future. This may affecthow basis spreads between different zones develop. If the pricezones more closely matched transmission constraints then the basisspreads between zones would probably be larger and more stable.

A final important locational issue may arise from the introductionof flow- based market coupling. This model better takes into accountthe inter- relationships between use of capacity on different inter -connectors in the meshed European networks. For example,suppose there are three price areas: A, B and C. In reality, thecapacity available between area B–C is affected by how much elec-tricity is flowing between A and B and between A and C. A


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flow- based approach explicitly takes these interactions into account.With a non- flow- based approach these relationships are notcaptured and the available transmission capacity between each areais set independently.

The flow- based model will, in all likelihood, tend to make theenvelope of interconnection capacity larger. On the other hand, itmay be more difficult for market participants to understand the priceformation process and make it more difficult to formulate a tradingstrategy.

KEY ISSUES FOR THE COMING DECADEEvolving market designThe main issues for the coming decade are mainly regulatory ratherthan purely economic. In particular, the increase in renewableproduction will be the main challenge for the market between 2013and 2020. First, it creates longterm uncertainty, beyond the tradinghorizon, about what level of renewables penetration will occur. Inaddition, the way in which renewable production is activated andsold into the market also brings short- term issues.

Under priority dispatch schemes, as in Germany, the renewablepower tends to be sold into day- ahead and intraday markets by thesystem operator rather than being spread over forward markets.This creates unnecessary volatility and uncertainty. There are somemoves towards removing priority dispatch rules and asking renew-able producers to sell their own production into the wholesalemarket. This is expected to introduce more commercially orientedtrading strategies that will be more predictable and stable.

Meanwhile, in the GB market things are moving in the oppositedirection. Under the proposed contract for difference (CFD) scheme,renewable producers will be compensated for the difference betweenthe day- ahead price and a negotiated fixed “strike price”. So,although renewable producers will be required to sell their ownpower, the linkage of the CFD to the day- ahead price may againmean that plant is not being optimised in a predictable commercialway.

Other regulatory developments include the intention, in many EUmember states, to introduce capacity mechanisms. This is part of thepolicy response to the uncertainty created around the extent ofrenewables and other low- carbon penetration. However, these inter-


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ventions will inevitably have an impact on power prices. They willalso introduce a further set of regulatory uncertainties that will makedeveloping a trading strategy more challenging, and this is likely toreduce the liquidity of forward markets.

Finally, as volatility moves from the forward markets to more short- term markets, different traded products may increase insignificance. Option products are a market- driven way to rewardcapacity and flexibility. There may, therefore, be greater use ofoptions to allow portfolios of intermittent generation to be managedeffectively. Of course, this will only happen if renewable producersare responsible for their own portfolios and if a voluntary optionmarket is not undermined by regulatory interventions. Somedesigns of capacity market such as the “reliability options” modelused in North America are effectively a compulsory, centralisedoption market.

Financial market regulationFinancial regulation is also set to have an impact on the format oftrading. The EU regulation on OTC derivatives, central counterpar-ties and trade repositories (EMIR) came into force on August 16,2012. It includes a requirement to centrally clear transactions once acompany’s portfolio exceeds a certain threshold of €3 billion. Manylarge energy trading houses may be captured by this and, if so, therewill be an increase in the amount of cleared transactions as a result.Discussions on the exact requirements were ongoing throughout2012–13 via the “Draft Technical Standards”. These were producedby the European Securities and Markets Authority (ESMA) and,following the scrutiny of the European Parliament that concluded inFebruary 2013, they were to be adopted by the Commission asbinding requirements via the Comitology process and phased inover three years: 2013–16.

In addition, discussions were ongoing during 2013 about newversions of the Markets in Financial Instruments Directive (MiFID).The old directive will be replaced by MiFID2 and a regulation(MiFIR). One possible outcome is that trading houses above a certainsize will be regulated in the same way as banks, complete with strictcapital requirements. However, there are possible exemptions thatare being discussed, including the ring- fencing of some physicaltrades when determining whether companies exceed the threshold.


129


MiFID is also expected to set requirements on companies regardingposition limits and risk management techniques.

Many of the proposals put forward in the EU context are alreadypart of the legislation in the US, via the Dodd–Frank Act. Traders willhave to get used to compliance with this type of regulation.However, all these tend to add transaction costs and potentiallyreduce the liquidity of wholesale markets. Other interventions havebeen regularly floated, such as the Financial Transaction Tax orsporadic restrictions on short- selling. These could have a similarnegative impact on traded markets.

Changing consumer requirements – more bespoke services?Other more consumer- driven factors are also relevant. The spread of small- scale renewable generation may tend to move the marketaway from more centralised solutions, and in the direction of morelocalised and bespoke solutions. New technologies such as electricitystorage may also be more easily developed on a small scale. This willmean that the traditional relationships between producers andconsumers will become blurred so that they become amalgamatedinto one role. So- called “prosumers” may become much more usual.Again this may make a centralised traded market less important. Onthe other hand, the development of alternative, innovative tradedproducts may still preserve the role of the classic trading function.

SOURCES OF MARKET INFORMATIONThere is a wide range of sources of information on Europeanmarkets. In 2005, the European Commission established the EnergyMarket Observatory, which now produces regular reports on pricedevelopments, investment, etc. The transmission system operators(via the European Network of Transmission System Operators,ENTSO) also provide information on interconnector availability andregular assessment and projections of the supply–demand position.

The Regulation on Energy Market Integrity and Transparency(REMIT) came into force in 2011, which requires all electricity andgas companies to publish any inside information that they hold. Ineffect, this means provision of data on all planned and unplannedoutages, projected return to service dates and metered productionvolumes of all power plants above a certain size.

It is expected that REMIT will be strengthened during 2013 with


130


the introduction of more specific binding guidelines on transparencyfrom the European Commission. This will apply to generators, trans-mission system operators and large consumers. The likelihood is thatthis will lead to a centralised platform for reporting information. Atpresent, companies are largely reporting inside information on theirown individual websites.

CONCLUSIONSThe main questions about power markets in Europe are well known.Where are prices and spreads going? What will the market look likein 2020 and beyond? Will there even be a market that we recognise?

The first question is difficult to answer. Prices and spreads arelow, and this is mostly due to an unexpected event: the financialcrisis and its impact on the economy and electricity demand. So,although at the time of writing there does not seem much prospect ofrecovery, we do not yet know what other unexpected events mightoccur. However, we do know that prices for the traditional baseloadproduct are likely to be continually eroded by more renewable pene-tration. Meanwhile, flexibility should become more valuable, so wemight end up in a situation where one type of traded productcontinues to experience falling prices, while prices are rising inanother segment of the market.

The market in 2020 will clearly look somewhat different. Morecomplex and bespoke products may develop, which may or may nothave the same liquidity as the traditional ones. Trading might alsocontinue to move towards the short term as it becomes more andmore difficult to take a position on how things will look beyond oneor two years. This may feed through into the relationships betweenthe market and consumers. Supply contracts to end- users based on longterm contracts may also become prohibitively expensive inview of the additional risks and uncertainties.

Will the traded market exist at all? There is clearly some risk thatthe panoply of regulatory interventions will drive liquidity out ofwholesale markets entirely. Contractual structures may then becomemore bespoke and possibly also have a high degree of regulatoryinvolvement. More integrated solutions may become more popularand this will move us away from traded outcomes.


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In this chapter, we will examine the key determining factors formetal price analysis: physical demand, supply and inventory. Wewill then explore how these three factors combined lead to priceformation, together with a short discussion of a range of other influ-ences – such as currency, speculative and investor flows orpositioning, and inflation. Across the metals complex, it should beapparent by the end of this chapter that the importance of thesevarious factors varies significantly from metal to metal. We finallyconclude with a short discussion regarding the major differencesbetween the three metal segments, with a summary of the individualfundamentals and trends in these markets.For the purposes of this chapter, we shall define the metals

markets, also known as basic materials or industrial minerals, asmined commodities that have a recognised and liquid global papertrading market that is widely used as the primary pricing mecha-nism for that commodity. The metals markets, under this definition,can be split into three areas: base metals, bulk commodities andprecious metals. However, for much of this chapter we shall refer tothe entire group as “metals”.The metal markets are, arguably, the most direct expression of

applied macro and microeconomics. The core driver of demand foralmost all metals is industrial production, on a country, regional andworld basis. However, there are micro differences for individualmetals demand and these can be very important for idiosyncraticpricing. The nature of global metal supply tends to be relatively

133

6

The Metals MarketsKamal Naqvi

Credit Suisse


stable with, typically, only modest seasonality compared to, say,agriculture. The meeting of demand and supply then is the stock andflow of inventory, which is the main underpinning for metal prices.The global metals markets are one of the longest serving

commodity trading markets. Used as a currency at various points ofhistory across theworld, themetalsmarkets are nowbest understoodas, arguably, thepurest formof global commoditymarket due to theirhomogeneity. Unlikemost agriculture and energymarkets, themetalmarkets tend tohave largely standardisedphysicalproperties andareless specific to regions or countries.Hence,metal prices tend to reflectthe interaction between global supply, demand and inventories.

INVENTORYAs they are relatively easily stored, inventories for metals tend to bemore visible and therefore quantifiable compared to other commodi-ties. The key to fundamentally driven commodity pricing is therelationship between inventories and price. For most of the metals(gold is perhaps an exception), this is a normal relationship – withdeclining inventories typically associated with upward price pres-sure. This is shown in Figure 6.1.The two key elements for pricing dynamics are:

the level of inventories, measured best in terms of how many�

days, weeks, months or years of consumption; andthe rate of change in inventory levels.�

These two factors combine to form the physical fundamental driversfor metal prices. A very low level of available inventories, such ascopper or tin (as noted in Table 6.1), will typically see high andvolatile prices as in this situation only modest changes in thesupply/demand balance are needed to produce a large change inprices. In contrast, metals with very large levels of inventories, suchas gold and silver in Table 6.1, require much larger changes in thesupply/demand balance to justify a change to price.It should be noted that the “weeks of consumption” heading in

isolation means little for relative pricing, but is shown for illustrationof relative availability of metal inventories. The price for an indi-vidual metal depends more on the relative level of inventorycompared to its own history.


134


Inventory levels are not only the primary driver for price levelsand change, but also for forward pricing, which will be discussedlater in the chapter.

DEMANDMetals demand is strongly linked to economic growth. However,while the level of global GDP is a reasonable proxy for living stan-

THE METALS MARKETS

135

Figure 6.1 Commodity prices and inventories

Source: Credit Suisse, Wood Mackenzie

0

20

40

60

80

100

120

0 2 4 6 8 10 12 14 16

Weeks of consumption

Com

mun

ity p

rice

($/t,

$/o

z, e

tc)

Table 6.1 Commodity inventories by weeks of consumption

Commodity Weeks’ of consumption(2012)

Copper 1.5Tin 2.1Lead 3.1Iron ore 6.0Thermal coal 6.0Zinc 8.0Nickel 11.0Aluminium 16.6Platinum 40.0Palladium 60.0Silver 400.0Gold 700.0


dards and can be a useful broad macroeconomic variable for theenergy and agricultural markets, it is not such a useful representa-tion for industrial materials demand, as a large share of GDP isrelated to the services sector in most developed economies. Rather,the best macroeconomic drivers of industrial materials demand areindustrial production (IP) and real fixed asset investment (FAI), asshown in Figure 6.2.There are, of course, micro differences between the metals markets

in terms of sensitivity to these broad macro variables, depending onwhich sectors and countries dominate their use (see later in thechapter), but they are relatively modest compared to the primarytrend.On a national level, for industrial materials one country has

become dominant: China. As shown in the Figures 6.3 and 6.4,Chinese demand for almost all metals has become dominant inabsolute terms and even more so as a proportion of global demandgrowth. For this reason, much of the traditional analysis of demandby country has been overwhelmed by the flows in Chinese demand,particularly as represented by Chinese trade data.

SUPPLYMetals supply originates from mined ore that is then processed intostandardised physical properties to allow for global sale. The various


136

Figure 6.2 Global industrial production growth (month-on-month trend)

Source: Credit Suisse, Thompson Reuters Datastream

-2.5%

-2.0%

-1.5%

-1.0%

-0.5%

0.0%

0.5%

1.0%

1.5%

2000 2002 2004 2006 2008 2010 2012


traded metal products, somewhat similar to the energy complex, area variety of extracted and processed minerals. Iron ore and coal areconcentrated ores and require only relatively modest processing tostandardise quality. Copper, zinc, lead, tin and nickel are refinedmetals from concentrate, while aluminium and the precious metalsrequire further elaborate processing to meet global standards.

THE METALS MARKETS

137

Figure 6.3 China is a key driver of growth in global metal demand

Source: Credit Suisse, Wood Mackenzie* 2010s average of first four years, with Credit Suisse 8% forecast for 2012 and 2013

35.0%

30.0%

25.0%

20.0%

15.0%

10.0%

5.0%

0%70s 80s 90s 00s 10s*

Figure 6.4 China dominating copper, aluminium, steel oil markets

Source: Credit Suisse BP World Statistical Yearbook, Wood Mackenzie, World Steel Association

6%

7%

8%

9%

10%

11%

12%

10%

15%

20%

25%

30%

35%

40%

45%

50%

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Steel Copper Aluminium Oil (rhs)


Scrap can also be a meaningful source of annual supply for somemetals, such as lead and the precious metals.Long lead times for new mines tend to lead to longer price cycles

for many metals. In Figure 6.5, copper mine production can be seento have grown only modestly from 2004 to 2012, despite a massiveincrease in copper prices. This is due to the lagged response of minesupply to price.The cost of supply is the other supply- side factor that supports

prices. Figure 6.6 depicts the industry cost curve for aluminium in2012, and this can be used as an indication of sustainable prices in themedium term. However, this support level is not a stationary one asmost elements of mine supply costs – such as labour, power, equip-ment and energy – are also cyclical.Using copper as an example, Figure 6.7 illustrates the drivers of

mined supply costs and also highlights the sharp escalation in costsin copper mine supply since 2005. In money- of- the- day terms, minesite cash costs have doubled, largely due to steep increases in the unitcosts of labour (direct wages), service provision (essentially a form oflabour) and consumables. Energy costs have also risen, but forcopper mines these are a smaller proportion of costs than, say,aluminium production.


138

Figure 6.5 World copper mine production has grown very slowly since the 1990s, but this could change in 2013–14


-2

0

2

4

6

8

10

10000

12000

14000

16000

18000

20000

22000

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

Mine supply (without disruption), kt Mine supply, kt Increase, % (rhs)


PRICESAs discussed earlier, the key to fundamentally driven commoditypricing is the relationship between inventories and price. The actualor estimated level of inventories, best measured in terms ofconsumption, and the expected change in inventories, known as themarket balance, are the core drivers for the price level, the volatilityof prices and the shape of the forward price curve.The metals markets tend to have long price cycles due to the long

lead times in mined supply. Figure 6.8 depicts a longterm time

THE METALS MARKETS

139

Figure 6.6 Aluminium cost curve (2012)


0 20,000 40,000

3,500

3,000

2,500

2,000

1,500

1,000

500

0

Production (kt/a)

Cas

h co

st (C

1)($

/t)

Cash cost (C1)

Figure 6.7 Copper mine costs of production: sharp rises in consumable and labour costs


0

500

1000

1500

2000

2500

3000

3500

4000

1990 1995 2000 2005 2010 2012

Services & other

Stores

Fuel

Electricity

Labour


series for base metals showing how long the price cycle tends to beand also, interestingly, that current prices for base metals are notsignificantly high in real terms. This is despite the fact that since 2002,the prices of all metals have risen significantly, with gold and ironore reaching all time highs in 2012, as shown in Figure 6.9.The rise of electronic access to commodity markets and growth in

high- speed trading technology has, in our view, changed short- termcommodity pricing dynamics – not necessarily for the better orworse, just changed. A standard technical analysis for copper, forinstance, has become a new challenge for traditional commodity


140

Figure 6.8 Average real base metal prices

Source: Credit Suisse, IMF, Bloomberg Professional Service

4.0

4.5

5.0

5.5

6.0

6.5

7.0

7.5

-8

-6

-4

-2

0

2

4

6

8

1850 1870 1890 1910 1930 1950 1970 1990 2010

Principal component Equally-weighted metals index (logs, rhs)

24 years!

19 years!

20 years!

19 years!

17 years!

23 years!

12 years

so far..!

Figure 6.9 Gold, oil, iron ore and copper remain expensive relative to history

Source: Credit Suisse, IMF, Bloomberg Professional ServiceNote: Indexed to 2002 prices

-100%

-50%

0%

50%

100%

150%

200%

250%

Aluminium Wheat Corn Zinc T. Coal Nickel Tin Lead Copper Iron Ore Brent Crude

Gold


market participants, including (perhaps even especially) specialistcommodity hedge funds. A paper on this topic (Filimonov, V., D.Bicchetti, N. Maystre and D. Sornette, 2013, “Quantification of theHigh Level of Endogeneity and of Structural Regime Shifts inCommodity Markets”, SSRN) concluded that there is evidence ofgreater price endogeneity rather than external news/factors. Othermarkets have gone through the same evolution, and the metalsmarket is no different. It does not mean the physical commodityfundamentals have become irrelevant – indeed, return dispersionsuggests the opposite – it simply means that there are a few morevariables added to the market.Macro factors have latterly become a more important driver of, or

rationalisation for, metal prices. The two factors that have enduredthe cycles as being an influence on metals prices, or being influencedby metal prices, are currencies and inflation.Figure 6.10 shows a long- run series of copper prices in a variety of

currencies; it is notable that for key cycles the price experience candiverge significantly. This is relevant to metal price formation, as aweak domestic currency is a positive for producers and a negativefor consumers, with the oppositive also being the case.The link between metal prices and inflation is more muted for

most metals, with the clear exception for gold. For many reasons,gold is an exception to the price formation basis for the majority ofthe metals markets. It has often been seen as a longterm preserver of

THE METALS MARKETS

141

Figure 6.10 Currency appreciation significantly affected copper prices


0

100

200

300

400

500

600

700

1971 1981 1991 2001 2011

USD AUD JPY


wealth and, therefore, a hedge against inflation. Certainly it is truethat, at times, gold prices can be highly correlated with inflationexpectations (see Figures 6.11 and 6.12).The bulk of this chapter has discussed spot or front price forma-

tion, which is the prime focus for metals market analysis as itdetermines the demand for physical metal for immediate delivery –


142

Figure 6.11 Gold versus five-year TIPS (since 2007)


-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0 $250

$500

$750

$1,000

$1,250

$1,500

$1,750

$2,000

Jan-07 Jan-08 Jan-09 Jan-10 Jan-11 Jan-12 Jan-13

%

Gold, $/oz (LHS)

US 5 year TIPS, % (scale inverted)

Figure 6.12 Gold versus five-year TIPS


-2.0

-1.0

0.0

1.0

2.0

3.0

4.0 $250

$500

$750

$1,000

$1,250

$1,500

$1,750

$2,000

Jan-09 Jan-10 Jan-11 Jan-12 Jan-13

Gold, $/oz (LHS)

US 5 year TIPS, % (scale inverted)


from which all market price points are determined. However, whentrading the metals markets, much discussion revolves around thepoint of the forward price curve that needs to be traded and to whatdegree that point does or does not reflect the expectations already “priced- in” to the market.For example, in Figure 6.13, the iron ore market curve changed

significantly in both shape and level over several months reflectinghow changes in market expectations, the demand from physicalconsumers for immediate delivery of metal and the flow of businessacross the various points of the curve can shift and reshape theforward prices.Commodities with relatively low levels of available inventory

tend to be in backwardation, with nearer- dated futures contracts athigher prices than the futures contracts further out the curve,reflecting the premium that the consumer is willing to pay to securemetal. If the contrary is true, and the market is perceived to be inample or oversupply, then the futures curve tends to be upwardsloping, and the market is said to be in contango.Metals tend to have a somewhat more consistent contango

compared to energy due to the relative ease of storing metals. Gold isthe extreme example of this, with storage of gold being a tiny fractionof its cost and, therefore, gold tends to trade in perpetual contango

THE METALS MARKETS

143

Figure 6.13 Iron ore market curve

Source: Credit Suisse, Bloomberg

125

120

115

110

105

USD

/metric tonne

Dec

201

2

Feb

2013

May

201

3

Jul 2

013

Oct

201

3

Dec

201

3

Mar

201

4

May

201

4

Aug

201

4

Oct

201

4

Dec

201

4

Iron ore 62% China (TSI) swaps : NYM : last price : 6/4/2013Iron ore 62% China (TSI) swaps : NYM : last price : 12/5/2012Iron ore 62% China (TSI) swaps : NYM : last price : 5/3/2013


with forward prices driven by the US interest rates minus the storageor leasing rate. For other metals, such as aluminium, there is also atendency towards contango as inventory tends to be built and heldfor large consumers, such as car manufacturers. Operators with theirown storage facilities and/or access to cheaper finance can some-times buy and hold physical metal against an offsetting paperposition for a (largely) risk- free return.

BASE METALSThe base metals, also known as industrial metals or non- ferrousmetals, are aluminium, copper, zinc, lead, nickel and tin. The world’sbenchmark contracts are listed on the London Metal Exchange(LME). However, other key contacts include the Comex Copper andShanghai Futures Exchange (SHFE) copper contracts.The LME has an idiosyncratic trading system. The most active

daily price is known as the “three months price”, literally a trading


144

Figure 6.14 Structure of LME futures

Source: London Metal Exchange

Cash 3 months

6 months 12 15 27 63 123

Daily prompt dates Weekly prompt dates

Monthly prompts to 12, 15, 27, 63 or 123 months

TinPP, LLDPE& Steel

LME Mini Aluminium(alloy) &NASAAC

Aluminium(Primarycopper)

Lead,nickel& zinc


date which is three months forward of the current day, subject to itbeing an official trading day (ie, not a UK holiday). Future points onthe forward curve are then traded as a spread to the three monthsprice. Figure 6.14 shows the structure of LME futures dates, daily outto three months, weekly to six months and monthly out to 10 yearsfor some products.The LME also remains one of the few remaining open outcry

trading markets where the official daily prices are set by the clearingprice found across the floor, as it is known. Commercial players(mining companies, industrial users, physical merchants, end- consumers), banks, brokers, hedge funds, and institutional investorsare all active participants.Most of the discussion in the chapter so far applies to the base

metals markets in terms of market analysis and price formation. Weshall now contrast the bulk commodities and precious metalsmarkets.We provide a chart and table summary of the main features of the

base metals markets in Figures 6.15–6.18 found at the end of thischapter.

BULK COMMODITIESFor the purpose of this chapter, we limit our definition of the bulkcommodities to the mined materials of iron ore and thermal coal(note that others may include steel and freight within the definition).The bulk commodities are so- called due to the sheer physical

volume of production. Both iron ore and coal production are morethan the combined output of the six LME metals. However, unlikethese metals, the majority of global production of both iron ore andthermal coal is used domestically, with the balance often beingshipped long distances to consumers. Both materials have a domi-nant usage, with iron ore being the key ingredient for steel andthermal coal for energy.Historically, both iron ore and thermal coal were supplied on a

contractual basis (typically, annually), based on periodic negotia-tions between producers and consumers. However, since the early2000s, both markets have moved away from this structure towards aphysical spot market supported by an over- the- counter (OTC) paperforward market. Latterly, clearing of OTC swaps and even futuresexchange markets have emerged. The OTC markets are priced

THE METALS MARKETS

145


against industry benchmark indexes that are based upon spot phys-ical deliveries.Due to the magnitude of the flow and the relative high costs of

freight as a percentage of the final price, both iron ore and thermalcoal can be quoted by including the cost of freight to the consumerport price (CFR). This is typical for iron ore, or from the port of theproducing country before the freight on board (FOB) price, whichtends to be more common for thermal coal.

PRECIOUS METALSPrecious metals, particularly gold, are among the most activelytraded commodity markets, with gold having the widest number oftrading participants of any commodity, including oil. The preciousmetals that are actively traded are gold, silver, platinum and palla-dium. All of these have liquid OTC and exchange- traded markets.Unlike other commodities, they also have a very large physicallytraded wholesale market, of which London is generally seen as theglobal centre, although there is a wide range of important localmarkets across the world.The term “precious” relates partly to their relative scarcity and

partly as they are often used as a store of value rather than for directconsumption – although both gold and silver are commonly used asminiature decorations on top of Indian sweets, and hence aregenuinely consumed! The precious metals markets are also distinc-tive in having traditional banking elements – that is, gold can bedeposited, on an allocated or unallocated basis, and therefore alsoborrowed or leased, much like classic money.The precious metals, and particularly gold, have probably more

trading centres than any other commodity, despite being globallyhomogenous. As mentioned, while the global central point for theprecious metals market remains London, there are a wide range ofvery important physical gold markets, including Zurich, Mumbaiand Dubai. However, increased market share and overall liquiditylies in the listed exchanges, in particular the New York MercantileExchange (Nymex), the Shanghai Futures Exchange (SHFE), theMulti Commodity Exchange (MCX), the Tokyo CommodityExchange (TOCOM) and the Dubai Mercantile Exchange (DME).Unlike other commodities, a large fraction of all the precious

metals mined in history still exist and can be considered, at least


146


theoretically, to be above- ground inventory. This is not so much thecase in silver, and even less in platinum and palladium, which is whythey are more similar to the base metals markets.Furthermore, precious metals, and particularly gold, used as a

central bank asset in bar form means that there is also an active andliquid lease or borrowing market, which reduces the scope for phys-ical scarcity to influence the price – although occasionally certainbars or coins may trade at a higher premium due to their individualscarcity. Instead, market sentiment tends to dominate preciousmetals prices and this can be influenced by many differing elements,of which physical supply and demand is just one; others includeinflation, currencies, geopolitics and uncertainty or risk more gener-ally. The jewellery sector is important for all the precious metalsmarkets, while industrial usage is also important for silver, platinumand palladium.Physical investor demand is also a key factor, with increased

accessibility through exchange- traded funds having become a majormarket influence and now also a major inventory.

CONCLUSIONSThe chapter was primarily designed to provide an initial guide toanalysing the basic material of metal markets. It should hopefullyhave become clear that while there are overarching similarities to thegroup, specific analysis requires a quite idiosyncratic approach tonot only each market’s supply, demand and inventory, but also to itsrelationship to other commodities, particularly other metals, as wellas wider macro relationships. In reality, each individual marketcould have an entire book dedicated to its analysis.The global metals markets are at a pivotal time. Since the early

2000s, prices have often been gripped in the so- called “super- cycle”.Definitions vary on what “super- cycle” means, but for some it ishigher than average real or nominal prices. Under such a definition,we think this will continue. However, for most it means synchronousrising metal prices, and this we do not think will occur. The true super- cycle, from 2002 to 2007, was buoyed by a range of synchro-nised, positive physical and financial factors that combined to driveprices to historical nominal highs.In summary, the physical factors driving the metals markets are

shown below.

THE METALS MARKETS

147


Demand surge:�

Chinese;�

emerging markets; and�

moderate growth across the rest of the world.�

Supply constraints and costs explosion:�

falling ore grades;�

labour shortages and disruption;�

technical problems (mines and refineries);�

infrastructure bottlenecks, delays and disruptions;�

resource nationalisation;�

environmental and social legislation;�

reduced availability of scrap; and�

shift to underground mining.�

Inventory declines:�

falling visible exchange inventories; and�

off- exchange inventories either falling or not being made�

available.Investor buying:�

Investor buying:�

index inflows;�

structured product buying; and�

exchange- traded product demand (ETFs, etc).�

Hedge fund buying:�

commodity specialist fund buying on constructive S&D;�

macro hedge funds buying on a China play and/or US dollar�

weakness; andtechnical traders buying due to signals and momentum.�

Corporate flows:�

consumer forward buying due to concerns over price rises;�

andproducer reductions of existing hedge books – ie, net buying.�

Looking forward, many of these factors are, or are likely to be,much less positive; indeed, they may become negative influences onprice over the next few years. Generally, we still expect nominal andreal prices to hold in a higher range compared to history, but we alsoexpect to see greater variation in individual metals. The winners arelikely to be those where we see little likelihood of sustained increasesin supply – such as zinc, lead, platinum, palladium and copper.


148


THE METALS MARKETS

149

Figure 6.15 Industrial metals: aluminium

Source: Credit Suisse, Wood Mackenzie, International Aluminium Institute

18%

27%

19%

24%

3% 3% 2% 2% 1%

45%

17% 15% 14%

3% 2% 2% 1% 1%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

China Europe Asia North America

Latin America

Russia Middle East Africa Oceania

2003 2012

Demand by country

Cost curve

Demand by sector

Construction, 19%

Transport, 32%

Electrical, 15%

Packaging, 13%

Consumer goods, 9%

Machinery & equipment, 8%

Other, 5%

3,000

2,500

2,000

1,500

1,000

US$

/t

90% minuspremium:US$1,812

US$280/t premium added

Current LMECash: US$1,893

80%

92%

90%:US$2,072



150

Figure 6.15 Industrial metals: aluminium (cont.)

20% 20%

14%

18%

5%

8%

4%

8%

5%

46%

10% 10% 9% 8%

5% 4% 4% 3%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

China North America

Russia Europe Middle East Oceania Asia Latin America

Africa

2003 2012

Supply by country

Costs breakdown

Integrated aluminium-making process flow chart

Alumina, 31%

Carbon & bath, 14% Energy, 39%

Labour, 6%

Other, 10%

Stage 1 –refining

Stage 2 –smelting

Recycling

Processing

Aluminium smelting

Alumina refining

Bauxite mining

Extrusion

Rolling

Casting

Source: Credit Suisse, Wood Mackenzie, International Aluminium Institute


THE METALS MARKETS

151

Figure 6.16 Industrial metals: copper

Demand by country

Cost curve

Demand by sector

Electrical/electronics, 34%

Construction, 30%

Transportation, 14%

Industrial machinery, 13%

Consumer products, 9%

10000

9000

8000

7000

6000

5000

4000

3000

2000

1000

00 5,000 10,000 15,000

Current price = US$7,370/t

90th percentile = US$5,335/t

Source: Credit Suisse, Wood Mackenzie, Teck

0

5

10

15

20

25

30

35

40

BrazilRussiaTaiwanItalyIndiaSouthKorea

JapanGermanyUSAChina

% o

f glo

bal c

oppe

r de

man

d

12.2

37.7

19.8

9.4 8.66.6

8.9

5.1 5.7 4.51.9

3.2 4.43.0 4.1 2.9

1.3 1.9 2.2 1.9



152

Figure 6.16 Industrial metals: copper (cont.)

Supply by country

Costs breakdown

CESL copper process flowchart

44%

15%

4% 4%

9% 7% 6%

9%

1%

43%

13%

9% 9% 7% 6% 6% 5%

2%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

Latin America

North America

China Africa Russia Oceania Europe Asia Middle East

2003 2012

Labour, 25%

Electricity, 13%

Fuel, 8% Stores, 32%

Services/other, 23%

Evaporator Condensate

Oxygen

Copperconcentrate

Washwater

Washwater Filtration

Residue washing(counter current

decantation)

Pressure oxidation

Atmospheric leach(optional)

Leach residue(to gold plant)

Gypsum(to tailings)

Thickener Pregnant leach solution (PLS)

Limestone

NeutralisationTo pressureoxidation

Filtration

Solvent extraction

Electrowinning

Copper cathode(to market)

Raffinate

Source: Credit Suisse, Wood Mackenzie, Teck


THE METALS MARKETS

153

Figure 6.17 Industrial metals: nickel

Demand by country

Cost curve

Demand and industrial production

USA10%

China33%

Japan11%

Korea6%

Taiwan5%

Germany7%

Other28%

25,000

20,000

15,000

10,000

5,000

0

-5,000

-10,000

US$

/tonn

e

LME cashPrice: US$18,917

Median:US$10,136

Ramp-ups, NPIand tocantins92.7%

90%:US$15,945

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

15%

10%

5%

0%

-5%

-10%

-15%

16%

12%

8%

4%

0%

-4%

-8%

-12%

IP – Mature economies (LHS)IP – Developing economies (LHS)Nickel consumption (RHS)

Source: Credit Suisse, Wood Mackenzie, Nickel Institute



154

Others (incl. chemicals) 6%

Other steel alloys (incl. castings) 10%

Stainless steels 61%

Non-ferrous alloys 12%

Electroplating 11%

Other 7%

Engineering 24%

Metal goods 16%

Electro & electronic 15%

Transportation 16%

Building & construction 11%

Tubular products 10%

Figure 6.17 Industrial metals: nickel (cont.)

First use consumption

Nickel production

Demand by application

1400

1200

1000

800

600

400

200

01999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

SulphidesLaterites

Source: Credit Suisse, Wood Mackenzie, Nickel Institute


THE METALS MARKETS

155

China41%

Europe19%

North America10%

Asia (excl Japan& China)

18%

Japan 4%

Latin America 5% Africa 1%Oceania 2%

Figure 6.18 Industrial metals: zinc

Demand by country

Cost curve

Demand and industrial production10%

8%

6%

4%

2%

0%

-2%

-4%

-6%

-8%

-10%1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

Global IP growth (YOY%) Est. refined zinc consumption growth (YOY%)

2,500

2,000

1,500

1,000

500

0

-500

-1,000

US$

/tonn

e

US$120/tonne premium

LME cashprice: US$1,847

90%:US$1,524

99.1%

95.9%

Median:US$965




156

Figure 6.18 Industrial metals: zinc (cont.)

First use consumption

Supply by country

Demand by application

China30%

L. America22%

Australia12%

Galvanising57%

Construction49%

Infrastructure13%

Consumerproducts

8%Industrialmachinery

7%

Transport23%

Miscellaneous4%

Rolled &extrudedproducts

7%

Oxides &chemicals

8%

Decastingalloys11%

N. America12%

Other Asia7%

Europe6%

India6%

Russian Fed.2%



THE METALS MARKETS

157

Figure 6.19 Bulk commodities: iron ore

Source: Credit Suisse, Wood Mackenzie, Company data

Spot Price

CS price forecast

Consensus

0

20

40

60

80

100

120

140

0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600

US$

per

dry

met

ric

tonn

e

Million tonnes per annum

BHP.AX CLF FMG.AX KIOJ.J RIO.AX VALE.N China Other Reported Cash Cost (FOB)

All-In Cash Cost (FOB)

All-In 62% IODEX equiv (CFR)

210

190

170

150

130

110

90

70

502009 2010 2011 2012 2013 2014

US$

/t

Iron ore (62% Fe CFR Tianjin spot) Quarterly avg forecasts



158

Figure 6.20 Bulk commodities: coal

Source: Credit Suisse, Wood Mackenzie, World Coal Institute

0

5

10

15

20

25

30

35

40

2011 2012 2013 2014 2015

Australia RoW Indonesia

-10

0

10

20

30

40

50

2011 2012 2013 2014 2015

India RoW China

Major contributors to seaborne demand

Types of coal

Major contributors to seaborne supply

HIGH MOISTURE CONTENT OF COAL

HIGHCARBON/ENERGY CONTENT OF COAL%

OF

WO

RLD

RES

ERV

ESU

SES

Low rank coals47%

Hard coal53%

Sub-bituminous30%

Bituminous52%

Anthracite-1%

Lignite17%

ThermalSteam coal

MetallurgicalCoking coal

Largely powergeneration

Manufactureof iron

and steel

Domestic/industrialincluding

smokeless fuel

Power generationCement manufacture

Industrial uses

Power generationCement manufacture

Industrial uses


THE METALS MARKETS

159

Figure 6.21 Precious metals: gold

Source: Credit Suisse, Wood Mackenzie, GFMS, Thomson Reuters, World Gold Council, Bloomberg Professional Service

Demand by sector

Cost curve

Above ground stocks

200

400

600

800

1000

1200

1400

1600

1800

1980 1985 1990 1995 2000 2005 2010

$/oz

Au

C3 costs (real)

Average gold price (real)

0

10,000

20,000

30,000

40,000

50,000

60,000

2000 2002 2004 2006 2008 2010 2012

Tonn

es

Cumulative supply used as investment

Cumulative supply used in jewelry

Cumulative supply used in industry/dental

Annual mine supply

Near to

market

Far fromm

arket

Jewellery57%

Bar coin retailinvestment

26%

Dental1%

Industrial11%



160

Figure 6.21 Precious metals: gold (cont.)

Mine supply by country

Central bank reserves

Supply by sector

Mineproduction

60%

Old goldscrap39%

Official sectorsales, 1%

8,13

4 3,

391

2,81

4 2,

817

2,45

2 2,

435

2,30

3 1,

054

1,04

0 99

6 76

5 61

3 55

8 50

2 42

4 38

3 36

6 32

3 44

5 31

0 28

7 28

2 28

0 22

8 80

3 3,

706

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

Uni

ted

Ger

man

y

IMF

2012

A M

ine

Italy

Fran

ce

2012

A

Chi

na

Switz

erla

nd

Rus

sia

Japa

n

Net

herl

ands

Indi

a

ECB

Taiw

an

Port

ugal

Ven

ezue

la

Saud

i Ara

bia

Turk

ey

UK

Leba

non

Spai

n

Aus

tria

Bel

gium

Aus

tral

ia

Can

ada

Oth

er

Tonn

es


THE METALS MARKETS

161

Figure 6.22 Precious metals: silver

Mine supply sources

Supply trends

Supply sources

Scrap20%

Government sales3%

Mineproduction

77%

Zinc & lead37%

Copper23%

Primary silver28%

Gold 11%

Others 1%

1,200

1,000

800

600

400

200

0

1,200

1,000

800

600

400

200

0

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

Mine productionProducer hedging1.0% growth rate

Net official sector salesImplied net dis-investment2.5% growth rate

Silver scrapZero growth5.0% growth rate

Source: Credit Suisse, GFMS, Silver Institute



162

Figure 6.22 Precious metals: silver (cont.)

Demand by sector

ETP demand

Demand trends

1,000

900

800

700

600

500

400

300

200

100

01999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Industrial applications Photography Jewellery & silverware Coins & medals

Coins & medals8%

Industrial applications53%

Photography13%

Jewellery &silverware

26%

500

450

400

350

300

250

200

150

100

50

0

30

25

20

15

10

5

0

Apr-06

Jun-0

6

Aug-0

6

Oct-06

Dec-0

6

Feb-

07

Apr-07

Jun-0

7

Aug-0

7

Oct-07

Dec-0

7

Feb-

08

Apr-08

Jun-0

8

Aug-0

8

Oct-08

Dec-0

8

Feb-

09

Apr-09

Jun-0

9

Aug-0

9

Oct-09

Dec-0

9

Feb-

10

Apr-10

Jun-1

0

Aug-1

0

Oct-10

Dec-1

0

Mln

oun

ces

iSharesETF Securities

ZKB physical silverSilver price (US$/oz)

Source: Credit Suisse, GFMS, Silver Institute


THE METALS MARKETS

163

Figure 6.23 Precious metals: platinum

Palladium mine supply

Platinum demand by sector

Platinum mine supply

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

'000

oun

ces

Others Zimbabwe North America Russia South Africa

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

'000

oun

ces

Others Zimbabwe North America Russia South Africa

-1,000

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

'000

oun

ces

Other Petroleum Medical & biomedical

Glass Electrical Chemical

Jewellery Autocatalyst Investment

Source: Credit Suisse, Johnson Matthey



164

Figure 6.23 Precious metals: palladium

Platinum ETF demand

Palladium ETF demand

Palladium demand by sector

-1,000

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

'000

oun

ces

Other Chemical Jewellery Dental Electrical Autocatalyst Investment

$500

$750

$1,000

$1,250

$1,500

$1,750

$2,000

$2,250

0

250

500

750

1,000

1,250

1,500

1,750

2,000

2,250

Oct

-09

Jan-

10

Apr

-10

Jul-

10

Oct

-10

Jan-

11

Apr

-11

Jul-

11

Oct

-11

Jan-

12

Apr

-12

Jul-

12

Oct

-12

Jan-

13

Apr

-13

Thou

sand

s oz

Plat. Ldn Bskt Ldn Plat. ZKB Pt other

Plat. US Plat. Swiss ABSA Plat, spot

$100

$200

$300

$400

$500

$600

$700

$800

$900

0

500

1,000

1,500

2,000

2,500

Jan-

09

Apr

-09

Jul-

09

Oct

-09

Jan-

10

Apr

-10

Jul-

10

Oct

-10

Jan-

11

Apr

-11

Jul-

11

Oct

-11

Jan-

12

Apr

-12

Jul-

12

Oct

-12

Jan-

13

Apr

-13

Thou

sand

s oz

Pall. Ldn Bskt Ldn Pall. ZKB Pd Other

Pall. US Pall. Swiss Pall, spot


Grains and oilseeds were the first commodities, the staple of our dietand the basic building blocks for meat and fish (through aquaculture).In developed economies, food represents some 10% of GDP, higher indeveloping economies. Around 20% of people around the worldreceive government- subsidised food. This chapter will examine thesecrops for each of the major producers and consumers around theworld, analysing how the meat and fish protein markets impactgrains, and the world’s ability to rotate and adjust crop plantings inthe face of a changing demand profile. Likely trends are also noted.

Once the domain of the big grain companies, these commoditieshave been a major asset class for investors since the early 2000s, andthis chapter will take a bottom- up approach to analysing the mostrelevant information for the various investment themes and their crit-ical drivers for the years ahead. The traditional power players in theagricultural markets, both originators and exporters, are the US, theEU, Brazil and Argentina, and this dominance has been dramaticallyaffected by the increasing importance in price formation of non- traditional spheres of influence. Investment themes here have beengreatly influenced by a number of factors, such as the emergence ofChina as the world’s largest grain economy – while US grainconsumption as ethanol has made it a significantly less importantplayer for global grains – biofuels in general, urbanisation and itsattendant social changes, dramatic changes in food consumption andrapidly changing agricultural and environmental policy around theglobe.

Surprises that have led to a tightness in these markets include the

165

7

Grains and OilseedsDavid Stack

Agrimax


disappointment of genetic modification (GM) to deliver on itspromise of dramatically improving yields, the slow growth of supplyversus the expected marginal supply curve and the high rate of expan-sion of China’s soya demand. There have been the usual droughts,food scares and a heightened sensitivity to food price inflation, as wellas the global economic crisis and Arab Spring that affected allinvestors and markets. However, a number of important players haveentered the agricultural markets. Land investment has become amainstream activity, with some spectacular successes and many fail-ures. The markets evolved from talking about speculation and landgrabs to dividing the new investors into multiple investment styles (asmany as 10, see Appendix 7.1). Some have embraced the traditional,fundamental style of the agricultural markets, while others intro-duced new methodologies. Finally, the Dalian Commodity Exchangebecame the second- largest futures market in the world, foreverchanging the role and dominance of the Chicago Board of Trade(CBOT). Uniquely, we will examine non- US grain and oilseedeconomies; the US is already data- rich and over- analysed, at a timewhen its importance in the global grains markets is declining. We lookat the evolution of the Chinese oilseed industry to a staggering 125million metric tonnes (MMT), far bigger than the US.

This chapter will present an in- depth look at key developmentsaround the world since the early 1990s, in particular:

the soybean rally of 2003, a surprise for everyone;�

the wheat rally of 2007, from sizzling problems to market explo-�

sion; andthe maize rally of the 2000s, and the US corn supply/demand net�

of ethanol (EtOH).

Finally, we will draw from past market developments to define themain issues and opportunities for the forward- looking investor.

FEEDGRAINS, FOODGRAINS AND VEGETABLE PROTEINS:THREE MARKETS, THEIR INDIVIDUAL ECONOMIES ANDINTERDEPENDENCEThe dynamic of these markets lies within the fundamentals, and thisremains the key to understanding them. By first examining thetrends for each of these three markets, and their major producers andconsumers in terms of both the switchable and non- switchable


166


demand and economic drivers, we can then progress to the role ofrotation in their convergence.

The world harvests 2,525 MMT of major grains (corn, sorghum,barley, wheat, rice, soybeans, rapeseed and sunseed – See Table 7.1)on 800 million hectares (MHas) of land. Across the major producingcountries, the land is devoted as follows: 79% grains (27% maize, 6%sorghum, 8% barley, 35% wheat and 25% rice) and 21% softseeds(64% soybeans, 21% rapeseed and 15% sunseed). The average yield is3.20 MT, of which grains average 3.40 MT and softseeds average 2.20MT. By crop, the global averages are maize 5.00, sorghum 1.50,barley 2.63, wheat 3.00, rice 2.88, soybeans 2.50, rapeseed 1.75 andsunseed 1.50. The gross production tonnages provide the basevolume for each local grain economy, which subsequentlyconsumes, exports or stores any excess to those two basic needs. Weneed to understand the local economy drivers and also the exportavailabilities. These exportable volumes, and the extent to whichthey are needed in other parts of the world, drive the price as we seeit on the futures markets and through the various cash or physicalprices the commercial world has access to.

AYP is the common industry abbreviation for area in terms of MHa,yield in metric tonnes per hectare (Mt/HA) and production (theproduct of A and Y). In this section, we will discuss the current AYPfor each major grain, where appropriate the whole grain economy forthe major grains, the evolution of the major producer economies sincethe early 1990s and their changing role in price formation, as well assome thoughts on how this may evolve. The following is a summaryof the total of 2,525 MMT of grain production:

850 MMT comes from the nine major maize producers (see Table�

7.3a) – US, Argentina, Brazil, Mexico, EU-27 (member states ofthe European Union), the Commonwealth of Independent States(CIS)/Former Soviet Union (FSU), Republic of South Africa(RSA), Thailand and China;60 MMT of sorghum has three major producers (see Table 7.3c) –�

US, Argentina and Australia;130 MMT of barley has three major producers (see Table 7.3b) –�

Canada EU-27 and CIS/FSU;670 MMT of wheat comes from eleven major producers (see�

Table 7.6) – US, Canada, Argentina, Brazil, EU-27, Morocco,CIS/FSU, Turkey, China, India and Australia; and

GRAINS AND OILSEEDS

167


460 MMT of rice comes from four major producers (see Table 7.7)�

– Brazil, Thailand, China and India.

Of the soft oilseed total of 350 MMT:

250 MMTof soybeans are from four major producers (see Table�

7.9a) – US, Argentina, Brazil and Paraguay;60 MMT of rapeseed are from four major producers (see Table�

7.9b) – Canada, EU-27, China and India; and40 MMT of sunseed are from three major producers (see Table�

7.9c) – Canada, EU-27 and CIS/FSU.

Hard oils (palm oil production) are dominated by Malaysia andIndonesia at 38MMT (see Table 7.2).

Each major producer has a substantial grain economy for eachgrain, and many interact since feedgrains are often combined withoilseed meals – for example, to make complete animal diets. Each ofthese economies is different and evolving. There are few clean lines,with many feedgrains also being foodgrains, and feedgrains being amajor feedstock for biofuels (primarily ethanol, but also sugar cane),as is vegetable oil (primarily biodiesel).

In Table 7.1 and subsequent tables we compare the last three- yearaverage of 2010, 2011 and 2012 (2010–12) to the previous three- yearaverages of five years ago (2005–07), 10 years ago (2000–02), 15 yearsago (1995–97) and 20 years ago (1990–92), to avoid blips in individualyears. We see from this summary that, although the grains areaappears remarkably stable over time (3% 20-year growth), theoilseeds area has expanded by more than 75%. For combined grainsand oilseeds, the 20-year yield growth has been 24% on an overallhectare expansion of 13%, leading to a production increase of 40%.For total arable land, the last five- year’s production growth cameevenly from area (5%) and yield (6%).

Additionally, we must remember that in this period the US tookaround 160,000 km2 or 18.1 MHa (equal to 40 million acres, MAc) outof production through its Conservation Reserve Program (CRP).Also, in this period the EU ran its Cereal Set-aside programme. Set- aside became compulsory in 1992, primarily as a means of reducingthe “grain mountain” as part of the Common Agricultural Policy. Itwas originally set at 15%, before being reduced to 10% in 1996 andthen abandoned in September 2007.


168


GRAIN

SAND

OILSEED

S

169

Table 7.1 Grain and oilseed 20-year AYP progression – comparing three- year averages of 2010–12 (absolute values) and percentagegrowth from 2005–07 (5-year growth), 2000–02 (10-year growth), 1995–97 (15-year growth) and 1990–92 (20-year growth); MHa,Mt/Ha and MMT

Crop Last three- year average (2010–12) 2005–07 2000–02 1995–97 1990–92

Area A Yield Y Prdn P AYP % growth AYP % growth AYP % growth AYP % growth

Grains 635 3.400 2170* 2% 8% 10% 6% 16% 23% 2% 23% 25% 3% 28% 31%**Oilseeds 164 2.200 355 15% 3% 18% 34% 8% 45% 52% 20% 82% 78% 27% 125%Total arable 799 3.200 2525 5% 6% 11% 11% 13% 26% 9% 20% 31% 13% 24% 40%

Source: Adapted from Informa; * 635/3.40/2,170 means grains area is 635 MHa, world average yield is 3.40 Mt/HA and world production is 2,150MMT; ** 3%/28%/31% means grains area has grown 3% in 20 years, yield has grown 28% and production by 31%

07 Chapter C

IT_C

omm


age 169

FEEDGRAINSThere are three major feedgrains in the world – corn, sorghum andbarley – the production of which amounts to 1,045 MMT. However,we must add significant volumes of feed wheat consumed in China(10–12 MMT), the EU (49–57 MMT in 2007–12), Russia (13 MMT) and


170

Table 7.2 Major exporters and major importers, by grain or vegetable protein

Corn/Maize Wheat Soybeans/Meal/Oil Rapeseed Palm Oil100 MMT 130 MMT 93/55/8 MMT 11MMT 38 MMT 70%

of the 54 MMTof global vegoil

Top 10 Exporters1 US US US/Arg/Arg Canada Indonesia (19.0)2 Argentina Australia Brz/Brz/Brz Australia Malaysia (18.7)3 Ukraine Canada Arg/US/US Ukraine4 Brazil EU-27 Paraguay/India–5 India Russia Canada/China/–6 Russia Argentina7 RSA India8 Paraguay Ukraine9 Canada Kazakhstan

10 EU-27 Turkey

Corn/Maize Wheat Soybeans/Meal/Oil Rapeseed Palm Oil

Top 10 Importers1 US EtOH Egypt China/EU/China EU-27 India2 Japan Brazil EU27/Indonesia/

India Japan China3 EU-27 Indonesia Mexico/Vietnam/

Iran China EU274 Mexico Japan Taiwan/Thailand/

Bangladesh Mexico Pakistan5 South Korea Algeria Japan/Japan/

Venezuela US Singapore6 Egypt South Korea Thailand/Philipp/

Peru Canada Egypt7 Iran Mexico Indonesia/Iran/

Algeria US8 Taiwan Iraq Egypt/South Korea/

Egypt Bangladesh9 Colombia Morocco US/Mexico/South

Korea CIS/FSU10 Algeria Nigeria/Philipp. South Korea/Canada

& Colombia/Morocco & RSA Iran, Vietnam

& Japan


India (3–4 MMT), a total of 80 MMT. It is important to remember thatsignificant quantities of feed wheat produced globally is fed toanimals – the broad consensus says this is about 17% of global wheatproduction, or 115 MMT.

The world harvests 855 MMT of maize annually, whose majoreconomies are the US (300), Argentina (25), Brazil (65), Mexico (20),France (15), EU-27 (60), CIS/FSU (30), RSA (10), Thailand (5) andChina (195). Grain sorghum production of 60 MMT, widely distrib-uted around the world, has major production in the US (7),Argentina (5) and Australia (2). For both of these crops, there is alsosignificant non- grain production as feed, used as “silage” – bestdescribed as a whole, above- ground crop, whose stems, leaves andgrain ear are pickled in vinegar (eg, formic acid) to preserve it, beforeit is stored and fed to livestock over the following winter. Barleytotals 130 MMT, of which Canada (10), EU-27 (55) and CIS/FSU (25)are the major economies. Note that, at 130 MMT, barley production isgreater than China’s wheat production (second only to EU wheatproduction) and twice that of Brazil’s corn production (the world’s third- largest corn producer). Both barley and sorghum are in declinein terms of devoted area and the world barley and feed-wheatmarkets are the same size.

The major feedgrains are starch or carbohydrate producing andconsumed by animals, hence the feed designation. They also haveconsiderable industrial use. We can divide the animal kingdom intotwo stomach types: monogastric and ruminant. We humans aremonogastric, having “one simple stomach”, as are pigs andchickens, while cattle are ruminants, having a “rumen”. The rumencan be thought of as a vat, capable of stewing and digesting highlyfibrous food, such as grass and leaves, which contain carbohydratesbound by lignin, a complex fibre. Feeds such as potatoes requireboiling to break down their complex carbohydrate structure tomake them easily digestible for monogastrics. Grains are simplyprocessed by grinding to break down the husk or outer covering,rendering them easily digestible to a ruminant, while full millingand husk removal makes them also easily digestible by mono-gastrics. As the reader will be well aware, there is an ongoingconflict between ease of digestibility and the many essential nutri-ents found in the husk – wholegrain bread being the classiccompromise for humans.

GRAINS AND OILSEEDS

171


COMMODITYIN

VESTING

AND

TRADIN

G

172

Table 7.3a Maize 5-, 10-, 15- and 20-year AYP progression (comparing 2010–12 with 2005–07, 2000–02, 1995–97 and 1990–92).After each country name the % of world area devoted to this crop in 2012 and 1992 are given

Last 3 year average 2010–2012 2005–2007 2000–2002 1995–1997 1990–1992 % Of worldArea A Yield Y Prdn P AYP % growth AYP % growth AYP % growth AYP % growth yield

Maize / CornUS 35% 21% 34.1 8.834 301 9% –6% 2% 20% 5% 26% 20% 15% 38% 22% 18% 43% 178%Argentina 3% 2% 3.7 6.625 24 28% –1% 26% 44% 12% 62% 19% 35% 59% 63% 59% 158% 133%Brazil 8% 10% 14.4 4.526 66 4% 25% 30% 15% 41% 62% 11% 79% 100% 9% 114% 133% 90%Mexico 2% 5% 6.5 3.087 20 –8% 1% –8% –11% 18% 5% –16% 34% 13% –7% 38% 27% 63%France 2% 1% 1.6 9.347 15 6% 4% 10% –13% 5% –8% –7% 12% 3% –8% 32% 21% 188%EU 27 7% 3% 8.6 6.870 59 –1% 11% 9% –6% 14% 7% 24% 0% 35% 97% –3% 89% 138%CIS/FSU 3% 2% 5.9 4.734 28 58% 31% 110% 120% 66% 269% 130% 64% 271% 108% 51% 216% 95%South Africa 1% 3% 3.1 3.953 12 13% 20% 34% –11% 48% 33% –18% 61% 32% –8% 82% 68% 80%Thailand 1% 1% 1.0 4.248 4 –1% 13% 12% –13% 11% –3% –11% 28% 15% –22% 55% 20% 85%China 23% 16% 33.3 5.779 193 19% 10% 30% 39% 22% 69% 41% 19% 68% 56% 27% 99% 115%World total 100% 100% 168.6 5.061 853 11% 5% 16% 23% 16% 43% 22% 24% 52% 29% 32% 69% 100%

Table 7.3b Barley 5-, 10-, 15- and 20-Year AYP Progression (comparing 2010-12 with 2005-07, 2000-02, 1995-97 and 1990-92).After each country name the % of world area devoted to this crop in 2012 and 1992 are given


BarleyCanada 5% 5% 2.5 3.125 8 –30% 5% –27% –37% 20% –25% –46% 4% –44% –40% 9% –35% 119%EU–27 25% 20% 12.5 4.250 53 –11% 6% –5% –12% 0% –12% –17% 3% –15% –15% 4% –12% 162%CIS/FSU 27% 37% 13.5 2.000 27 –18% 7% –12% –17% 2% –16% –42% 39% –18% –50% 10% –45% 76%World total 100% 100% 49.5 2.625 129 –12% 8% –5% –10% 5% –6% –26% 16% –14% –33% 13% –25% 100%

Table 7.3c Sorghum 5-, 10-, 15- and 20-Year AYP Progression (comparing 2010-12 with 2005-07, 2000-02, 1995-97 and 1990-92).After each country name the % of world area devoted to this crop in 2012 and 1992 are given


SorghumUS 5% 10% 1.9 3.713 7 –21% –10% –30% –41% 4% –39% –53% –8% –57% –56% –9% –60% 243%Argentina 3% 2% 1.1 4.367 5 87% –7% 74% 93% –12% 70% 53% 11% 67% 52% 18% 78% 286%Australia 2% 1% 0.7 3.181 2 –12% 11% –7% –11% 36% 20% 20% 33% 59% 46% 64% 133% 208%World total 100% 100% 38.5 1.527 59 –8% 4% –4% –5% 3% –3% –9% 5% –4% –3% 5% 2% 100%

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For many animals, grains are a simple supplement for their diet(eg, beef cattle that consume mainly forage), while dairy cows, pigsand poultry require a considerable amount of protein to be added totheir diet since they perform optimally with a 20–25% protein feed,almost twice that of any cereal grain. This implies a 75:25 grain:mealcombination. Therefore, major feedgrain consumers must alsoproduce or import their protein needs, making the EU a majorimporter of softseed proteins. Although it may seem very straight-forward, the Pearson Square formulation works surprisingly well forestimating diets for forecasting animal or aquaculture needs, and iseasily found with a web-search.

Biosystems, of which one is the stomach, are complex and a seriesof associative effects can be observed. We do not digest equally meatand potatoes that are eaten separately, as compared to eating combi-nations in various proportions. The cooking method and previousmeal also influence digestion. We do not similarly digest meat andrice in the same way as meat and potatoes. This leads to feed conver-sion efficiency (FCE), a metric which is the first step towardsmetabolisability, the rate at which we actually use the nutrients wehave ingested. FCE is normally expressed as kilograms (kg) of drymatter output per Kg of DM feed.

In principle, as we allocate raw materials, feedgrains should onlygo to those processes that efficiently transform them into humanfood. For example, this means that we would not feed grains to cattleother than what is required to optimise their ability to digest cellu-losic feeds. If this were the case, and we were simply economicactors, we would have more than enough to feed the world –however, this would have the effect of large parts of the common dietdisappearing around the world. We prefer to eat as we please,dependent on prevailing price and income.

Associative effects include the reality of optimised nutrition,combining carbs, proteins and fats to get the optimal feed conver-sion. Diets have been balanced at the commercial level based on the least- cost formulation since the 1960s, and it remains a simple linearprogramming exercise. Animal nutrition has advanced much fasterthan human nutrition, not least because we can isolate genetics andenforce diets for animals, before butchering them to measure theoutput much more easily than with humans. Optimising nutrition isscientifically easy but socially complex, and we can imagine very

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different optimal strategies for an Olympic athlete and a couchpotato, or a new born infant and an octogenarian. Calories are tomodern nutrition what gasoline was to the Model T Ford: rawenergy. We have come to think in terms of metabolisability (useful-ness) of carbs, proteins and fat, and also the various micronutrientsand salt balances that influence our bodies and lives.

Wheat and barley belong to a category of grains (which includesoats) that the US Department of Agriculture (USDA) refers to as the“small grains”, and which are reported separately. A report issuedannually in December and various updates provide detail on a state- by- state basis of the AYP of these three crops. The EU treats wheat inthe same way that the US does maize, since it is the base of animalfeed, and consequently describe everything else as “coarse grains”.The reader must be careful to compare coarse grains in differentgrain economies – they mean different things.

There are over a dozen major feedgrain economies in the world,all growing grains and other food and feed in rotations – customisedfor the location, growing degree days (GDDs) and currenteconomics. A major grain economy is defined by the author as oneproducing more than several million tonnes in excess of its localrequirements through rotation. An example is Ukraine, whichproduces roughly 0.5 tonnes of wheat per capita. Its enormoussimultaneous local production of potatoes leaves it with a hugeexportable surplus of basic carbohydrate. The US produces onetonne of corn for every inhabitant. Once you get in the grain oroilseed producing business as a farmer, the quality of your outputand its ultimate designation as food or feed will depend on thevariety you chose to plant, how you cared for it, mother nature(weather), evolving global demand, the market where you choose tosell it and the degree to which it is carefully handled, processed,marketed and blended.

In the early 1990s, the US dominated the maize market globally asa producer and exporter. Its exports were residual to its own animalfeed and food, seed and industrial (FSI) needs, and it carried largestocks. In 1990/91, the US had almost 35 MMT in stock, producing200 MMT and exporting 55 MMT. The US Maize crop year (CY)begins in September and ends before or at the start of harvest inAugust of the subsequent year. Optimal planting is between April1st and May 30th while harvest runs from August 20th to November


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Table 7.4 True US corn (maize) annual S&D (MMT), using 1990/91 crop year alcohol as base: a 20-year perspective (September–August crop)

Crop year 1990/91 1995/96 2000/01 2005/06 2010/11 2011/12 2012/13

US (September/August)Harvested Area (MHa) 27.1 26.4 29.3 30.4 33.0 34.0 35.5Yield (MT/Ha) 7.44 7.12 8.59 9.29 9.59 9.24 7.67

Carryin 34.2 39.6 43.6 53.7 43.4 28.6 25.1Carryin less 20 days 'fuel as corn' 34.2 38.8 42.8 51.9 37.0 21.7 18.1

Production 201.5 188.0 251.9 282.3 316.2 313.9 272.4Production less EtOH 201.5 186.8 244.8 250.5 197.6 195.5 164.5Imports 0.1 0.4 0.2 0.2 0.7 0.7 3.2Total supply 235.8 228.0 295.7 336.2 360.2 343.3 300.7

Adj total supply (ex fuel) 235.8 226.8 288.6 304.4 241.6 224.9 192.8

UseFeed & Residual 117.1 119.4 147.9 155.3 121.7 115.5 109.2

% Adj Tot Supply 50% 53% 51% 51% 50% 51% 57%Food/Seed/Ind 36.2 41.4 50.2 76.7 163.3 163.5 153.0

Ethanol FSI 8.9 10.1 16.0 40.7 127.5 127.3 116.8Fuel FSI 0.0 1.2 7.1 31.8 118.6 118.4 107.9Non Fuel FSI 36.2 40.2 43.1 44.9 44.7 45.1 45.1Total FSI as % total supply 15% 18% 17% 23% 45% 48% 51%Non fuel FSI as % total supply 15% 18% 15% 15% 18% 20% 23%

Adj domestic use (ex fuel) 153.3 159.6 191.0 200.2 166.4 160.6 154.4Exports 43.9 56.4 49.3 54.2 46.6 39.2 25.4

Exports as % adj total supply 19% 25% 17% 18% 19% 17% 13%Exports as % adj domestic use 29% 35% 26% 27% 28% 24% 16%

Adj total use 197.1 215.9 240.3 254.4 213.0 199.8 179.8Carryout 38.6 10.8 48.2 50.0 28.6 25.1 13.0Adj Carryout (ex 20 days 'fuel as corn') 38.2 10.3 47.4 47.7 21.7 18.1 6.6

Adj C/O as % adj domestic use 25% 6% 25% 24% 13% 11% 4%

20 days of fuel as corn 0.5 0.6 0.9 2.2 7.0 7.0 6.4

Source: AgrimaxNote: In each step the traditional USDA format is improved by deducting maize produced for fuel EtOH and the appropriate stocks deducted.

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30th. Both run progressively northwards. The current crop wasplanted two weeks late. The US plants 353.5 MHa, and has a three- year average yield of 8.875 MT/Ha, which includes the disastrousharvest after the drought of 2012. Yields are normally expected to beover 9.0.

Since the two major drivers of domestic maize consumption for allmajor producers are Feed and FSI, Table 7.4 takes the unusualapproach of stripping out the “corn-for-ethanol” maize demand toallow accurate comparison with other countries. We can thenproceed to examine the US and other maize-economies sequentiallyand draw some conclusions for the future. Table 7.4 follows usualUSDA protocol so Area and Yield are directly comparable withUSDA. Thereafter, line by line, it strips out the ethanol demandwhich is not a grain demand and allows us to get to non-fuel FSI byfreezing fuel ethanol demand at 1990/91 CY-levels and shows in theAdjusted domestic use row that demand is in fact almost flat in theUS from 1990 CY to 2013.

The feed economyOne way to quantify grain demand is to employ feed- use data and grain- consuming animal units (GCAUs), factors that allow compar-isons of grain demand among different types of livestock. OneGCAU is 2.15 tons (short tons have 2,000 pounds, while metric have2,204.6). The USDA has developed a different factor for each type oflivestock based on the average amount that one such animalconsumes in a year. For example, a dairy cow has a GCAU factor of1.0474, while a broiler has a factor of 0.002. Using these factors, wecan see that one dairy cow will use the same amount of grain (1.0474× 2.15 tons = 2.25 tons) in a year, as approximately 523 broilers (onebroiler will consume 0.002 × 2.15 tons = 0.0043 tons, and 2.25 dividedby 0.0043 equals 523 broilers). The major GCAU factors are feedercattle: 0.0547, broilers: 0.002, layers: 0.0217, turkeys: 0.0155, dairy(cow + calf): 1.0474 and hogs: 0.2285. Informa Economics, Inc. offersthe best analysis of GCAU’s and also protein- consuming animalunits (PCAUs), allowing us to view the relative intensity by animaltype of each major feed component side by side.

Globally, we are eating an increasing amount of white meat,resulting in greater numbers of monogastrics and increased feedconversion efficiency (FCE). Two important issues arise here: there


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has been a dramatic increase in industrial (large- scale) animalfarming since the early 1990s, as well as further urbanisation, whichare additive in effect. The effort to supply large quantities of meatrequires significant supply chains and consumer packaged goods(CPGs) provided by companies such as Nestlé and Danone, meatcompanies and retail supermarkets.

Animal feed demand is not hardwired in the same way as FSI fortwo reasons. The feed compounder can choose many feeds to makethe ration, and the consumer has a lot more discretion, cut by cut, asto what meat they choose to eat. It is beyond the scope of this chapterto discuss global meat demand, but we do size the larger foodprotein economies – US, China, EU, Russia, Brazil and India (Table7.5) – and look at the broad consumption figures. In addition, wenote that, in much of the world, consumers will switch betweendifferent food proteins as their relative price changes. Price changesfor proteins are frequently more volatile than for grains or oilseeds.Pork accounts for 60% of China’s meat protein consumption. Ingeneral, poultry is substituted as a meat protein when pork pricesreach high levels. Conversely, when pork prices are affordable,China’s consumers prefer to purchase pork products.

Before looking at animal feed, we should briefly review animalprotein consumption. For some inexplicable reason, it is unusual tofind this critical information in most discussions on grains andoilseeds. Table 7.5 shows that China leads on production andconsumption, consuming twice as much meat as the US, while in 1990they consumed roughly the same. In terms of quick numbers, thismeans the average Chinese person eats half as much meat protein asthe average American, 40% more pork per capita, one quarter as muchchicken and one ninth as much beef. From a tiny chicken industry inthe early 1990s, China has come to consume more chicken than the US,at some 14 MMT. Not only does China consume 33% of the world’smeat, but also 33% of the world’s fish and aquaculture, and in 2010 itbecame the largest animal feedgrain user, including an estimated 12MMT of wheat. In terms of total animal protein, China is twice as big aconsumer than both the EU 27 and US.

Outside of China, Brazil has become a major meat exporter. Inaddition, despite being widely thought of as a vegetarian country,India consumes almost 20 MMT of meat per annum. It is estimated toconsume 5.5–6.0 kgs per capita of chicken with a retail value of

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US$9.0 billion, and it is widely touted to become more populous thanChina’s 1.35 billion people. Therefore, the forecasted 20% growth inchicken consumption in India will have an impact on the grainmarkets. Russia is seriously underserved in the animal protein cate-gory. In the not too distant future, the author would expect theChinese and Brazilian poultry economies to surge past the US andEU markets. As a final caution against analysing enormous popula-tions, remember the ag majors dig a lot deeper into this kind of data,and categorising 100 million people – never mind 1.35 billion – asbehaving in a homogenous way is intuitively risky.

The (FSI) industrial corn economyTo compare FSI of the major producers we must use the adjustedtrue corn supply and demand (S&D) for the US. It has a 45 MMT FSIdemand, roughly half the size of its feed consumption, growing 25%in 20 years and from 15% to 26% of the adjusted production whichmakes it globally comparable. Argentina consumes 2.2 MMT (up90% in 20 years, yet declining from 14 to 8% of the crop); Brazilconsumes 7.0 MMT (up 100% and down from 14% to 11% of thecrop); Ukraine uses 1.5 MMT (up 33%, from 21 to 7%); Russia 1 MMT(down 40%, from 44 to 10%); the EU 15.5 MMT (up 45%, from 30 to28%); RSA 4.5 MMT (up 15%, from 46 to 35%); China 64 MMT (up100%, from 27 to 31%) and India 8.3 MMT (up 23%, from 78 to 42%).Non US major producers total 110 MMT in FSI, an important 2.4times the US, growing 84% over 20 years and declining only slightlyfrom 28 to 25% of local production. The industrial corn economy isaimed at high value- added processing, and a typical analysis isheavily clouded by the conventional reporting process of FSI, whichincludes fuel ethanol. FSI has no meaningful seasonality while feeddemand does.

There are two main types of corn processing: dry milling (EtOH)and wet milling (sweeteners). The products of each type are utilisedin different ways. Over 80% of US ethanol is produced from corn bythe dry milling process. The ethanol is dehydrated to about 200ºproof using a molecular sieve system, and a denaturant such as gaso-line may be added to render the product undrinkable. With this lastaddition, the process is complete and the product is ready to ship togasoline retailers or terminals. The remaining stillage then under-goes a different process to produce a highly nutritious livestock feed


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(DDGS). The carbon dioxide released from the process is also utilisedto carbonate beverages and in the manufacturing of dry ice. Ethanolyield is constantly rising and water use efficiency improving. Theinitial assumption that biofuels were good for the environmentbecause they had a smaller carbon footprint is debatable regardingthe contention that the production of grain alcohol, and thereforeE15, may actually have a greater environmental impact than fossilfuels.

US non- fuel FSI averaged 15% of production across the 20-yearperiod, amounting to some 42 MMT. In theory, this all comes fromwet milling, a process which takes the corn grain and steeps it in adilute combination of sulphuric acid and water for 24–48 hours inorder to separate the grain into many components. The slurry mixthen goes through a series of grinders to separate out the corn germ.This process is the backbone of industrial processing for the produc-tion of fructose, glucose, dextrose, starch, potable alcohol andindustrial alcohols. These figures are typical of an industrial maizeeconomy found all over the world – with the exception of high- fructose corn syrup (HFCS) and fuel ethanol, which are US- specific.In 20 years, US production of HFCS increased by 33%, glucose anddextrose by 54%, starch by 14%, potable alcohol was unchanged andcereal consumption increased by 64%, largely driven by the USDAfood pyramid. The growth is predictable since the plants areannounced and take time to build. This industrial demand is largely non- switchable. For example, it was affected by a 2006 agreement(which became effective in 2008) to allow sweeteners to flow fromthe US to Mexico without tariffs.

HFCS is produced by wet milling corn to produce corn starch,then processing that starch to yield corn syrup, which is almostentirely glucose, and then adding enzymes that change some of theglucose into fructose. The resulting syrup (after enzyme conversion)contains naturally 42% fructose, and is consequently called HFCS 42.Some of the 42% fructose is then purified to 90% fructose (HFCS 90).To make HFCS 55, the HFCS 90 is mixed with HFCS 42, and thisincreased fructose percentage gives it the same “sweetness” taste assugar (which is why it is called “high” fructose corn syrup).

A system of sugar tariffs and sugar quotas imposed in 1977 in theUS significantly increased the cost of imported sugar, and US manu-facturers therefore sought cheaper sources. HFCS, as it is derived

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Table 7.5 FAO estimates of world 2010 animal protein consumption by type – major economies (MMT)

Beef and Pork Poultry Meat Commercial catch Total %Meat %of worldveal of world & meat protein in diet fish

aquaculture

World 57.3 103.2 76.0 236.5 142.0 100% 615.0 38% 100%US 12.0 10.2 16.5 38.7 4.9 16% 82.3 47% 3%EU-27 8.1 23.0 9.0 40.1 6.4 17% 86.6 46% 5%Brazil 9.1 3.2 12.3 24.6 0.5 10% 49.7 49% 0%Russia 1.4 1.9 2.3 5.7 3.5 2% 14.8 38% 2%India 2.8 2.6 5.5 7.5 2% 18.4 30% 5%China 5.6 51.1 12.5 69.2 47.5 29% 185.8 37% 33%

Top ten consumers by rank1 US China US China2 Brazil EU 27 China India3 EU-27 US Brazil Peru4 China Brazil EU 27 Indonesia5 India Russia Mexico US6 Argentina Vietnam India Japan7 Australia Canada Russia Chile8 Mexico Japan Argentina Vietnam9 Pakistan Philippines Iran Thailand

10 Russia Mexico Thailand Russia

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from corn, is more economical because the domestic US prices ofsugar was twice the global price while the price of corn was kept lowthrough government subsidies to growers. HFCS became an attrac-tive substitute, and was preferred over cane sugar by the vastmajority of US food and beverage manufacturers. Soft drink makerssuch as Coca- Cola and Pepsi use sugar in other countries, butswitched to HFCS in the US during the mid-1980s. In 2010, the CornRefiners Association applied to allow HFCS to be renamed “cornsugar”, but this was rejected by the US Food and DrugAdministration in 2012.

Barley (Hordeum vulgare L.) is a member of the grass family andtherefore closely related to wheat, and is a major cereal grain.Important uses for barley are as animal feed, as a source offermentable material for beer and certain distilled beverages, and asa component of various healthfoods. It is used in soups and stews,and in barley bread. Malting barleys are normally separate anddistinct varieties from feed barley. In a ranking of cereal crops in theworld, barley is fourth, both in terms of quantity produced and areaof cultivation. For our purposes we include it in feedgrains although,as with most of these crops, the lines are blurred.

Canada, the EU and CIS/FSU are the major barley producers (seeTable 7.3b), accounting for 70% of global production, and their yieldsare quite different at 3.125, 4.25 and 2.00 MT/Ha, respectively, givingvery different competing crop economics. As one can imagine, thedecline in area has been greatest in the low yielding producers, andin 20 years Russia fell from almost 50 MMT to almost 25, and globalproduction decreased from 170 to 130 MMT, down some 33%, ofwhich the big three declined by 40, 15 and 50%. Barley has beenclosely associated with small farms and on- farm feeding, whichmeans the decline will continue.

Sorghum is in a genus of numerous species of grasses and a rela-tive of other C4 plants like maize and sugarcane. With lower yieldsthan maize the US (the world's largest producer, see Table 7.3c) hasmore than halved its sorghum crop to 10% of global production, theremainder being scattered around the world where it may be locallyimportant as food or feed. Many species are cultivated in warmertropical climates worldwide. It is also biologically in the same tribeand subfamily as sugarcane and might have been grown widely inBrazil where local tastes prefer rice as food carbohydrate. Globally its

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Table 7.6 Wheat five-, 10-, 15- and 20-year AYP progression (comparing 2010–12 with 2005–07, 2000–02, 1995–97 and 1990–92).After each country name the % of world area devoted to this crop in 2012 and 1992 are given

Wheat Last three- year average (2010–12) 2005–07 2000–02 1995–97 1990–92 % of worldArea A Yield Y Prdn P AYP % growth AYP % growth AYP % growth AYP % growth yield

US 9% 11% 19.0 3.000 59 –4% 13% 9% –3% 16% 12% –24% 22% –7% –25% 21% –10% 100%Canada 4% 6% 9.0 2.875 25 –5% 12% 7% –13% 39% 20% –24% 27% –4% –37% 29% –19% 96%Argentina 2% 2% 4.0 3.625 15 –29% 29% –8% –37% 59% 0% –29% 60% 14% –16% 70% 46% 121%Brazil 1% 1% 2.0 2.500 5 6% 41% 48% 21% 72% 107% 44% 60% 128% –15% 100% 78% 83%EU 27 12% 8% 25.5 5.250 135 3% 4% 7% –2% 7% 4% 10% 12% 24% 35% –2% 32% 175%Morocco 1% 1% 3.0 1.625 5 4% 28% 29% 10% 58% 75% 22% 38% 51% 19% 21% 39% 54%CIS/FSU 22% 21% 49.0 1.875 92 5% –2% 3% 9% 0% 9% 4% 29% 35% 4% 0% 4% 63%Turkey 4% 4% 8.0 2.125 17 –6% 6% 0% –9% 12% 2% –8% 17% 8% –11% 20% 7% 71%China 11% 14% 24.0 4.875 118 4% 8% 12% –3% 29% 24% –18% 28% 5% –21% 51% 19% 163%India 13% 10% 29.0 3.000 87 8% 13% 22% 11% 8% 20% 14% 16% 33% 23% 32% 63% 100%Australia 6% 4% 13.5 2.000 26 11% 45% 60% 14% 24% 40% 31% 3% 35% 60% 19% 90% 67%World total 100% 100% 220.5 3.000 668 2% 7% 10% 2% 13% 15% –2% 19% 16% –2% 21% 18% 100%

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stagnant 60 MMT production is neither important in world trade norexpected to be so.

FOODGRAINSThere are two major foodgrains: wheat and rice. The world harvests670 MMT of wheat annually (see Table 7.6), of which the majorwheat economies are the US (60), Canada (25), Argentina (15), Brazil(5), EU-27 (135), Morocco (5), CIS/FSU (90), Turkey (15), China (120),India (85) and Australia (25). World rice harvests is 460 MMT (seeTable 7.7), of which Brazil (10), Thailand (20), China (140) and India(100) are the largest economies. There are thousands of wheat vari-eties being grown in the world, each selected, bred and adaptedbased on locality and consumer preference.

Table 7.6 shows that US wheat production is flat (area declinesand yield improves) and expected to decline as maize takes up moreland for ethanol. Canada, Argentina, Australia and Brazil are stag-nant, while the EU, China and India have grown quite dramatically.In addition, the FSU declined dramatically as it became more marketbased, but has considerable potential to recover productionthrough the use of modern farming methods. Yield growth aroundthe world remains good, in many cases due to suboptimal wheatareas being taken out of production in China and the FSU.

Throughout the world, there are various ways of categorisingwheat, largely dependent on intended use. We can think of wheatglobally and genetically as having 10% protein content, oftenreferred to as its fair merchantable quality (FMQ). FMQ changeswith variety, husbandry and weather. While the EU tends to specifywheat by specific weight (in the US, it is thousand grain weight,TGW) measured in kilograms per hectolitre (Kg/hl) and variety,feed wheat is generally assumed to have a 72 kg/hl FMQ (UK Liffecontract spec) and milling or baking wheat to have a 76 kg/hl FMQ(French Euronext contract spec).

The most common simple laboratory test for protein quality(gluten) is the Hagberg falling number (HFN), which measures therate of fall of a plunger through a column of water/flour mix, repre-senting its stickiness or so- called “gluten extensibility” – the ability ofthe wheat to form a uniform rising dough. From the most simplefeed/food designation in Europe, each major wheat exporter has itsown preferred designations. A wheat chapter that does not discuss

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type by geography and consumption would be pointless, so we willtake a deeper look at China and India. Wheat varieties can vary over100 miles, and there are thousands around the world.

In fact, there are several hundred varieties of wheat grown just inthe US, all of which fall into one of six recognised classes. Wheatclasses are determined not only by the time of year they are plantedand harvested, but also by their hardness, colour and the shape oftheir kernels. Each class of wheat has its own family characteristics,as related to milling and baking or other food/feed use. Wheatproduction by type across the states and then subsequently forspring, winter and durum, and the intensity by county within eachstate can be seen at: http://www.thefreshloaf.com/node/4632 -/majorwheat- growing- regions- us- reference- maps.

The largest volume of US wheat is of Chicago Board of Trade(CBOT) type and specification and is often referred to as simply W. CBOT- type wheat is both an animal feed and capable of makingbiscuit dough, or a simple unleavened dough, and has low proteincontent and poor “gluten extensibility”. Kansas City Board of Trade(KCBT) wheat (often referred to as KW) is true bread wheat destinedfor human consumption but capable of being fed in small quantitieswith other grains to animals. Its gluten extensibility is sufficient tocapture a bubble of air and allow the dough to rise to produce a loafof bread. Minneapolis Grain Exchange (MGE) wheat (often referredto as MW) is best thought of as a high- class or technical wheatcapable of making fine pastries such as croissants. The gluten isextremely flexible and can produce a low- dough large bubble.

W has no protein minimum per se, while KW has 11% and MW has13.5%. All three futures contracts are based on #2 grade, which is aminimum TGW, #1 would be higher and sub-economic to deliver aswe can simply blend it down. All of these markets carry a variety ofscales that adjust for delivering #3 grain (lower specific weight), andKW and MW allow for penalties to be deducted for protein levelsdown to 10.5% and 13.0%, respectively. “Protein scales” refer to theper 0.5% or 1.0% value for protein quoted in the physical or cashmarkets, and represent the value of different grades that are blendedby millers and shippers to make actual grists (the baker’s wheat“slate”) or shipping contract minimums. Protein levels do not blendlinearly but are close enough for anything we need to discuss here.Baking is in fact a science, and there is a large body of work available


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on wheat qualities, baking and the use of gluten- extenders, forexample, a man- made additive intended originally for use in poorharvests but now used widely to create uniformity.

Although many talk about declining wheat per capita consumptionand the rise of corn and soybean production, this masks a much morecomplex picture. To talk of wheat and bread is a serious mistake,demographically. The grains and oilseeds industries of other coun-tries are substantially different to the OECD; Mexico thinks abouttortillas more than loaves of white bread, the Indian/Pakistan wheateconomy is very large but flour production and sales direct to theconsumer, rather than bread, remains a substantial industry, whileAsian noodles are a major source of wheat consumption all over theglobe, not just in Asia. Although much has been written on wheat andfoodgrains, most focus on the easily researched OECD producers: US,Canada (three planting zones, 14 classes and three or four grades forexport of each variety), Australia (five planting zones, six principalgrades targeting 13 end uses, from Indian bread to Udon noodles andAsian instant noodles), Argentina (seven planting zones, three majorcategories, four flour grades) and France (17 planting zones or areas,four classes and four grades, all variety- specific). We will look at thetwo most populous countries, China and India, to provide a crossreference of their enormous wheat economies rarely found outside anag major or the most serious investor. Without understanding thesetwo rapidly evolving wheat economies there should be little expecta-tion of understanding price evolution.

Prior to the expansion of the EU to 27 countries, China was theworld’s largest producer and consumer of wheat. Comparativeadvantage has led China to discourage low- quality wheat produc-tion, and it has reduced the amount of land devoted to wheat sincethe early 2000s. It imports as a way of balancing quality not quantity,and the US wheat class designations will not advance your under-standing of Chinese wheat needs. Its planting zones can be dividedbroadly into three: hard wheats around the Greater KhinganMountain range, hard wheats along the Yellow, Huai and Hai riversand, finally, soft wheats along the lower Yangtze river. They includenine classes (the first is H or S for hard and soft, then W or R for whiteor red and W or S for winter or spring – the main ones are HWW,HWS, SWW, SWS, HRW, HRS, SRW, SRS and other), and five grades(79+, 77+, 75+, 73+ and 71+ Kg/Hl) by specific weight and a variety

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of other quality characteristics, including moisture and foreignmatter.

The nine Chinese grades are further divided into two classes of high- quality strong gluten wheat, two classes of high- quality weakgluten wheat and three other qualities for specific end- uses. Eachgrade class has a specific flour quality, including HFN and a range ofother specific qualities. In addition, each of the classes and flourgrades are identified by planting zone. Some 10% of the wheat isused for high- quality bread cookies and dumplings, some 50% forsteamed bread, noodles and instant noodles, and the remaining 40%is used locally for home baking or small bakeries. Urbanisation andthe industrialisation of its food industry is dramatically changing thepatterns of consumption in China. Despite their large total animalprotein consumption, the population effect means China (andindeed Asia) depends heavily on foodgrains for nutrition.

Noodles represent some 40% of total flour consumption, and are amajor staple in East and Southeast Asian countries. Apart fromwheat flour, they can be made from rice flour, potato flour, buck-wheat flour, corn flour, bean, yam and soybean flour. While pasta ismade from tetrapolid durum wheat (Triticum durum), noodles aremade from the hexaploid Triticum aestivum, which contains gluten,which reacts to the pressure during the sheeting process. Eggs arefrequently added to provide a firmer texture.

Given that wheat consumption in the form of Asian noodlesexceeds the total US wheat production, we can understand its signif-icance in the forecasting of demand for wheat round the world.Chinese noodles are typically made from hard wheat flours,Japanese noodles from soft wheat of medium protein. By colour,they are typically classified as white (containing salt) or yellow(containing alkaline salt). White salt noodles include Japanesenoodles, Chinese raw and dry noodles. Yellow noodles includeChinese wet noodles, Hokkien noodles, Cantonese noodles, Chukka- men, Thai bamee and instant noodles. Over 50 billion meals areannually served around the world that contain ramen noodles alone.Asia imports US HRS, DNS, HRW, SRW and SWW, Australian stan-dard white (SW), premium white (PW) and prime hard wheats (PH),as well as Canadian Western Red Spring (so called CWRS), CanadianWestern Red Winter, Canadian Prairie Spring White and CanadianPrairie Spring Red wheats to blend with local wheats to make


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noodles. China consumes 35% of global instant noodles, just twice asmuch as Indonesia, which is twice that of Japan. The US and SouthKorea consume one fifth as much as China.

Steamed bread accounts for 60% of flour consumption inNorthern China (where it is a staple) and 20–30% in the South (whereit is a dessert). In Asia, it represents 5–15% of flour consumptiondepending on the country, and it is popular in the Philippines, forexample. It is made predominantly from soft- to- medium hardwheats and, while it is prepared somewhat like a western pan bread,it is then steamed rather than baked. There are three principal typeswith varying protein and gluten qualities – Northern, Southern andTaiwanese. The Northern type is typically made from local wheat,and has 10–11% protein. The Southern type has added sugar andbaking powder. The Taiwanese type has the highest protein, whileall three types contain yeast. The steaming process produces a higher- quality food than baking as it destroys less of the amino acids(especially lysine) than the higher temperature baking. However, itis less conducive to large- scale production since much of its eatingqualities are associated with being freshly steamed. It loses qualitywhen re- steamed and its shelf life is short compared to baked breaddue to the higher moisture content. This will inevitably lead to whatthe US and Europe call “in- store baking” as a means of bringing large- scale industrialisation to the cities.

India’s second largest foodgrain crop is wheat, but strategically ithas tremendous and growing importance with an ever- larger popu-lation, as it is a non- monsoon- based crop. It has six major growingareas: the Northern Hill Zone (NHZ, 1.2 MHa), the North West PlainZone (NWPZ, 9.0 MHa), the North East Plain Zone (NEPZ, 9.0MHa), Central Zone (CZ, 5.0 MHa), Peninsular Zone (PZ, 1.0 MHa)and the Southern Hill Zone (SHZ, 0.2 MHa). Some 90% of Indianwheat receives irrigation, although in the NHZ this does not occur athigher elevations but closer to rivers unless the crops are close to ariver.

The NWPZ is a large fertile part of the Gangetic Plain and is morethan 90% irrigated, with crops maturing in 140 days and multipledays with lows of less than 5° C. Wheat plants tiller well and developmany spikes, so yields are high. However, disease can be a problemdue to mono- cropping (poor rotation). Temperature spikes at grainfill can hurt yields in the same way as in the US Midwest. The NEPZ

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is humid with a large number of minor rivers for irrigation, and italso suffers from wheat diseases associated with a humid environ-ment. Wheat matures here in 125 days and is susceptible tounneeded rain showers at harvest time. The CZ and PZ are highlanddeep soil areas but difficult to irrigate and may receive only twoapplications of water per season, and with high temperatures we seeshort growing seasons and poor yields.

India has developed and distributed 200 of its own varieties ofwheat since the 1980s which see several days with temperature lowsof less than 5°C predominantly targeted for the higher yieldingregions. Disease control is effected by using the SHZ for plantbreeding. The many problems associated with Indian wheat produc-tion have been solved around the world and will be in India as well,although this will take time. These problems include but are notlimited to poor acceptance of new varieties and the widespreadplanting of retained production from year to year, poor mechanisa-tion, lack of modern harvesting methods and inexperienced machineoperators, which results in low- quality grain samples and lots ofadmixture of foreign matter.

A considerable amount of Indian wheat is consumed as chapati, aflat unleavened bread. The warm wheat areas have higher proteinthan the cool NHZ. Hill wheats are widely used for biscuits/cookies.PZ wheat is used for crackers and cookies due to its protein level andquality. The baking industry is largely based in the south, which hasa deficit in wheat and the vast size of the country makes transportexpensive. Significant quantities are exported for hard currency, butthe industrialisation of baking and the introduction of whole grainbranded flour will lead to improvements in revenues. Better pricesfor wheat will improve flour yields, and urbanisation will changefarming practises and consumption patterns, making India a signifi-cant importer and producer of higher- quality breads for itsincreasing population.

India will become an importer of higher- quality wheats over time,which will have a significant impact on world wheat flows. Withonly five classes (medium hard bread wheat, premium hard breadwheat, biscuit wheat, durum and Khapli wheat, a particular Indianwheat used for semolina) and no effective grading due to a largelyflat price structure for wheat within classes under the Indian staterun Public Supply Distribution (PSD) system, the Indian wheat


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market will evolve as it becomes more market driven. Most ethnicbreads (chapati, naan, tandori, rumati, roli, puri and bhatore) aremade from medium- hard bread wheat. The Indian government hastraditionally supported domestic wheat prices at a significantpremium to world prices and they have carried significant stocks toallow it to intervene in the domestic food price.

Given the overall excitement in the wheat market of 2007, we willreview the run- up to this bull market, its causes and its effects. Aswith all great bull markets, its roots lay in the previous year’s crop,with widespread problems for the major exporters (US, EU-27,Canada, Australia, Argentina and Russia). Since the early 2000s,these countries have produced between 271 (2006–07) and 344 MMT(2008–09), carried stocks as low as 36 MMT (end-2007) and as high as73 MMT. Exports have ranged between 88 and almost 125 MMT andstocks have responded dramatically, to build or draw- down, as pricesignals have changed. At the beginning of 2006–07, their stocks stoodat 65 MMT and by the beginning of 2008–09 had fallen to 36 MMT, a10-year low.

Of these countries, all but Russia has highly visible stocks. Russiatypically has a stock/use ratio of 10%. The EU has highly volatilewheat production, producing 133 MMT in CY2002/03 and111/147/132 MMT in the subsequent years, respectively. In CY2005–06, its crop decline year- on- year (yoy) of 14.5 MMT was absorbed bythe other major producers. The following year, however, saw disap-pointing crops with the US down 8.0 MMT, the EU down 7.5 andAustralia down a disastrous 14.5 MMT. This 30.0 MMT dip was notoffset by the other major exporters and exports from the groupdropped to 88 MMT. Australia has a volatile, rain- dependent wheatcrop and, while production ranges between 10.0 and 30.0 MMT, it isin fact rather binary, producing less than 15.0 MMT in dry years andmore than 25.0 MMT in wet years.

In 2006–07, the world became increasingly concerned with wheatand the US drew stocks from 15 to 12 MMT, cutting domestic use andexports. Similarly, the EU drew stocks by almost 10 MMT and also cutdomestic use and exports. Canada boosted exports and drew stocks,and Australia halved its stocks to export more than 16 MMT versusthe previous year’s 23 MMT. Russia maintained big exports at 10MMT, and Argentina almost emptied its stocks completely. At timeslike these, we turn to the minor exporters (Ukraine, Kazakhstan, India,

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China and Turkey) to see what they can contribute. The Ukraine cropdipped from 18.0 to 14.0 MMT, and they drew stocks to maintainexports at 3.5 MMT, down 3.0 on the previous year. Russia agitated fora Ukraine export ban. Kazakhstan exports surged from 4.0 to 8.0 MMTon a decent crop and a stock draw. India, however, who can be a 5.0MMT exporter, was coming off two disappointing crops and had lowstocks. So, not only were they absent from the export market inCY2006/07, but in fact imported almost 7.0 MMT. China’s productionrose 11.0 MMT yoy, but they were already in stock- building mode andwithdrawing from the export market strategically. China thereforebarely exported 1.0 MMT more than the previous year. Turkey wasdown to bare minimum stocks and had a sufficiently reduced crop inCY2006/07 to be absent from the export market. In fact, across theminor exporters there was a significant increase in imports yoy,primarily lead by India and indicating the structural shift in the twomost populous countries in the world; China is now a structuralimporter, and while India may come and go as both exporter andimporter, it will inevitably follow China to the structural importercategory.

Among the major importers, Egypt built stocks by 1 MMT in2006/07 and increased imports yoy, Brazil increased imports by 1MMT, Japan maintained imports, Indonesia raised imports andAlgeria cut theirs by an offsetting amount. South Korea, Nigeria,the Philippines and Morocco cut imports modestly, while Iraqimports took a big downturn and Mexico was unchanged. Overall,major importer demand dipped by only 2.0 MMT in the face of a30.0 MMT dip in major exporter production, pinpointing the verystaple nature of wheat demand and its insensitivity to price. Itshould be clear to the reader that every large market player hasaccess to the shipping fixtures, or grain movements, by loadportand discharge port.

We then entered the major bull run. Any problems in the 2007growing season would cause a major disruption, and the hedgingpressure and speculative pressure increased to intense levels. USproduction rebounded by 6.5 MMT in CY2007/08 and another 12.0MMT in CY2008/09, but only after drawing stocks to a low 8.0 MMT.Disastrously, the EU had more problems in 2007/08 and productiondipped another 5.0 MMT, and stocks hit a near record low. ByCY2008/09, a world- saving rebound of 31 MMT would be


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Table 7.7 Rice 5-,10-, 15- and 20-year AYP progression (comparing 2010–12 with 2005–07, 2000–02, 1995–97 and 1990–92). Aftereach country name the % of world area devoted to this crop in 2012 and 1992 are given

Rice Last three- year average (2010–12) 2005–07 2000–02 1995–97 1990–92 % of worldArea A Yield Y Prdn P AYP % growth AYP % growth AYP % growth AYP % growth yield

Brazil 2% 3% 2.5 3.250 8 –12% 18% 5% –18% 44% 18% –27% 77% 30% –41% 106% 22% 113%Thailand 7% 6% 11.0 1.875 20 4% 5% 9% 7% 10% 18% 15% 22% 41% 21% 34% 61% 65%China 19% 22% 30.0 4.750 141 4% 6% 10% 5% 6% 12% –4% 8% 4% –8% 17% 8% 165%India 27% 29% 43.5 2.250 100 –1% 7% 6% 0% 20% 20% 1% 22% 23% 2% 32% 35% 78%World total 100% 100% 158.5 2.875 461 3% 6% 9% 6% 11% 18% 6% 15% 21% 8% 21% 30% 100%

Source: Adapted from Informa

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harvested, but not until the market had gyrated wildly. Adding tothe woes in 2007, Canadian wheat production dipped 5.0 MMT, andthey too drew stocks heavily to record a low of barely 4.0 MMT.Australian production recovered, by a mere 2.8 MMT, to a sub-14.0MMT crop. Argentinian and Russian crop production increasedslightly, and the major exporters saw their total production increaseby 6 MMT and stocks draw another 7.0 MMT on top of the previousyear’s 20.0 MMT decline. Collectively, their production would surgeby more than 66.0 MMT in CY2008–09 to end the bull market. Minorexporters had a domestic production rebound of 8.0 MMT butreduced their exports yoy, and while they cut their imports in halfthey were also building stocks. Although there was some variancebetween major importers, stock were built modestly and importsrose modestly.

Wheat exhibited the classic volatility of a market with inelasticdemand and whose price- solving mechanism is to scale a steepmarginal supply curve to increase production at the expense ofcompeting crops. This occurred at the same time as crude oil pricewas increasing dramatically and maize demand for ethanol surgedin the US. As in Table 7.6, the wheat supply from 2005–07 to 2010–12would only increase in area by 2%, yield would rise 7% and produc-tion by 10%. Production increases were 20% in the EU, 18% in China,14% in CIS/FSU, 13% in India and 9% in the US.

As a foodgrain, rice provides the most widely consumed staplefood of over half the world’s population (see Table 7.7), especially inAsia and the West Indies. It is the seed of the monocot plants Oryzasativa (Asian rice) or Oryza glaberrima (African rice). It is the predom-inant dietary energy source for 17 countries in Asia and the Pacific,nine countries in North and South America and eight countries inAfrica.

Rice provides 20% of the world’s dietary energy supply, whilewheat supplies 19% and maize 5%. It is the grain with the second- highest worldwide production after maize, but since a large portionof maize crops are grown for purposes other than human consump-tion, rice is the most important grain for human nutrition and caloricintake, providing more than one fifth of the calories consumedworldwide by the human species. There are many varieties of riceand culinary preferences vary regionally. In the Far East, there is apreference for softer and stickier varieties.


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Rice yields continue to grow while area is largely stagnating,China’s dramatically better yields than India means it produces 40%more rice from two- thirds as much land. India’s yield growth at 33%in 20 years is, however, twice that of China, and Brazil’s yield hasdoubled in the same period. As previously mentioned, the Indiancrop is monsoon- driven. World trade is small and most countriesthat consume rice grow their own.

To close the foodgrain section, some basic numbers are providedfor processed food sales. Worldwide, they are approximatelyUS$3.5 trillion, and the industry is growing. The processors are giantcompanies that own huge brands, the CPGs such as Nestlé SA. Thesecompanies dwarf the ag majors who are their suppliers. The foodindustry is a complex, global collective of diverse businesses thatsupply much of the food energy consumed by the world’s popula-tion. Only subsistence farmers, those who survive on what theygrow themselves, can be considered outside of the scope of themodern food industry.

In developing country markets, the two reference points are theUS and the UK. With populations of 313 million and 55 million,respectively, they can be used to estimate what the food economiesof less- developed countries will likely look (more) like in the nextfew years.

In the US, consumers spend approximately US$1.3 trillion annu-�

ally (see www.ers.usda.gov /data- products/food- expenditures.aspx) on food, or 8.5% of GDP. Over 16.5 million people areemployed in the food industry. There is a consumer base ofroughly 300 million. Therefore, China, for example, has thepotential to be four times this size.In the UK, the food industry is extensive. The UK grocery market�

was worth £163.2 billion in 2012 or 11% of GDP, and employstwo million people. It is the largest manufacturing sector in theUK and represents around 15% of total UK manufacturing.Around 13% of all people working in manufacturing in thecountry work in the food and drink industry. This is roughly aconsumer base of 50 million, so South Korea, for example, hasthe potential to be this size.

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Table 7.8(a) Soybean 5-,10-, 15- and 20-year AYP progression (comparing 2010–12 with 2005–07, 2000–02, 1995–97 and 1990–92). After each country name the % of world area devoted to this crop in 2012 and 1992 are given.

Soybeans Last three- year average (2010–12) 2005–07 2000–02 1995–97 1990–92 % of worldArea A Yield Y Prdn P AYP % growth AYP % growth AYP % growth AYP % growth yield

US 29% 42% 30.5 2.750 85 8% –2% 5% 4% 8% 12% 17% 11% 30% 31% 18% 54% 110%Argentina 18% 9% 18.5 2.625 49 16% –7% 9% 62% –2% 60% 191% 20% 248% 286% 12% 333% 105%Brazil 24% 18% 25.5 2.875 74 18% 7% 27% 57% 6% 66% 114% 25% 166% 154% 53% 288% 115%Paraguay 3% 2% 3.0 2.125 6 19% 8% 28% 105% –18% 68% 155% –6% 139% 222% 39% 348% 85%China 8% 14% 8.0 1.750 14 –15% 10% –6% –14% 4% –11% –1% 3% 2% 8% 26% 36% 70%India 10% 5% 10.0 1.125 11 22% 10% 34% 73% 27% 120% 96% 18% 132% 223% 21% 294% 45%World Total 100% 100% 105.0 2.500 258 13% 1% 15% 33% 5% 39% 63% 15% 87% 89% 25% 135% 100%

Table 7.8(b) Rapeseed 5-,10-, 15- and 20-year AYP progression (comparing 2010–12 with 2005–07, 2000–02, 1995–97 and 1990–92). After each country name the % of world area devoted to this crop in 2012 and 1992 are given.

Rapeseed Last three- year average (2010–12) 2005–07 2000–02 1995–97 1990–92 % of worldArea A Yield Y Prdn P AYP % growth AYP % growth AYP % growth AYP % growth yield

Canada 22% 15% 7.5 1.750 14 38% 5% 45% 88% 32% 143% 69% 34% 127% 164% 37% 258% 100%China 20% 31% 7.0 1.750 13 15% –1% 14% 2% 16% 18% 10% 32% 45% 23% 45% 78% 100%India 20% 31% 7.0 1.000 7 6% 5% 11% 44% 15% 65% 4% 9% 14% 12% 13% 27% 57%EU 27 19% 15% 6.5 3.000 20 17% –1% 17% 57% –1% 56% 58% 94% 186% 117% 11% 145% 171%World total 100% 100% 34.0 1.750 61 25% 2% 28% 47% 16% 70% 47% 28% 87% 75% 31% 131% 100%

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Table 7.8(c) Sunseed 5-,10-, 15- and 20-year AYP progression (comparing 2010–12 with 2005–07, 2000–02, 1995–97 and 1990–92). After each country name the % of world area devoted to this crop in 2012 and 1992 are given.

Sunseed Last three- year average (2010–12) 2005–07 2000–02 1995–97 1990–92 % of worldArea A Yield Y Prdn P AYP % growth AYP % growth AYP % growth AYP % growth yield

US 2% 6% 0.5 1.625 1 –19% 2% –19% –30% –5% –35% –40% 8% –35% –22% 11% –14% 108%Argentina 6% 15% 1.5 2.125 4 –27% 25% –9% –16% 23% 3% –44% 18% –34% –29% 38% –2% 142%EU 27 16% 24% 4.0 1.875 7 12% 15% 28% 15% 26% 45% –31% 96% 48% –3% 37% 66% 125%CIS/FSU 53% 26% 13.0 1.375 18 32% 15% 51% 84% 42% 160% 106% 43% 195% 179% 7% 199% 92%World total 100% 100% 24.5 1.500 37 9% 16% 26% 23% 28% 57% 23% 23% 52% 44% 16% 68% 100%

Source: Adapted from Informa

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OILSEEDS – VEGETABLE PROTEINSWe harvest 355 MMT of the three major softseeds (soybeans, rape-seed and sunseed, (see Tables 7.8 a, b and c)), of which 260 aresoybeans, 60 are rapeseed and 35 are sunseed. The major soybeaneconomies are the US (85), Argentina (50), Brazil (75) and Paraguay(5). For rapeseed the big four economies are Canada (15), EU-27 (20),China (15) and India (5). For sunseed the big three economies areCanada (5), EU-27 (5) and CIS/FSU (20). The softseeds yieldvegetable oil and high-protein meal in ratios of 19/35/33% of oil and79/63/65% meal from soy/sun/rape crushing, the balance being thehulls or shells. This means a global soft oil output of roughly 50, 12and 20 MMT, respectively. Much of the softseeds are crushed andtheir products consumed locally. Historically, we have describedChina, or Asia, as “oil deficit” and the EU as “meal or proteindeficit”. Global exports of the three softseed oils are estimated byAgrimax at 8.0, 5.0 and 3.0 MMT respectively, compared to globalpalm oil flows of more than 38.0 MMT.

In the early 1990s, the US dominated global soybean production.By a decade later, Brazil and Argentina combined produced as muchas the US, and by the early 2010s Brazil alone threatened to match theUS in production. World planted area has grown by almost 90%.World yields have grown by 25%, and production has surged by135%. Brazil has the highest yields, followed by the US andArgentina, but it is important to note that yield advancements are indecline and largely occurred during the 1990s.

In contrast, hard oils (their physical state at room temperature) areproduced primarily from fruit (as compared biologically to seeds).The most commercial is palm oil, but the family also includescoconut oil and others. Butter and lard (animal fat) are also includedin this category. Malaysia and Indonesia dominate palm oil produc-tion and annually export some 19.0 MMT each, amounting to morethan 65% of global vegoil (the common industry abbreviation forvegetable oils) trade flows. Since they come from fruit, there is anassociated pulp that remains from processing, normally returned tothe soil as fertiliser.

The four countries that dominate world rapeseed production,with 90% of production, are all major wheat producers, and rapeseedgrows very nicely in a rotation with wheat. EU yields are more than170% of the world average and planted area is now more or less


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equal to each of the other producing countries, at 7 MHa each. EUarea planted has driven the global yield growth, expansion beingprimarily for biodiesel production. Indian yields are abysmal, butdomestic consumption is protected by the large costs of importingrapeseed from major producers and trucking to the interior. Chinadominates global rapeseed trade.

Sunseed production is driven by three countries (CIS, EU andArgentina – 49%, 19% and 11%, respectively), with the US a poorfourth at 3%. While production has tripled in 20 years the low yieldsand expanding area in the CIS has slowed the market growth. Yieldshave expanded healthily in any case, although there was a lostdecade when CIS yields regressed rather than grew. Sunflower oil issold at a premium compared to other vegetable oils into toMediterranean and North African markets, where it is preferred as acooking oil. Global production at less than 40.0 MMT will grow to50.0 or 60.0 MMT.

Each major vegetable protein economy produces a variety ofproteins locally as determined by their comparative advantage, andthe balance is either exported or imported. Since China is a majorimporting vegetable protein economy, its S&D balance issummarised here. Its domestic production of almost 60.0 MMT ofmajor oilseeds is dominated by soybeans (15), sunseeds (2.5) andrapeseeds (12.5), as well as cottonseeds (12.5) and groundnuts (15),the non- US name for peanuts. In addition, it imports a staggering60.0 MMT of soybeans and 2.5 MMT of rapeseeds, crushing 100.0MMT per annum. In the early 2000s, China imported only 20.0 MMTof soybeans and had a major softseed crush capacity of 55.0 MMT, ofwhich 25.0 MMT was soybeans. At that time, the big four soybeanexporters (US, Argentina, Brazil and Paraguay) had a total crushcapacity of 100 MMT which has grown over the same 10-year periodto roughly 125 MMT, (US 45, Argentina 38, Brazil 40 and Paraguay 3)while China’s grew from 25 to 100 MMT.

Softseed crushing is the process by which seeds are pressedthrough a die. Heat, steam and solvents are used to extract the oilfrom residual meal to form the two principal by- products and leavethe hulls and seed covering. One can imagine other softseeds that areproduced and used in different ways, such as peanuts, sesame seedsand mustard seeds, which are consumed and processed or simplycooked. Oilworld.biz, an analyst specialising in the vegetable oils

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business, comprehensively analyses the crushing of 10 majoroilseeds, 17 major oils and 12 major meals, including cottonseed oil,fish oil, corn oil, palm and palmkernel oil, butter and lard. Localtastes and GDDs drive the local markets, and deficits and surplusesare imported or exported. Rapeseed oil is preferred for its taste inChinese cooking and it is by far the largest rapeseed market in theworld.

The complexity of the soybean business is perhaps best illustratedby the soybean product tree (see Bell, David E., and Mary L.Shelman, 2006, “Bunge: Poised for Growth”, Harvard BusinessSchool Case 506–036, July), which shows that the crushing plantcomplex is quite large and comparable to a petroleum refinery if allthe various streams are included. In North America and the EU, thecrushing plant will supply downstream processors such as Solae (aBunge DuPont joint venture) for further processing. In Brazil, theindustry is still evolving to develop the various processed productstreams and many of these crushing plants are truly biomass opera-tions – for instance, the plant being built on 10,000 Ha of which 20 Hais the actual plant, bottling and bagging, trucking, warehousing andlogistics, and the balance is producing eucalyptus trees which areharvested and used to power the plant and its various services.

What we should note at this point is these plants do not suddenlyappear, they are planned in advance and the crushing equipment isordered in advance. The storage facilities for vegoil are quite tech-nical, and meals are not without their complexities due to theirphysical characteristics. Fundamental analysis includes the fore-casted change in crush and downstream capacity by location andtype of operation. Compared to petroleum refineries, they are cheap.Impressive worldscale operations are built for US$0.2 billion ratherthan US$2.0 billion. Within the study mentioned above, at that timeBunge was the world’s biggest soybean crusher, and the expectationfor soybean crush evolution by geography is given. They expectedthe 2010 crush of soybeans to look like US/Arg/Braz/EU/China as52/25/30/17/25 (149 MT total), respectively, when in fact it lookedmore like 45/38/40/12/76 (211 MT total), respectively. The forecastmissed both the size and geography of actual growth. The error was–7/13/10/–5/51 (62) or 87/152/133/71/304 (142)% of forecast.Although these would have been constantly revised by Bunge, itdemonstrates the ease of making a substantial error and consequentdifficulty of building for the future. In fact, the marketplace did


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generally underestimate China’s appetite for soybean imports andthe desire to crush them domestically, as it did the Chinese growthstory for all commodities.

We also have to remember that once capacity is built it is normallyrun, in any industry, at any contribution to the bottom line thatexceeds variable cost. The different regional growth patterns implydifferent rates of port development to move the commodities, as wellas different rates of growth in downstream and associated indus-tries. A further unexpected agribusiness consequence, but one whichthe petroleum business is quite familiar with, was the building ofstrategic reserves of foreign currency held in commodities by China– look at their enormous stocks of soybeans and oilseed rape, 16 and6 MMT, respectively.

The Chinese domestic oilseed complex growth has been virtuallystagnant since the early 2000s growing from 55 to more than 58MMT, within which soybeans contracted by 2 MMT, while rapeseed,cottonseed and groundnuts increased. Soybean imports grew from21 to almost 60 MMT, and rapeseed imports grew by more than 2.5MMT, so that crush now stands at soy 61 MMT, rapeseed 15 MMT,sunflower 1 MMT, cottonseed 10 MMT and groundnuts 7 MMT, fora total crush of 96.0 MMT – the biggest in the world. Add to that the24 MMT of oilseeds consumed other than through full crush (partialprocessing), of which 11 MMT is soybeans, 3 MMT is cottonseed and8 MMT is groundnuts, and we see a better picture of the 120 MMTChinese oilseed powerhouse, consuming far more than any of thebig three producers. In the early 2000s, they carried oilseed stocks ofalmost 19 MMT in China, and in 2013 stocks stand at an estimated 24MMT. The USDA estimates that if it costs US$100/MT to movesoybeans from Iowa to Chinese ports, it costs US$175 from MatoGrosso in Brazil. This means the expansion of soybeans in Brazil isdisadvantaged at the farmgate by that amount, and this inevitablyleads to Brazil finding other uses for soybeans until transport effi-ciencies can be de- bottlenecked.

On the soybean demand side, 10 years is also a long time and therapidly changing face of Brazilian agribusiness is well illustrated bythe emergence of JBS on the world stage. JBS is the largest Brazilianmultinational food processing company, producing fresh, chilledand processed beef, chicken and pork, and also selling by- productsfrom the processing of these meats. This has lead to a surge inBrazilian soybean consumption domestically. In a decade, its

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domestic meal consumption has grown by 5 MMT to 12.5 MMT.Argentine meal consumption in the same period grew from 0.25MMT to almost 2.0 MMT.

JBS has established itself as the largest global company in the beefsector with the acquisition of several retail chains and food compa-nies in Brazil and around the world, especially the 2007 US$225million acquisition of US firm Swift & Company, the third- largest USbeef and pork processor, renamed as JBS USA. It leads the world inslaughter capacity, at more than 50,000 head per day, and continuesto focus on production operations, processing and export plants,nationally and internationally. With the new acquisition, JBS enteredthe pork market, featuring an impressive performance in thissegment, to end the year as the third largest producer and processorof this type of meat in the US. The acquisition expanded thecompany’s portfolio to include the rights for worldwide usage of theSwift brand. The following year, JBS acquired Smithfield Foods‘ beefbusiness, which was renamed JBS Packerland. JBS’s productionstructure is embedded in consumer markets worldwide, with plantsinstalled in the world’s four leading beef producing nations – Brazil,Argentina, US and Australia – serving 110 countries throughexports. In September 2009, JBS announced that it had acquired thefood operation of Grupo Bertin, one of three Brazilian marketleaders, consolidating its position as the largest beef producer in theworld. On the same day, it was announced that the company hadacquired 64% of Pilgrim’s Pride for a bid of US$800 million, estab-lishing JBS’s position in the chicken production industry. In August2010, it was reported that JBS was trying to sell some of the eightslaughterhouses it owns in Argentina because of “scarce livestockand export restrictions.” By 2011 they were attempting to gaincontrol of Sara Lee Corporation‘s meat business.

Brazil has aquaculture production targets of 1.0 MMT by 2015 and10.0 MMT by 2020 from a base of 0.5 MMT in 2011. While this may betoo high to achieve by 2020, we can easily imagine them managing itby 2025, again reshaping soya and (non- vegetable) protein flows. It isworth looking back at Table 7.5 to understand the significance of thisnumber.

In 2003, the unthinkable happened in the soya world: there wereterrible crop yields in the US, Argentina and Brazil, all in the sameyear. Forecasters expected a rising yield, but the US dipped from 2.56


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to 2.28 MT/Ha, Argentina from 2.82 to 2.36 and Brazil from 2.82 to2.37. In 2002, Argentina and Brazil out- yielded the US for the firsttime, and their combined production matched US. For the first time,not only did global soybean production not grow, it dipped by some10 MMT. This sparked an unprecedented rally which had long- termeffects on how the markets traded, who dominated them and howthe Chinese thought about their soybean strategy. In the author’sopinion, this drop may be partly attributed to the illegal spread ofGM seeds in Latin America at a time when the technology was newand certainly undeveloped for Latin American conditions. Critically,it demonstrated that yield advancements came with increasing yieldvariability and unexpectedly large sensitivity to weather variations.The US saw 0.5 standard deviation changes in GDD’s give far biggerswings in yield than history would have lead us to expect.

For those who follow freight markets, part of China’s soybeanimporting strategy has been to add Chinese tonnage to the global drybulk market, since they are structurally short, causing a sharp down-ward correction in freight prices.

HOW ROTATION CONVERGES THE GRAINSAs a major source of income for trading companies and hedge fundsalike (see Appendix 7.1), and definable by excellent fundamentalanalysis, we can “arbitrage” maize, wheat and soybean prices. In theshort run, one can reasonably expect these three commodities tochange price relative to each other, to reallocate or switch hectaresbetween crops and hemispheres. We can always bring more landinto production, but in Brazil, for example, that involves a year ofland clearance of indigenous plants before a year of growing rice andclearing the land, and then a serious commercial crop can be startedin the third year. Table 7.9 shows an interesting view of the majorcrop economies in a side-by-side comparison of total arable land flex-ibility and individual crop flexibility. The major opportunities withexisting resources, in terms of area, are all within Table 7.9. Theserious student should understand this one table representation offlexibility in both percentages and individual crops as well as theyield gaps presented in the various tables for the major crops, bycountry.

The US and the EU-27 are the most economically responsive areasor “rational actors” to relative price, by which we mean per-hectare

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COMMODITYIN

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Table 7.9 Rotation flexibility by major grain economy in 20 years (total arable area and min/max percentages by major crop)“Swingable hectares” is total area times (max minus min); current percentage devoted to each crop is given by country and world

Area Maize Barley Sorghum Wheat Rice Soybeans Rapeseed Sunseed

MHa Min Max Min Max Min Max Min Max Min Max Min Max Min Max Min Max

US 90 32% 41% 2% 6% 22% 34% 28% 38%EU-27 60 9% 17% 21% 34% 40% 46% 6% 12% 6% 15%Argentina 30 9% 17% 2% 5% 11% 38% 31% 68% 6% 19%Brazil 45 28% 46% 3% 11% 5% 15% 32% 59%China 105 22% 33% 23% 31% 29% 34% 7% 10% 6% 8%India 90 31% 34% 48% 57% 3% 12% 6% 9%CIS/FSU 85 2% 9% 16% 38% 53% 64% 5% 17%

“Swingable hectares”US 7.9 0.0 3.5 10.9 0.0 8.8 0.0 0.0EU-27 4.8 8.1 0.0 3.8 0.0 0.0 3.8 5.2Argentina 2.2 0.0 0.9 7.9 0.0 11.3 0.0 4.0Brazil 8.3 0.0 0.0 3.2 4.5 12.4 0.0 0.0China 12.4 0.0 0.0 8.5 4.7 3.6 2.7 0.0India 0.0 0.0 0.0 2.7 8.7 7.6 2.7 0.0CIS/FSU 5.3 18.6 0.0 9.6 0.0 0.0 0.0 9.6Theoretical total 41 27 4 47 18 44 9 19

Current % Maize Barley Sorghum Wheat Rice Soybeans Rapeseed SunseedUS 40% 0% 2% 22% 0% 35% 0% 1%EU-27 16% 22% 0% 44% 0% 0% 11% 7%Argentina 12% 0% 4% 11% 0% 66% 0% 6%Brazil 31% 0% 0% 4% 5% 59% 0% 0%China 33% 0% 0% 23% 29% 7% 7% 0%India 0% 0% 0% 33% 48% 12% 8% 0%CIS/FSU 9% 17% 0% 59% 0% 0% 0% 15%

Current % world 21% 6% 5% 28% 20% 13% 4% 3%

Source: Agrimax.

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income. Since the early 1990s the US has planted as little as 32% andas much as 41% of its 90.0 MHa of arable land to maize, 2–6% tosorghum, 22–34% to wheat and 28–38% to soybeans. At the lastcount, the US were at maximum on maize, 35% on soybeans andminimum on wheat. This trend will continue with more ethanol(maize) produced and less land available for wheat and soybeans.Wheat area is the most switchable, and surged 3.0 MHa in 2003.Soybean hectares surged almost 2.5 MHa in 1997 in response to theFreedom to Farm Act. Over the 20 years, total land area onlyincreased by 6 MHa. With the threat (or reality) of E15, it is expectedthere will be more maize at the expense of wheat.

By contrast, the EU-27 has 60.0 MHa in grains and oilseeds up byalmost 20.0 MHa's in 20 years, with maize swinging between 9% and17%, wheat between 40% and 46%, barley between 21% and 34%,rapeseed between 6% and 12% and sunseed between 6% and 14%. Atthe last count, the EU-27 was close to maximum on wheat and rape-seed, average on sunseed and close to bottom on barley.

Argentina and Brazil till some 30 MHa and 46 MHa, respectively,with each having grown from 15.5 and 30.5 since the early 1990s.Argentina is more rotationally complex, with 10–17% maize, 2–5%sorghum, 11–38% wheat, 31–68% soybeans and 6–19% sunseed.Brazil is 28–46% maize, 3–11% wheat, 5–15% rice and 32–59%soybeans. Latterly, Argentina has been in the middle on maize, at thehigh end for sorghum, at the bottom end for wheat and all the way tomax on soybeans and at minimum for sunseed. Brazil was close tominimum for maize, bottom end for wheat and rice and, likeArgentina, at max for soybeans.

China, with 103 MHa under tillage, is almost unchanged in areasince the early 1990s (+5 MHa), and can swing 22–33% on maize, 23–32% on wheat, 29–34% on rice, 7–11% on soybeans and 5–8% onrapeseed. At the last count, it was max on maize (to blend withimported soybeans), minimum on wheat, rice and soybeans andclose to max on rapeseed. The main China growth story is meatproduction – pork and chicken – with high FCE. A high FCE requiresa singular focus on “maize-plus-soymeal” diets, for physical flowa-bility or product handling as well as nutrition.

India, with more than 90 MHa in tillage, swings only 31–34%wheat, 48–57% rice, 4–12% soybeans and 6–9% rapeseed. Food secu-rity points to more wheat over time but much of this is going to go to

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more intensive large- scale farming. Indian productivity per hectarehas only one way to go: up.

The CIS/FSU has some 85 MHa under tillage and dismal yields.Maize farming should be declining and swings 2–9% (currently atmax), wheat 53–64% (currently in the middle), 16–38% barley(currently at the low end) and 5–17% sunseed, which is now at thehigh end. We expect to minimise barley and maximise sunseed andwheat for the foreseeable future.

It would be nice to make a big deal of Argentina and Australia, butthis is not realistic. They do not have the land mass or yields and so,even if Canada is max on rapeseed at 1.0 MHa, it simply does notmake a global difference. At this time, it is max on rapeseed andminimum on wheat. Australia is dryland farming with sporadic rain,so unreliable. The “call- like” planting of Australian wheat meansthey will continuously plant, from year to year, and hope for rain justas Texas does in the US.

Area times yield equals production. The most populous countrieshave the land pretty much tapped and China has done tremendouswork on yield. The baton falls to India to improve crop husbandry.Brazil has land in abundance but infrastructure is so tight and expen-sive that it is likely to continue its domestic trend toward more meatand aquaculture production. This would expand its export capacityby displacement, just as it now moves vast quantities of sugar bycontainer to the export market. The major opportunities with existingresources in terms of A are all within Table 7.9 and the serious studentshould understand this one table representation of flexibility in bothpercentages and individual crops as well as the yield gaps presentedin the various tables for the major crops, by country.

SUMMARY OF MAJOR TRENDS AND SWING FACTORS FORTHE FUTUREIf one thing alone has changed the grain markets completely sincethe early 1990s and is likely to continue to do so, it is undoubtedly theUS corn- based ethanol programme. It remains phenomenally diffi-cult to change commercial US law once it is in place other than byincremental amounts. If cellulosic ethanol arrives it will change theworld forever and cause grain prices to collapse. However, itappears to be no closer in terms of substantial economic reality thanwe saw in the early 2000s.


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If there is a second thing that has also changed the grain marketscompletely during this period, it is the manner and rate at whichCPGs are growing to dominate our increasingly urbanised foodconsumption. At the time of writing, China’s ShuanghuiInternational has just bought Smithfield Foods, the huge US- basedbut globally active pork and meat company, for US$4.7 billion. Theneed for modern food processing safety, branding and packaging,and all the required supply chain management skills, has rendered itmore cost effective “to buy it rather than build it”.

If there is a third thing that must happen over the next few years, itis the intensification of agriculture – for the cost of bringing morearea into production has become much more expensive than mosthad anticipated.

Since maize combined with soybean meal is the cornerstone ofmodern animal (and soymeal for aquaculture) nutrition, much moregrain will be consumed in Brazil and exported as meat. China andthe US have some 34 MHa under maize, and both will increase area.Also, Chinese yield will move towards the US (there is a 3 MT/Hagap, see Table 7.3), just as China did with the EU in wheat (see Table7.6). The maize market into the 2020s will remain fundamentallytight and expensive. E15 will take more corn to the fuel tank,although there are some real costs being discussed at the retail petrolstation level where the retail supplier is pushing hard to stay at E10or go to E15, but not carry both. This would require adding pumps,tanks, trucks and re- branding – all expensive items. Brazil will exportmore maize than the US consistently. The only two things that cancause maize demand to break to the downside are a dramatic u- turnin US energy policy (1:100) or a breakthrough in cellulosic ethanol(1:50). Even a dramatic fall in crude oil prices would only stimulatemaize demand for the gasoline pool as it worsens the economics forcellulosic ethanol. Economics says Brazilian ethanol should continueto flow in ever- greater quantities to the US, but it may not become apolitical reality.

It is ironic that the CIS/FSU has a higher barley than wheat yield,something almost impossible in terms of modern farming. TheCIS/FSU has the greatest potential to increase yield through intensi-fication and plant breeding, and has some 49 and 14 MHa underwheat and barley, respectively. Any area reductions will be offset byincreased commercialism of these two markets inside Russia, from

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farmgate to consumer. Wheat will continue to assume the role ofprimary determinant of grain prices globally as its volume isincreasing while US maize volumes decline, net of ethanol. India willbecome a consistent importer of wheat and withdraw from theexport market into the early 2020s.

The Chinese (Asian) and Indian (sub- continent) appetites for soyawill continue as meat and fish demand increase. The CPG intensifica-tion of their food systems will also increase, along with urbanisationand wealth. At 11 MMT of soybeans and 7 MMT of rapeseed, soft-seed production in India is growing rapidly, and significant importswill come in time.

We have been waiting for palm oil production to reach amaximum in Malaysia and Indonesia, but it continues to increase. Atsome point this must happen and will create more pressure forglobal soybean area to increase.

In terms of AYP, we will continue to see area expand slowly butyield to expand at more impressive rates (see Table 7.1). In fact, theauthor is optimistic it will be much higher.

APPENDIX 7.1: AGRIBUSINESS INVESTORSThe ag investing “funds” are listed below.

Commodity-specialist funds: Ospraie, Ospraie Wingspan,�

Touradji, BlackRiver, Armajaro, etc;Global Macro funds: DE Shaw, Soros, etc;�

Pension funds: APG, Calpers, BT, Hermes, TIAA- CREF, etc;�

Sovereign Wealth funds (all EM- based and EM in focus): Kuwait�

Investment Authority, National Bank of Dubai, SinoLatinCapital, etc;Private Wealth aggregators: Barclays Global Investors (now�

BlackRock), GSAM, Adecoagro (Soros), etc;Index funds: the GSCI, DJ- AIG etc index funds and their�

hedgers, etc, as well as Schroders in ags;The Mega funds (ABC): Ashmore, Blackrock, Carlyle, etc;�

Managed Futures industry: self explanatory;�

Endowment funds: Harvard, etc; and�

Private Equity: BlackRiver (Cargill), The Mega Funds, Louis�

Dreyfus (Calyx Agro) and the L- D family, as well as PAI and anendless list stretching to CP (Charoen Pokphand) and Glencore.


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This chapter will provide the risk management professional with anorientation to understand the oldest and oddest of energy markets:coal. It will explain the physical characteristics of coals, coal marketstructure and dynamics, and the coal price indexes and tradingvenues used for transacting financial derivatives. The chapter willalso cover key developments in the fundamentals of coal along withan understanding of the broad range of instruments available tomanage risk in that market, and will provide an overview of marketdrivers and their interaction, as well as offer an initial reference forthe detailed data needed to analyse the coal market.

OVERVIEWCoal seems to be the unwanted stepchild of the energy world: dirty, old- fashioned, not really popular anymore. Who cares? On the otherhand, those who do care a lot often seem to echo the famous words ofa White House adviser on energy and the environment:

“A Harvard University geochemist who serves as a scientific adviserto President Obama is urging the administration to wage a ‘war oncoal.’

‘The one thing the president really needs to do now is to begin theprocess of shutting down the conventional coal plants,’ Daniel P.Schrag, a member of the President’s Council of Advisers on Scienceand Technology, told the New York Times. ‘Politically, the WhiteHouse is hesitant to say they’re having a war on coal. On the otherhand, a war on coal is exactly what’s needed.’”1

Trends in worldwide coal consumption indicate that ProfessorSchrag’s war is going badly:

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8

CoalJay Gottlieb


“Coal consumption grew by 2.5% in 2012, well below the 10-yearaverage of 4.4% but still the fastest- growing fossil fuel...Coal reachedthe highest share of global primary energy consumption (29.9%)since 1970.”2

Coal has driven global development since the British industrial revo-lution, beginning in the 18th Century with the harnessing ofincreasing amounts of coal- fired steam power for transportation andsteel production. The role of coal- fired steam in transportation andmanufacturing along with the use of coking coals in the productionof steel is familiar. While other fossils remain a big part of people’sdaily lives – petrol for cars and natural gas for home heating andcooking – coal has largely receded from view. It works away quietlyin the industrial background. While coal is no longer used locally fortransportation or building heat, it is still consumed as a key compo-nent in steel and cement production and fuels around 40% of theworld’s electric power generation.

Coal is found abundantly around the world, is relatively easy toproduce with existing mining technologies and can be transportedthrough a wide variety of modes, such as conveyor belt directly frommine to power plant, or through combinations of truck, rail, bargeand ocean- going freighter. As transportation infrastructure devel-oped around the world since the 1960s, prices for bulk transportationdeclined and coal changed from a commodity with only a localregional reach and economics to one that is traded similarly to other highervalue energy commodities, flowing around the world fromproduction areas to wherever it commands the highest value inconsumption. Along with the explosion of transportation options,coal consumers have become much more sophisticated in managingtheir power plants to run on a greater variety of coals, adjusting forphysical and chemical differences in coals from divergent sources.

The major exporters of coal are Indonesia, Australia, South Africa,Colombia, US and Russia. China and Europe are the majorimporters. While the exact numbers will of course change from yearto year, the major participants will not.

CHARACTERISTICS OF COALSo, let us return to the question, “what is coal?” It is an energy- richsource of carbon that is relatively easy to find, mine and transport,but is also bulky and heavy relative to its energy value. Also, coal


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comes with many other non- carbon components that must becontrolled to limit pollution and other unwanted emissions frompower production and other consumption.

Coal is a combustible sedimentary organic rock consisting of morethan 50% carbon by weight. It is a fossil fuel derived from plants thatgrew in swamps that were later buried by sediments. Geologicalprocesses compressed and heated the plant remains over vastperiods of time, producing various ranks (or categories) of coal.With increasing rank, coal becomes harder, brighter and the heatcontent is higher. The ranks from lowest to highest are: peat,brown coal, lignite, sub- bituminous, bituminous and anthracite(listed in Table 8.1).

While coal is chiefly comprised of carbon, hydrogen and oxygen,it also contains varying amounts of sulphur, nitrogen and otherelements. Coal quality varies a great deal and is priced based onthese characteristics. The heat content is the key value of thecommodity for electricity generators and cement producers, whileother characteristics are important for steel producers. Disposing ofthe non- desirable components, particular sulphur and nitrogen,adds cost to the consumption of coal.

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Peat Wet plant material that has been subject to bacterialand fungal action, very low energy level, moisturelevel ~60% calorific value ~2,600 kcal/kg

Brown coal Peat that has had the water squeezed out, plantremains still visible moisture ~50%, calorific value2,800 kcal/kg

Lignite Coal is hard and massive, black looking, moisturecontent 40–50%, calorific value about 4,000 kcal/kg

Sub- bituminous Coal is hard, brittle, black and shiny, moisture contentis 20–40%, calorific value 4,000–5,800 kcal/kg

Bituminous Coal is softer and shiny, moisture content is 8–20%,calorific value is 5,800–8,000 kcal/kg, crucibleswelling number from 2–9+ possible for coking coals,volatile matter 16–40%

Anthracite Coal is very shiny, repels moisture, calorific value7,800–8,000 kcal/kg, no coking properties

Table 8.1 Coal rank description


Heat content is measured as the heat produced by combustion of aspecified quantity of the fuel when burned at a constant pressureunder controlled conditions for water vapour. It is measured interms of either British thermal unit (Btu) per pound in the US or kilo-calorie per kilogram (kcal/kg) internationally. In all cases, higherheat content is preferable to lower.

Thermal coal fires power generation plants, and metallurgical (ormet) coal is used for steel production. We will now look at whichcoal qualities are of importance in thermal and metallurgicalconsumption.

Thermal coalMost coal is used for the energy content within the volatile matterand the fixed carbon. These coals are generically termed “thermal”(or steam) coals and are mostly used for electricity generation. Atypical Australian thermal coal contains 6,080 kcal/kg of usableenergy (net as- received energy) or 25.46 megajoules/kilogram(MJ/kg) of coal. Electrical energy (power) is measured in wattswhich are joules per second, therefore one kilowatt hour of electricity(one unit) converted from coal at 35% efficiency requires 10.286 MJ ofcoal energy every hour, or 0.404 kg of coal. Other thermal coal usesare the calcination (breakdown by heat) of limestone to form cementfor construction industries or lime for agricultural purposes.Hospitals and other institutions use coal for process heat, as do abat-toirs, wool sours and timber- drying processes.

Metallurgical coalFor steel and other metallurgical production, certain bituminouscoals are particularly suited to release gaseous components, calledvolatile matter, when heated to extremely high temperatures in theabsence of oxygen. When these special bituminous coals swell onheating above 3500C and release their volatile matter, they leavebehind a hard porous carbon residue called coke. These coals arecalled coking coals and are limited in their occurrence around theworld. Coking coals are primarily used to make coke that, underhigh temperatures, reduces metal oxides to metals. This processoccurs when the coke is combined with the metal oxides at elevatedtemperatures. The carbon from the coke combines with the oxygenfrom the metal oxides to produce carbon dioxide, liquid metal and


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residual ash (slag). The coals most suitable for producing cokecommand the highest prices on the world market.

Sulphur content is always undesirable. Creating air pollutionwhen the coal is burned, sulphur emissions must be controlled withexpensive technologies. Laboratory analysis of sulphur content aspercentage of total weight of coal is typically adjusted for the heatcontent of a ton of the coal for pricing purposes, as regulatory stan-dards are based on how much sulphur is emitted per ton of coalburned.

High- rank coals are high in carbon and therefore heat value, butlow in hydrogen and oxygen. Low- rank coals are low in carbon buthigh in hydrogen and oxygen content.

TransportationMore than any other energy commodity, transportation costs are amajor component of the cost of fuel delivered to the end- user. This isa simple result of coal’s high bulk and weight relative to its value.The high cost of transportation and rigidities in the transport infra-structure impact the markets for coal. Coals are typically pricedeither free on board (FOB) at mine origin, or cost, insurance andfreight (CIF) at the consumer’s destination, with either the consumeror producer responsible for arranging and paying for transportationfrom or to that point. There are no intermediate collection points andfew wholesale marketing points. Train shipments are difficult, if notimpossible, to re- schedule and redirect, so there is very little tradingof physical coal once it is en route to an ultimate destination, unlikethe vast amount of trading of oil tankers. Seaborne coal markets arewhere the most active trading occurs, because of the greater flexi-bility and relative low cost of moving a bulky item across the waterversus across land.

Coal mines are either surface (open pit) or underground.Transportation from the mine can be done through a number ofmodes, but again the low value- to- weight ratio makes minimisingthe physical handling of coal the key to cost efficiency in transporta-tion. Depending on distance and mode of transport, transport costsfor delivered coal range from 20–70% of delivered price to the ulti-mate consumer, a major component of the total cost of coalprocurement.

Coal can be moved directly from source to end- user via truck for

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distances of less than 100 miles. For longer distances, rail or water- borne transport is typically used. Coal can also be trans- shippedfrom rail or truck into river barges or ocean- going vessels. For otherthan international export, no more than two trans- shipments wouldbe used, as it is important that transportation mode changes add aslittle cost as possible. Therefore, coal goes from mine to end- userwith few intermediate transactions.

Historically, coal sold under longterm supply contracts with lesstrading than other commodities – due to high capital costs mirroredon both the production side (mine and transportation development)and the use side (power plant construction). Since many of themines, transportation networks and generation plants have been putin place and their capital costs are amortised, the economics allowsfor shorter deals. In addition, consumers have learned to be muchmore flexible in sourcing, which enables coals to compete amongeach other and against other fuels. Consequently, markets havebecome more dynamic. Trading and risk management tools havealso grown to match that flexibility. An increasing proportion of coalis sold on the spot market and priced off of indexes. This is what hasstimulated the growth of derivatives trading.

Cheaply mined and having relatively low heat content (and alsolow sulphur content), Powder River Basin coals are shipped by railfrom Wyoming to west coast ports and then on to Asia. Eastern UScoals can change modes several times, from mine by rail or truck toriver barges and then out to Europe through loading on ocean- goingvessels in the New Orleans area, or directly by rail to ports on the eastcoast. Once seaborne, coals from Australia and South Africacompete with the US coals for markets in Europe and Asia. Theconsumer purchases the coal based on a limited number of heatcontent and quality variables against the price delivered to theirpower plant. Thermal coal has become for the first time a truly worldcommodity, a fact that is reflected in the growth of derivativestrading.

Bituminous coal is typically much more expensive to mine, has upto 50% greater heat content and thus significantly lower transporta-tion costs, and can be environmentally friendly, commanding higherprice at the mine. As mentioned above, sub- bituminous coal, such asfrom US Powder River Basin, has lower heat content and transporta-tion costs as much as 50% greater – with long, overland rail


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transportation to end- users or export terminals – but has low miningcosts due to thick seams of easily accessible coal through surfacemining, and often has lower sulphur content. This makes itextremely competitive, even in world markets, and significant exportcapacity on US west coast is under development.

MARKET STRUCTUREWorldwide, most coals are priced on a per ton basis. In the US,however, many utilities prefer to buy on a price based on heatcontent rather than weight, in million Btu (MMBtu).

Prices are measured by many indexes that are transparent andreliable, and have allowed the growth of derivatives trading basedon them. In the past, published prices rarely changed and weretotally unreliable for any contracting or trading. Little spot tradingoccurred and longterm contracts included negotiations of manyfactors, particularly free supply options for the buyer, which madeprice comparison across time or contracts meaningless. For thesereasons, active physical and financial trading of coal was slow todevelop, but has become fully integrated into the energy riskmanagement environment.

A joint venture between an energy market news organisation,Argus Coal Services, and a coal industry economic and managementconsulting firm, IHS McCloskey, produces the API indexes, whichare the standard industry benchmarks. The main focus for activity inthe coal derivatives market is the API 2 index, which consists of anaverage of the two firms’ price assessments for coal imported intoAmsterdam, Rotterdam and Antwerp, and includes CIF. Anothermajor index is API 4, which is the benchmark for coal exported fromRichards Bay in South Africa and also incorporates CIF. Argus esti-mates that more than 90% of the world’s coal derivatives are pricedagainst these indexes. The list below describes the key indexes usedfor international physical and derivatives coal business.

API 2 index: the industry standard reference price used to trade�

coal imported into northwest Europe. The index is an average ofthe Argus CIF Rotterdam assessment and McCloskey’s north-west European steam coal marker.API 4 index: the price for all coal exported out of Richards Bay,�

South Africa. The index is calculated as an average of the Argus

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FOB Richards Bay assessment and McCloskey’s FOB RichardsBay marker.API 5 index: the price for exports of 5,500 kcal/kg net as received�

(NAR), high- ash coal from Australia. The index is calculated asan average of the Argus FOB Newcastle 5,500 kcal/kg assess-ment and the equivalent from IHS McCloskey.API 6 index: this index represents 6,000 kcal/kg NAR coal�

exported from Australia. It is calculated as an average of theArgus FOB Newcastle 6,000 kcal/kg assessment and the equiva-lent from IHS McCloskey.API 8 index: the price for 5,500 kcal/kg NAR coal delivered to�

south China. It is calculated as an average of the Argus 5,500kcal/kg cost and freight (CFR) south China price assessment andthe IHS McCloskey/Xinhua Infolink south China marker.

The publishing schedule for these widely used indexes are asfollows:

Weekly average coal price:�

Northwest Europe (CIF ARA) API 2 index;�

South Africa (FOB Richards Bay) API 4 index;�

Australia (FOB Newcastle) API 5 index;�

Australia (FOB Newcastle) API 6 index; and�

CFR south China API 8 index.�

Monthly coal price: API 2, API 4, API 5, API 6, API 8 indexes; and�

Daily coal price: API 2, API 4 indexes.�

These prices are available exclusively through the Argus/McCloskey’s Coal Price Index service.

FINANCIAL MARKETS FOR COALVirtually all markets are served by multiple over- the- counter (OTC)-cleared standardised derivatives contracts. Multiple platforms offerproducts on the same indexes. OTC trades are cleared on twocompeting platforms: CME/Nymex and the IntercontinentalExchange (ICE). The types of coal futures for each exchange are listedbelow (as of June 2013). The exchanges also list options and strips formost of these futures.


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CME coal product slateThermal coal productsGlobal:

MTF: Coal (API 2) CIF ARA (Argus/McCloskey);�

MFF: Coal (API 4) FOB Richards Bay (Argus/McCloskey);�

ACM: Coal (API 5) FOB Newcastle (Argus/McCloskey);�

NCL: Coal (API 6) FOB Newcastle (Argus/McCloskey) futures;�

SSI: Coal (API 8) CFR South China (Argus/McCloskey) futures;�

MCC: Indonesian coal (McCloskey sub- bituminous futures); and�

MC6: Indonesian coal (McCloskey sub- bituminous 6,000kcal�

basis) futures.

US domestic:QL: Central Appalachian coal futures;�

QP: Powder River Basin coal (Platts OTC broker index) futures;�

andQX: CSX coal (Platts OTC broker index) futures.�

Coking coal productsALW: Australian coking coal (Platts) low- vol futures;�

ACR: Australian coking coal (Argus) low- vol futures; and�

ACL: Australian coking coal (Platts) futures.�

ICE coal productsUCA IFEU Coal: Central Appalachian coal futures;�

CRF IFEU Coal: CFR South China coal futures;�

UCX IFEU Coal: CSX coal futures;�

NCF IFEU Coal: gC Newcastle coal futures;�

SUB IFEU Coal: Indonesian sub- bituminous coal futures;�

UCP IFEU Coal: Powder River Basin coal futures;�

AFR IFEU Coal: Richards Bay coal futures; and�

ATW IFEU Coal: Rotterdam coal futures.�

SUPPLY, DEMAND AND REGULATIONRegulationEnvironmental concerns and regulation often create significantimpacts and uncertainties around the supply and consumption ofcoal. Specific areas of close regulation include the following.

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Supply: mountaintop removal and water- course impacts for�

mining; construction of transportation facilities such as majorrail improvements or development of export terminals.Consumption: emission of carbon, sulphur oxides and nitrous�

oxides on the consumption side; retrofitting of new control tech-nologies and purchase of emission allowances and credits.

Supply and tradeMinerals mining companies focused primarily on coal extract themajority of the produced coal. Such companies range from nationalproducers to international corporations, as well as many smallercompanies. While it used to be very common, particularly in theAppalachian region of the US, for companies to be formed to ownand operate just a single mine, much of the industry has takenadvantage of economies of scale that have resulted in a greaterconcentration of ownership in larger corporations. Consequently, short- term spikes in price can occur due to strikes, labour shortages,transportation bottlenecks and mining problems at the larger mines.

Typical production cost increases in major coal exporting coun-


216

Figure 8.1 Largest coal exporters annual exports (thousand short tons)

Source: EIA, international energy statistics

400,000

350,000

300,000

250,000

200,000

150,000

100,000

50,000

02000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

IndonesiaAustraliaRussiaUnited StatesColombiaSouth Africa


tries outside the US have increased by around 200% since the late2000s. These dramatic rises in cost vary across production areas andare due to a wide variety of reasons. The main impact has been toincrease the integration of worldwide coal markets as producers lookfor more extensive markets and consumers search for competitivepurchasing opportunities.

Figure 8.1 shows the changing landscape of the top global coalexporters. Almost half of Australia’s exported coal goes to metallur-gical use, mainly in Asia and Europe, with Japan, India, China andSouth Korea being the main Asian importers. Japan is also the largestbuyer of Australian thermal coal. The US and Canada export signifi-cant quantities of metallurgical coal, but thermal coal comprisesmost of Indonesia’s rapidly growing export volumes. China,South Korea, India and Japan are the largest importers of US coal.Figure 8.2 shows the distribution of recoverable reserves for coalglobally, while Figure 8.3 displays the trends for the largestimporting countries.

ConsumptionWhile there are other important trends in coal demand, such ascontinued growth in India’s consumption and imports, China alonehas dominated global consumption and demand growth. Again

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Figure 8.2 World recoverable coal reserves (861 million tons)

Source: BP, June 2013, “Statistical Review of World Energy”

Other26%

US28%

China13%Australia

9%

Russian Federation18%

Indonesia6%


quoting from BP, “Statistical Review of World Energy” (June 2013)regarding 2012 annual growth in coal use:

“Consumption outside the OECD rose by a below- average 5.4%;Chinese consumption growth was a below- average 6.1%, but Chinastill accounted for all of the net growth in global coal consumption,and China accounted for more than half of global coal consumptionfor the first time. OECD consumption declined by 4.2% with lossesin the US (–11.9%) offsetting increases in Europe and Japan. Globalcoal production grew by 2%, with growth in China (+3.5%) andIndonesia (+9%) offsetting a decline in the US (–7.5%). Coal reachedthe highest share of global primary energy consumption (29.9%)since 1970.”

Thermal coal consumption in the US has decreased since 2008compared with increasing consumption in Asia and Europe. In theUS, natural gas continues to displace more and more coal generationdue to the costs of upgrading old coal plants to meet ever- higheremission standards, and the low cost of gas due to greater expansionof supply.

The relationship between natural gas prices and coal prices forpower production has driven coal markets like never before. The


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Figure 8.3 Largest coal importers annual imports (thousand short tons)

Source: EIA, international energy statistics

250,000

200,000

150,000

100,000

50,000

02000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Japan

China

South Korea

India

Taiwan


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Figure 8.4 EIA historical and forecast annual coal consumption (quadrillion Btu)

Source: EIA, international energy statistics database (as of November 2012), and “EIA Annual Energy Outlook 2013” (base case).

100

90

80

70

60

50

40

30

20

10

02005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

ChinaUnited StatesOECD EuropeIndiaOECD AsiaRest of World

08 Chapter C

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glut of natural gas in the US in 2012 drove natural gas prices to a levelwhere, in July 2012, for the first time in history US electricity produc-tion from gas- fired plants was equal to that of coal- fired plants.Contrast this with the early 1990s, when coal represented more than50% while gas represented roughly 5%. At prices above US$3.50/MMBtu for gas, coal becomes competitive again. Gas- fired powerdisplaced US coal in the international markets, where the cheap coalsignificantly increased European coal- fired generation at the expenseof their natural gas plants.

China’s consumption growth comes largely from increasingpower generation. China has large domestic coal reserves, but it willalways take advantage of low import prices and significantlyincrease imports appropriately. Since US demand has been downdue to the explosion of inexpensive supplies of natural gas, Chinahas imported US and other coals while reducing domestic produc-tion. When demand and prices increase in the US domestic markets,China will rely on its own production again.


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PANEL 8.1: FUEL TO POWER SPREADS

A key ingredient in most liquid derivatives markets is the trading of spreadsbetween one instrument and another. In effect, most commodity trading isbased on the differential between two (if not more) prices. Few traders takeon outright risk, but most do choose very specific relationships where theyhave developed expertise and expect that they can both recognise certaintrends before the market has fully taken them into account and can, in anyevent, minimise the risk exposure made by each trade.In options trading, there is a whole unique vocabulary describing the

various types of spreading between puts and calls on various strike pricesfor the same security or commodity. Often, commodity futures spreads arebuilt on assessments of the likely trends in differentials between variouscontract months in the same commodity. Classic commercial hedgersspread the exposure between long and short positions for the samecommodity for the same delivery period, with one side being in the actualpurchase or sale of the physical commodity with an offsetting derivativesposition. In theory, whatever loss accrues on one side should be offset bya corresponding gain on the other, keeping the producer, consumer ormerchandiser of the actual commodity protected against swings in themarket price of the commodity. Hedging thus frees up the firm to focus onoperational and marketing efficiencies rather than worrying about its busi-ness being disrupted by volatile price movements. This is, of course, intheory. In practice, the “basis” of the differential between the underlying


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physical and the financial derivative must be managed closely. Hedging isoften called an exchange of absolute price risk exposure for “basis” riskexposure.Most pertinent to coal are the spreads between inputs and outputs in a

commodity production process. The classic spreads are the soybean“crush”, whereby trades are made on the differentials between the rawsoybean inputs which are crushed to make soybean meal and soybean oil,with each having their own derivatives. With the advent of petroleumderivatives trading, the petroleum “crack” spread became a key differen-tial, measuring the cost of raw petroleum being refined into heating oil andgasoline. Since petroleum refiners often had catalytic cracking units, and“crack” is similar to “crush”, the petroleum “crack” was the logical newname.With the advent of electricity, natural gas and coal trading, the deriva-

tives world added the “spark” spread, the differential between natural gasfuel prices and electricity output prices. For coal- fired plants, the equiva-lent spread is the “dark” spread. Both of the spark and dark spreads can becalled “dirty” when they do not incorporate the cost of purchasing carboncredits for emissions created by the plants.In all these input- to- output spreads, financial traders develop standard-

ised relationships describing the amount of each input required for eachunit of output. As the reality for each bean- crushing operation, each oilrefinery or electric power plant will vary from these standard tradingmodels, the hedging/risk management teams for those operators willadjust their trades accordingly.For a simple example of a dark spread calculation using US measure-

ment units, the spread is measured by:

Spread = [Power price (US$/MWh)] – [Coal cost (US$/ton) +Transport cost (US$/ton)] x [Heat rate of generator (MMBtu/MWh) ÷Heat content (MMBtu/ton)]

where MWh is megawatt hour, heat rate is the rate at which the electricgenerator converts heat from the coal combustion into power, measuringthe efficiency of the generating unit, and heat content is how much heat isproduced by burning a ton of that coal.Unlike spark spreads, which are calculated using natural gas costs and

on- peak power prices, dark spreads often use a combination of on- and off- peak power prices. This combination (referred to as the flat price)reflects the different role that coal- fired generators play in the supply stackof a particular electric system. Coal- fired generators have traditionallyserved as baseload generation. They run throughout the day and night.The combination of on- peak (during the day) and off- peak (nights andweekends) power prices reflects this role.In addition, as gas- fired plants are typically more efficient than coal,

typical spark spread heat rates correspond to an efficiency of around 0.5(50%), while dark spread heat rates are near efficiencies of 0.38 (38%).


CONCLUSIONTypical production cost increases in the major exporting countriesother than the US have increased by about 200% since the late 2000s,resulting in the integration of worldwide coal markets.

Different coals compete with each other through a sometimes- complex value optimisation, combining quality, suitability, locationand cost of transport. Quality differentials continue to play a biggerrole in import decisions for coking coal because they play a bigger rolein the suitability for various steel plants. This contrasts with steamcoal, which is basically just “heat” and is very interchangeable. Thereare sufficient known and accessible reserves of met- quality coal;however, due to the increases in production costs, prices have to riseto bring them to market. Therefore, if demand for steel production issufficient, met coal prices will rise to meet the input demand.

Demand drivers are factors that move electricity demand such asweather, economic growth and, to some extent, the price ofcompeting fuels including natural gas. Met coal demand dependsdirectly on steel production.

Multi- year coal contracts have been in a long process of evolutionsince the early 1990s. It used to be fairly easy to describe typical termsand conditions, but this is no longer the case as there are many typesdiffering within countries and from country to country.

Coal remains the single most important fuel for generating elec-tricity worldwide. Traditionally, coal has been by far the cheapest fuelfor generating electricity. The other cheaper form is hydropower,which is strictly limited by geography and annual weather conditions.However, due to technological improvements in extracting naturalgas, that fuel has become consistently competitive to coal on price.Furthermore, natural gas is less carbon- intensive than coal, itsburning produces fewer undesirable emissions and the capital costsof building natural gas- fired generation are much less than for coal.Therefore, coal has lost significant ground to natural gas. Due to itsabundance and the high level of installed generating capacity,however, coal will continue to play a significant role in electricitygeneration.

Since the beginning of the industrial revolution, coal has been – andcontinues to be – the workhorse of the energy world. The coalmarketing chain from production to final consumer is typically muchless diverse and complex than other commodities. Coal also has a


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much lower value per weight than other commodities. Also, indus-trial organisations are the exclusive end- user consumers for coal. Thehigh proportion of transportation costs and less- diverse end- usersresult in few transactions from mine- mouth to final consumer.Therefore, among the major energy commodities, coal markets havebeen the slowest to adopt financial derivatives. However, coal hasbecome a full member of the energy risk management jigsaw.

APPENDIX 8.13

Coal conversion statistics and terminologyBasis of analysisDefinitions:

as received (ar): includes total moisture (TM);�

air- dried (ad): includes inherent moisture (IM) only;�

dry basis (db): excludes all moisture; and�

dry ash- free (daf): excludes all moisture and ash.�

The proximate analysis of any coal – ie, the percentage content ofmoisture, ash (A), volatile matter (VM), fixed carbon (FC), alsosulphur (S) and calorific value (CV) – can be expressed on any of theabove bases.

Conversions:

[For daf, multiply db by 100/(100–A)]

Example:

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To obtain: Air dry Dry basis As received

multiply

ar by: (100 – IM%)/(100 – TM%) 100/(100 – TM%) –

ad by: – 100/(100 – IM%) (100 – TM%)/(100 – IM%)

db by: (100 – IM%)/100 – (100 – TM%)/100

ar ad db daf

TM 11.0 – – –

IM 2.0 2.0 – –

Ash 12.0 13.2 13.5 –

VM 30.0 33.0 33.7 39.0

FC 47.0 51.8 52.8 61.0

Sulphur 1.0 1.1 1.12 –


MASSUnits:

Metric ton (t) = tonne = 1,000 kilograms (= 2,204.6 lb);�

Imperial or long ton (lt) = 1,016.05 kilograms (= 2,240 lb); and�

Short (US) ton (st) = 907.19 kilograms (= 2,000 lb).�

Conversions:From long ton to metric ton, multiply by 1.016;�

From short ton to metric ton, multiply by 0.9072;�

Mt – million tonnes;�

Mtce – million tonnes of coal equivalent (= 0.697 Mtoe); and�

Mtoe – million tonnes of oil equivalent.�

Calorific values (CV)Units:

kcal/kg – Kilocalories per kilogram;�

MJ/kg* – Megajoules per kilogram; and�

Btu/lb – British thermal units per pound.�

* MJ/kg = 1 Gigajoule/tonne (GJ/t)

Gross and net calorific valuesGross CV or higher heating value (HHV) is the CV under labora-�

tory conditions.Net CV or lower heating value (LHV) is the useful calorific value�

in boiler plant. The difference is essentially the latent heat of thewater vapour produced.

Conversions (units):From kcal/kg to MJ/kg, multiply by 0.004187;�

From kcal/kg to Btu/lb, multiply by 1.800;�

From MJ/kg to kcal/kg, multiply MJ/kg by 238.8;�

From MJ/kg to Btu/lb, multiply MJ/kg by 429.9;�

From Btu/lb to kcal/kg, multiply Btu/lb by 0.5556; and�

From Btu/lb to MJ/kg, multiply Btu/lb by 0.002326.�

Conversions – gross/net (per ISO, for as received figures):kcal/kg: Net CV = Gross CV – 50.6H – 5.85M – 0.1910;�

MJ/kg: Net CV = Gross CV – 0.212H – 0.0245M – 0.00080; and�

Btu/lb: Net CV = Gross CV – 91.2H – 10.5M – 0.340.�


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where M is percentage moisture, H is percentage hydrogen, O ispercentage oxygen (from ultimate analysis,4 also as received).

For typical bituminous coal with 10% M and 25% volatile matter, thedifferences between gross and net calorific values are approximatelyas follows:

260 kcal/kg 1.09 MJ/kg 470 Btu/lb

Power generation:1 MWh = 3600 MJ;�

1 MW = 1 MJ/s;�

1 MW (thermal power) [MWth] = approx 1,000 kg steam/hour;�

and1 MW (electrical power) [MWe] = approx MWth/3.�

A 600 MWe coal- fired power station operating at 38% efficiency and75% overall availability will consume approximately:

Bituminous coal (CV 6000 kcal/kg NAR): 1.5 Mt/annum.�

Brown coal (CV 2250 kcal/kg NAR): 4.0 Mt/annum.�

1 Aaron Blake, Washington Post, June 25, 2013: Obama science adviser calls for “war on coal”.2 BP, 2013, “Statistical Review of World Energy”, June.3 Source: World Coal Association website: http://www.worldcoal.org/resources /coal-

statistics/coal- conversion- statistics/.4 Ultimate analysis determines the amount of carbon, hydrogen, oxygen, nitrogen and

sulphur.

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Part II

Trading and InvestmentStrategies


Although oil, metals, grains and financials are commodities key tomaking the world go round, only farmland has no substitute.Everyone has to eat in order to survive, and the production of almostall food can be traced back to farmland. Demand is growing for farm-land as the world’s population and global need for food increases.However, what many do not realise is that the supply of farmland isnot changing, thus creating a severe imbalance in its supply anddemand.Over the long term, farmland will provide a steady stream of

income and capital gains due to the increasing global demand foragricultural commodities, driven by the rising world population,rapid growth in emerging markets and continued demand forethanol and biofuels.To understand it properly, we have to ask what exactly is farm-

land? The definition of farmland or agricultural land is the landsuitable for agricultural production, both crops and livestock.According to the United Nations Food and Agriculture Organization(FAO), there are three primary types:

1. arable land: land under annual crops, such as cereals, cotton,other technical crops, potatoes, vegetables and melons; alsoincludes land left temporarily fallow;

2. orchards and vineyards: land under permanent crops (eg, fruitplantations); and

3. meadows and pastures: areas for natural grasses and grazingof livestock.

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9

Farmland as an InvestmentGreyson S. Colvin and T. Marc Schober

Colvin & Co. LLP


For our purposes, we will generally focus on arable land or row cropfarmland that produces grains planted in rows harvested each year,including corn, soybeans and wheat. These are the grains that are(and will be) needed to feed the world’s growing population. Wewill also look at farmland located in the US, since it has some of thebest producing farmland in the world, as well as the most advancedfarmers and farming technology, the most developed infrastructureand uses the most leading technologies.According to the Natural Resource Conservation Service (NRCS),

there are 12 recognised types of soil in the world. Of these, the mostnaturally fertile are mollisols, which is suitable or very suitable farm-land. Mollisols are generally found in only four places: in the PampasRegion of Argentina, the Steppes of Ukraine and Russia, areas ofNortheast China and the Grain Belt of America. Mollisols make uponly 7% of the ice- free land in the world and are the best soils forfarming because they contain large quantities of organic matter.Mollisols found in the Midwestern US are the best for agriculture

due to the grasslands formed thousands of years ago. These prairiesproduced strong and fertile soils because each year the grasses (andanimals) would break down, with nutrients in the organic matterdecomposing into the ground. Once the Wisconsin Glacier retractedfrom Illinois and Iowa, great dust storms blew fertile silt on top of theyoung land, making it ideal for crop production.However, in terms of percentage of land area, not very much of

the planet is actually appropriate for farming. Once you removeplaces that are too cold or too wet, the deserts, the forests, the badsoils and every other strange place that cannot host a decent haul ofcrops, there is not much left over. However, while America has 5% ofthe world’s population, half of its land is suitable for cultivating andgrowing crops. In comparison, China has 20% of the world’s popula-tion but only 7% farmable land, according to the FAO.Under the rule of law, US farmland cannot be hijacked by a totali-

tarian government or organised crime (yes, organised gangsters doterrorise and control some farms in the Ukraine and Russia), and theUS Midwest Corn Belt sits in the optimum climate for production.When coupled with modern technology, the US farmer’s work ethic,excellent soil and infrastructure for transporting crops, the US isunsurpassed for production.All farmland is not created equal and no two properties are the


230


same. The ability of the land to produce profitable crops is part artand part science; however, at the end of the day, so is analysing andvaluing farmland. This chapter will therefore cover the followingfactors that drive the fundamental investment rationale for farmlandinvestments.

Land scarcity: there are approximately 3.5 billion acres of arable�

land in the world, and the potential for adding a mere 5% overthe next couple of decades.Food demand: as incomes rise, demand for proteins will�

increase, with a corresponding increase in the need for feedgrains. Demand is growing in developing countries, where theUS Department of Agriculture (USDA) expects exports to risefrom US$82 billion in 2007 to nearly US$167 billion by 2021.Bio- fuels: agriculture and energy markets are bound together by�

federal mandates for renewable fuels. USDA estimates that40.5% of the US corn crop was used for ethanol in 2011 and 42.0%to have been used in 2012.Declining inventories worldwide: inventories of grains, as�

measured by stocks- to- use ratios, have been trending down-wards in many countries. In the US, there is less than a 21-daysupply of corn. In China, declining stocks have created thepotential for increasing imports of corn.Resource conservation: agriculture production must be�

managed as a sustainable resource to feed the world’s growingpopulation. Water is a vital resource and is a limiting factor forirrigated agriculture throughout the world.Low farm sector debt levels: the US farm sector has a healthy�

balance sheet. Current debt- to- assets ratios are at 40-year lowsand 78% of Iowa farmland is free of debt.US infrastructure: from transportation and storage networks to�

the stability of government programs and the know- how at USuniversities, the US farm sector has the ability to grow and effi-ciently market large volumes of feed and foodstuffs.Inflation hedge: many economists expect inflation to rise in the�

longer term as large federal deficits and the Federal Reserves’easy money policy create the right conditions. Farmland ishighly correlated to inflation and negatively correlated to mostother financial asset classes.

FARMLAND AS AN INVESTMENT

231


Cash returns: farmland is a performing asset, generating modest�

cash returns of 4–6%, depending on location and crop.Sustainable asset: farmland improves in productivity over time�

when well managed.

The chapter is organised into the following sections: the first willexamine value creation and investment in farmland, before we delveinto renewables and their impact. The next section details productionand its limitations, and we finish with an investigation into globalfarming.

VALUE CREATION AND INVESTMENTValue creation from farmlandArable land for farming has been valued since the first crops weredomesticated. Farmland creates multiple commodities: wheat, corn,animal products and meats, and even wind energy if a landownerchooses to lease out part of their land to host a wind turbine. Aninvestment in farmland can provide a steady stream of income fromdemand for agricultural goods, drivenby the risingworldpopulationand rapid growth in emerging market consumption. The continueddemand for ethanol and biofuels also puts upward pressure on cropvalues. Demand for agricultural commodities is outpacing supply,which positions farmland for long- termappreciation.We should look at what makes something valuable as a

commodity; is it, or does it offer, a broadly desired marketable item?Is it something that would be dearly missed if it disappeared fromthe worldwide market? In addition to being an end- user item, cansomething also serve as an investment vehicle?

Farmland as lease propertyA farmland owner who does not intend to operate the farm oftenmonetises the land’s value by leasing. A rental lease, in this case, is anagreement between the landlord and the farmer of the property.Often these agreements are legally binding documents drafted byattorneys, but can be as vague as a verbal commitment (in whichcase, it may be as solid as the paper it is written on).Leases span all different lengths of time, from one year to the life

of the property, but in the Midwestern US they are often for betweenone and five years. Farmers aggressively seek leased land for their


232


operations in order to expand and capture economies of scalewithout increasing the most expensive element of production: theland. Leased land allows farmers to spread equipment and otherfixed operation costs over more acres to increase profit margins, andalso allows them to increase income by farming more acres.There are several possible lease options available to a landlord, but

any of them should return roughly a third of all revenues generatedfrom the land per year. There are three main types of farmlandleases:

cash rent: fixed rate per acre per year;�

crop sharing: landowner shares in the expenses and profits; and�

custom farming: the landowner hires farmers to operate the land�

and pays all the expenses.

Farmland risk–return profileFigure 9.1 highlights the risk to reward profile of the different typesof leasing methods. Cash rent typically yields 4–5% annually, whilecrop sharing can earn another 50–200 basis points on average. Theincremental risk taken on by the crop- sharing contract is arguablynot appropriate for the potential return. Returns will be much morevolatile, but over a 10-year period should yield higher than a cashrent contract. Custom farming is a good fit for landlords who areexperts on farming operations and who live near the property, butthe risk–reward ratio does not appear too suitable for many otherlandlords. If you are a casual investor looking to expand your port-folio, custom farming may not be the best option for you.Typically, the cash rent contract is recommended. The best thing

about this contract is that the rent is typically 100% prepaid beforethe farmer plants a single crop in the ground. This prevents the


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Figure 9.1 Farmland risk–return profile

Own/hold Cash rent

CroplandLow risk

Low returnsHigh risk

High returnsTimber Tree cropsPrime farmland

Crop share OperateCustom farm Joint venture

3% 5% 7% 10% 12% 12%+


landowner from taking on crop or credit risk from the farmer. Thelandowner does not have to worry about drought or the rate of cropgrowth. Land across the Midwest is typically leased at 4–5% of themarket value of the land; target farmland for investment that can beleased for 5% or greater is recommended. Farmland in other regionsof the US can have lower lease rates as a percentage of value due tothe commodities produced and other factors affecting the value,such as potential development.

Farmland as an investmentFarmland has a proven record – it has been one of the top performinginvestments over the last 100 years. In the 20th century, farmlandonly decreased in value three times: during the Great Depression, theinflation crisis of the early 1980s and in the housing crisis of 2008/09.The US farm sector has a healthy balance sheet and, as mentioned, debt- to- asset ratios are low. Unsurprisingly, farmers historicallyhave been the main buyers of US farmland and do not buy intendingto flip for profit but rather to hold for decades or generations,keeping the land in the family. Farmland is the most valuable asset afarmer can own, which leads most to reinvest a significant part oftheir crop and livestock revenue back into the purchase of additionalfarmland to expand their operations.It is also important to understand that farmland values per acre

are essentially a function of revenues generated per acre. Revenuesare mainly dictated by two variables: price of the commodity andyield per acre. In the 20th century, grain prices were fairly stablewhile production increased a few percentage points per year, onaverage. The increase in production allowed farmland to become oneof the most stable and consistent asset classes.Despite three downturns over the last 100 years, farmland returns

in the US are historically one of the best investment vehicles,comparing favourably with more traditional assets such as stocksand bonds. Table 9.1 clearly shows the stability of farmland. Bear inmind, this includes crop years and/or regions that were wiped outor suffered severely diminished yields due to drought, flood andother disasters.In 2012, the Federal Reserve Bank of Chicago reported that farm-

land values grew by 16%, the third largest increase in the previous 35years. Despite the worst drought in over 55 years, high commodity


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prices and record farm incomes drove demand for agricultural land.Survey respondents anticipated that the momentum would continueover the next 12 months based on the record income expectations for2013. Iowa farmland values led the pack, with a 20% return in 2012,followed by Illinois and Michigan with an 18% annual return. Thiswas during a time many considered recessionary.One of the most attractive attributes of farmland is income

realised from rental. Since 1967, rural cash rents have yieldedroughly 5.7%, according to the USDA (this was calculated by theauthors using historical data from: http://usda.mannlib.cornell.edu/MannUsda/viewDocumentInfo.do?documentID=1446). Thiscompares very favourably to Treasury bonds and other income- producing assets. The cash rental contract is typically prepaid, so theinvestor does not have to take operational or credit risk from thefarmer. Society will undoubtedly be drastically different by the mid-21st century, but the US farmer will still be leasing farmland to raiselivestock and crops.Farmland also provides investors with the chance to diversify

from traditional investments, which makes it an excellent asset tobalance a portfolio and offset financial and commercial real estatemarket volatility. Farmland has always shown a positive correlationto the Consumer Price Index (CPI), exceeding stocks, bonds and non- farm real estate.Farmland is frequently compared to investing in gold because of

its characteristic as an inflation hedge. However, unlike gold, farm-land also produces a stable income stream, and as a consequence ithas been described as “gold with yield.” Gold does not stock- split or


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Table 9.1 Midwestern US farmland returns

State 1 year 5 years 10 years 20 years 50 years 100 years (%) (%) (%) (%) (%) (%)

Illinois 22.8 12.0 11.8 8.1 6.9 4.6Iowa 22.8 16.2 14.1 9.7 7.6 4.9Nebraska 33.5 18.4 13.5 8.7 7.4 4.6North Dakota 26.5 14.1 11.8 7.3 6.8 4.2South Dakota 23.9 13.1 12.8 8.5 7.1 4.1Wisconsin 7.4 3.7 7.4 8.5 7.4 4.7US 10.9 5.8 8.3 6.9 6.5 4.5

Source: USDA Economic Research Service


pay dividends; you just hang on to it, pass it down or sell it. It canalso be seen as similar to non- dividend paying equities. Eventually,the only way these stocks bring value to you or your family is whenyou sell them. However, farmland will bring returns to you andgenerations of your family as long as they continue to own andmanage the land.

RENEWABLES AND THEIR IMPACTRenewable fuels impact on the farmSocial and political concerns regarding climate change and fossil-fueldependency have led to a significant focus on renewable fuels, suchas ethanol, as a replacement for petroleum- based fuel sources.Ethanol is primarily manufactured from crops such as corn, wheatand sugar cane. According to the USDA, ethanol production in theUS increased from less than three billion gallons in 2003 to over sixbillion gallons in 2007, and is estimated to exceed 12 billion gallonsby 2020. The Renewable Fuel Standard from the 2007 EnergyIndependence and Security Act calls for total renewable fuel to reach36 billion gallons by 2022.Ethanol, no matter how viable or controversial, is mandated as a

renewable source of energy. At its most basic, ethanol is grainalcohol, produced primarily from corn and sugar cane. The USDAestimates that more than 40% of US corn production was used toproduce ethanol in 2011. In January 2011, the US EnvironmentalProtection Agency (EPA) approved the use of E15 gasoline for vehi-


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Figure 9.2 Investment correlation with farmland (1971–2009)

Source: NCREIF, Ibbotson & Associates, Morningstar, Western Spectator (June 2010)

Historical correlations with US Farmland Correlations Negative Positive Long term US corporate bonds -0.43

US treasury bills -0.22

S&P 500 -0.18

International equities -0.15

US small cap equities -0.07

US commercial real estate +0.23

S&P GSCI +0.28

Gold +0.30

US inflation +0.36

-0.50 +0.50-0.25 +0.250


cles manufactured in 2001 or thereafter. Almost all gasoline in the USis E10, or 10% ethanol content. The increase to E15 will help the US inits goal of using 36 billion gallons of renewable fuel by 2022, as perthe Energy Act of 2007.In 2004, the US government passed a 45 cents- per- gallon tax

credit, commonly known as the “blender’s tax credit”, to provide aneconomic incentive to blend ethanol with gasoline. The official nameis the “Volumetric Ethanol Excise Tax Credit”, and it was part of theAmerican Jobs Creation Act of 2004, although the incentive expiredat the end of 2011. In response, critics have argued that ethanol is aninefficient source of energy and should no longer be supported bythe government. However, it seems unlikely that ethanol productionwill disappear in the near future. The federal government does notlook to be changing these mandates.Wind energy is another source of commodity revenue for the rural

landowner. By its very nature, farmland usually lies in the vastexpanses of open prairie that allows the wind’s unfettered flow.Wind energy could even meet 20% of the US electricity demand by2030. According to the US Department of Energy (DoE), farmlandowners can benefit from wind energy by having one or more windturbines placed on their property and receive a lease- rate paymentper turbine.Landowners can receive up to US$15,000 annually per turbine,

although each wind company’s contract will differ. One windturbine only requires roughly a single acre of land and has minimaleffect on farming practices. One acre of cropland is lost, but isreplaced with revenue from wind turbine leases. Once the windturbines are finalised and constructed, landowners typically receivefixed and variable payments based on electricity production. SouthDakota is in an excellent position to capitalise on wind energy, as thestate is known as the “Saudi Arabia of Wind.”According to Dakota Wind Energy, South Dakota has the wind

potential to meet 50% of US electricity demand. It ranks fourth in thenation in wind power, behind North Dakota, Texas and Kansas.Since the late 1980s, the cost to produce wind electricity has droppeda huge 90%, according to the American Wind Energy Association(AWEA). Although wind energy costs are not as low as for conven-tional power, ever- improving technology is driving wind energycosts down. The government has helped promote the development


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of wind energy through subsidies, such as accelerated depreciationand the production tax credit (PTC), which offsets the cost of devel-opment.The primary constraint of wind energy is the transportation of

electricity. Since electricity must be used immediately or transportedto a power plant, wind turbines must be closely connected to electricgrids that can transmit the energy. The majority of the windy regionsof the US are located in rural areas with limited amounts of energydemand and transmission capacity.One solution is the Green Power Express transmission line being

developed by ITC Holdings Corporation. The transmission lineswould span roughly 3,000 miles from the Dakotas into Wisconsin,Illinois and Indiana. The Green Power Express, due to be completedby 2020, will provide a path for newly generated electricity to travelto heavily populated areas such as Milwaukee and Chicago, andeven open up the entire eastern seaboard. This may very wellinvolve an opportunity for landowners to lease land for infrastruc-ture development in support of the initiative.Fuels based on crops may be new, but a windmill on a US farm is

as old as a Norman Rockwell painting. Farms started featuringwindmills on their properties as early as 1900 for the purpose ofpowering the well pump. It was not electricity, but the mill gener-ated power and reduced the need of human or animal powerthrough harnessing natural wind energy. Efficiently introducing thenew technologies of wind turbines and eco- fuels allows a landownerto even further diversify the sources of revenue from their farmingenterprise.

Increases in demand for agricultural productsGrain supplies in the US and globally are at decade lows, driven byemerging market demand, disappointing US yields and demand for biofuels. The ending corn stocks- to- usage ratio has been trendingdownwards, from roughly 20% in 2004 to 5.6% in 2012, according tothe USDA (these figures were calculated by the authors; the data onwhich this is based can be found here: http://www.usda.gov/oce/commodity/wasde/).In the USDA’s October, 2012, update of “World Agricultural

Supply and Demand Estimates” report, ending stocks for 2012/13are projected to be down by 37% to 619 million bushels, as corn use is


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expected to exceed production by 444 million bushels and theMidwestern US has had the worst drought in over 50 years. US cornstocks have declined to a 21-day supply, meaning that if cornproduction was halted, the US would run out of corn in a little overhalf a month.The global demands for food and rising commodities prices have

driven agriculture fundamentals upwards. The USDA estimates thatfarm incomes have been steadily trending higher, increasing from28% in 2010, 47% in 2011 and was recorded at 14% in July 2013, andwill continue to rise – allowing farmers to reinvest their dividendsback into farmland to expand their operations.Despite the rapid growth in agriculture, farmers’ balance sheets

remain very conservative. Strong farm income and minimal use of


239

Figure 9.3 US corn stocks/usage ratio

Source: ERS/USDA

0%

10%

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30%

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60%

70%

1980 1986 1992 1998 2004 2010

Figure 9.4 Farm sector debt-to-assets ratio

Source: ERS/USDA

0

5

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1960 1970 1980 1990 2000 2010


debt have allowed the US farm sector to maintain conservativebalance sheets as current debt- to- assets ratios continue at decades- long lows. New banking regulations have restricted the access tocapital for farmland buyers, and loans secured by farmland are typi-cally limited to 50% of the purchase/appraised price. This securefinancial situation bodes well for farmland (farmland owners tend tobe more on the commonsense side of economics).

PRODUCTION AND ITS LIMITATIONSLimits to productionFarmland values are expected to continue their momentum into the2020s and beyond due to the strong global and ever- increasingdemand for food. The world’s gross agricultural output mustincrease by 3.4% to meet this demand, according to the FAO. Thetwo primary ways to increase agricultural production are to eitherincrease the amount of acres planted or increase productivity withtechnology. With urban sprawl and land development, increasingyield seems to be the logical answer.The future ability to expand arable acres will be difficult. The

prime areas for farming have already been identified, are being usedfor production and have built- in transportation and infrastructuresupport. The marginal arable acres that can be put into productionwill be in odd, outof- theway places with less than optimal growingconditions and possible transportation issues. However, there is away to grow yield and increase arable acres.Although the introduction of genetically modified organisms

(GMOs) has been somewhat controversial, they have not onlyincreased bushels per acre in standard farming regions, but theyhave also brought better drought and cold tolerance in the US, aswell as expanding the land area that can be used for cold- sensitivecrops. For instance, the land planted to both corn and soybeans sincethe late 1990s has extended into the colder north and drier westareas. The acreage allotted to corn and soybean production isexpanding northwest to regions where the number of growingdegree days are less. Crop insurance for corn acreage now expands60 miles further north into Canada. As a result, the Corn Belt and,along with it the opportunity to invest in high- quality producingfarmland, continues to grow.


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Acres in conservation programmesThere is yet one more resource for production: farmland set asideunder theConservationReserveProgram(CRP)couldbeaddedto theamount of US arable acreage. According to the USDA, 31.3 millionacres had been enrolled in the CRPunder almost 738,000 contracts bythe endof 2010.As theCRPcontracts expire,muchof this landmaybeputback intoproduction,butamajority ismarginableatbest,which isthe primary reason it was put into the programme in the first place.The CRP pays landowners not to farm their cropland in order toprotect areaswherewildlife cangrowand fertile land can take abreakfrom producing crops. Other environmental programmes includeenvironmental quality incentives and wetland preservation. Thismust be done for the longterm health of the soil. CRP will providemore acres in theUS for production, but due to the lack of soil quality,the effect on total productionwill beminimal.

GLOBAL FARMINGFarm growing in other global regionsThe amount of acres of arable farmland has been almost static as the non- farm development of farmland in North America and Europehas been offset by expansion of farmland in Africa and SouthAmerica. There are approximately 1.5 billion hectares being farmedaround the world. The FAO estimates that the world has a total of 2.5billion hectares of “very suitable” or “suitable” land for farming andraising crops. About 80% of this reserve land is located in Africa andSouth America. The investment bank Credit Suisse estimates thatthere is only about 300,000 hectares of additional potential acreage,with the majority in Brazil and Indonesia.Table 9.2 summarises the acres in use in 2013 and potential global

arable acres. The primary expansion opportunity lies in Brazil,where the government organisation Conab estimates there are anadditional 106 million hectares available for agricultural develop-ment. Historically, the soil was thought of as unfarmable due to highacidity levels and lack of nutrients. However, technologies such asstrip tilling, soil surveys and Global Positioning Systems (GPS) haveallowed farmers to improve soil fertility, and a new type of soybeandeveloped to grow in tropical climates from the early 1980s meantthat farmers were able to start producing crops in previously unsuit-able acreage.


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Indonesia has a huge opportunity to expand acreage for palm oilcultivation. The Indonesian government estimates that it is onlyusing half of its land available for cultivation. In January 2011,Indonesia targeted expanding the county’s agricultural land by twomillion hectares in the medium and long term, although this plan hasreceived a great deal of criticism as it would result in the removal oftropical forests.Ukraine, Russia and Kazakhstan saw a substantial decline in

arable acres and crop yields following the decline in communismduring the early 1990s. This demonstrates the loss of the motivation- to- yield prospect of farming: farming is hard work and if your labourgoes into the pockets of organised crime or corrupt government,there is no incentive towards healthy crop production. The FAO esti-mates that arable acres declined 11% between 1992 and 2005. CreditSuisse estimates that if arable acres return to 1992 levels, that itwould add 1.9% to the total global arable acres.

Big (farm) trouble in ChinaThere has been much speculation, and even fear, about the rise ofChina. Chinese demand for agricultural products will likely be a keyforce in these markets for the coming decades. The year 2010 markeda new era for China as it announced it would no longer be self- sufficient in corn production. The demand of the most populatedcountry in the world for corn and feed is now outpacing supply asthe nation continues to consume more and more protein. China andits people are in the process of transitioning from a grain- based diet


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Table 9.2 Global acreage expansion

Area (1,000 Ha) Acres (2013) Additional acres

Europe 94,294 1,000US 173,158 12,950Brazil 66,500 106,000Other Latin America countries 59,290 76,000Indonesia 37,500 102,000Russia 123,368 10,397Ukraine 33,333 1,120World total 1,553,689 309,467

% of world total 20%

Source: Conab, Indonesia Ministry of Ag, USDA, FAO, Credit Suisse


to a protein- based diet. On average, it takes seven pounds of grain toproduce just one pound of meat, according to the Earth PolicyInstitute.One of the primary problems limiting China’s ability to feed itself

is its land imbalance. China has roughly 20% of the world’s popula-tion with only 7% of the world’s arable land. The supply of arablefarmland in China is decreasing rapidly as well. By 1950, China hadlost a fifth of its arable land due to erosion, desertification and devel-opment, and is expected to lose 10–15 million more hectares by 2020,according to the UN.In order to be self- sufficient in grain production, the vice minister

of agriculture, Wei Chaoan, stated in 2010 that China needed tomaintain 120 million hectares for crop production until 2020.Government figures estimate that the amount of arable land is actu-ally 122 million hectares, which has remained unchanged since 2005.Bank of America estimates that China’s arable land has already fallenbelow the 120 million hectare threshold and could decrease to 117million hectares by 2015.As its economy and population grow, China will have to increas-

ingly rely on the import market to solve their shortage of corn andother foodstocks. Chinese imports of corn will grow from 1.0 milliontons in 2010 to 15 million tons in 2014–15, according to the US GrainsCouncil. 15 million tons of corn translates to Chinese imports of 600


243

Figure 9.5 China corn supply demand

Source: USDA Foreign Agricultural Service

20,000

60,000

100,000

140,000

180,000

80/81 90/91 00/01 10/11

Prod

uctio

n (1

000M

T)

Total production Total consumption Ending stocks


million bushels, equal to 15 tons of corn, will have a substantialimpact on global corn stocks.China’s transition to a net importer of corn is very similar to its

transition to becoming a net importer of soybeans. Before 1995,China was a net exporter of soybeans, but by 2010 it was the world’slargest soybean importer, importing more than 57 million tons of thecrop, 21.6% of world production, according to the USDA. The rapidindustrialisation of developing markets will have serious repercus-sions on the demand for grain. In China specifically, there may bearound 500 million more people demanding a protein- based diet.China is not the only example of a developing country that has

an increasing appetite for grains. As the world’s middle classcontinues to develop, the demand for grains will continue to growexponentially.

Global demand for farm crops and commoditiesAccording to the US Census Bureau, there were approximately 7.0billion people inhabiting the Earth in 2012, compared to just 1.7billion in 1900 and 5.8 billion in 1985. The rate of population growthis not expected to temper as the United Nations estimates the world’spopulation is likely to reach 9.2 billion by 2050. Most of this popula-tion growth is expected to originate in emerging economies, withdeveloped countries remaining stable.The global population growth rate is expected to decelerate due to

lower fertility rates, to roughly 1% by 2030, down from a 2% annualgrowth rate in 1970, according to the United Nations. Despite theslower population growth rate, life expectancies have substantiallyimproved from 30–40 years in pre- industrial times, to roughly 65years. The prospect of feeding a demographic that is becoming lessproductive is another factor that puts a strain on food production.In order to feed the world’s growing population, agricultural

output will need to double by 2050, according to the FAO. This willbe a daunting goal to accomplish as agricultural resources arealready strained. Since the early 2000s, agricultural output hasgrown by 2.4% annually. In order to double agricultural output by2050, output must increase at 3.4% per year. To meet future demand,experts are predicting that global agriculture will need to producemore food in the next 50 years than what was produced during theprevious 10,000 years, putting more and more pressure on futurefarmers and the land they use to produce our food.


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Food demand is growing faster than population growth becauseof the development of middle classes in emerging markets, due to above- average GDP growth. The Brookings Institution estimatesthat, by 2021, China’s middle class could grow to over 670 million,compared to only 150 million in 2010. Economists have long shownthat, as GDP rises, so does the consumption of animal protein as apercentage of diet. As emerging economies continue to develop,there will be a transfer from a grain- based diet to a protein- baseddiet. Over half the increase in global calorie consumption since theearly 2000s has been a result of increased meat consumption,according to the FAO. It takes two pounds of grain to produce onepound of chicken, five pounds of grain to produce one pound ofpork and seven pounds of grain to produce one pound of beef.Again, this represents a great demand for commodity production.

SUMMARYFarmland values have been steadily increasing due to increasedcommodity production on farmland, but the primary driver offuture value increases will derive from the supply and demand ofthe commodities grown from the land. Corn supplies are at theirlowest levels in decades. The major difference between the 1995 cornsupply and corn supply in 2013 is that global corn production waslow in the mid-1990s due to poor production, which was only a short- term effect. That US corn supply has become an alarming 20days is due to the increased usage of corn across the entire world.What is exciting about farmland is that the agriculture proposition


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Figure 9.6 World population (1950–2050)

Source: US Census Bureau, International Data Base

0

2

4

6

8

10

1950 1970 1990 2010 2030 2050

Billions


is still the tip of the iceberg. Most agriculture investors are attractedto the sector because of the wealth creation due to the transfer to a protein- based diet in emerging markets. China is expected toincrease corn imports from 1 million tons in 2010 to 15 million tonsby 2014. The biggest demand for grain by the emerging markets hasnot even occurred yet. The basic supply and demand is in place forfarmland to continue its bullish trends in the long term.Although the amount of farmland is limited in the US, farmable

corn- producing land is expanding into areas with great soil butheretofore slightly unsuitable climates in the Midwest, primarily dueto biotech seeds. Large seed and agrichemical companies havefocused years of research on higher performing varieties and hybridsof important food and feed crops. The next generation of biotechtraits focus on greater productivity, improved nutrient use, diseaseresistance, plant density and drought and cold tolerance.While GMOs may bring a degree of controversy, they also

generate much- needed crop acreage and yield. And with peoplealways looking for safe places to invest, this can translate to a greatinvestment upside through increased commodity production.Although farmers make up the majority, people from many

different walks of life own farmland, and outside investors havealways had a minority interest. However, outside investor interesthas grown latterly and will keep growing as farmland continues tofeed the world’s growing population. Almost 200 investment firmsare expected to invest US$30 billion in farmland by 2015, accordingto Michael Kugelman of the Woodrow Wilson International Centerfor Scholars. Worldwide media coverage now includes farmland ona daily basis and the expansion of farmland as an asset classcontinues to occur.The average age of the US farmer is steadily increasing. The 2007

Census of Agriculture reported their average age had increased from50.3 in 1978 to 57.1 in 2007. The ageing farmer may provide an oppor-tunity for the non- farmer investor to get into this commodity- producing market. There was a time when the family farm went tothe son when the father retired or passed on. However, societaltrends have seen people selling the family farm and getting out of thefamily business.Demand is growing for farmland as the world’s population and

global needs for food increase. What many do not realise is that the


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supply of farmland is not changing, thus creating a severe imbalancein its supply and demand. An investment in farmland over the longterm will provide a steady stream of income and capital gains due tothe increasing global demand for agricultural commodities, drivenby the rising world population, rapid growth in emerging marketsand continued demand for ethanol and biofuels. Demand for agri-cultural commodities is outpacing supply, which positions farmlandfor longterm appreciation.


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The pool of participants trading in agriculture commodities hasgrown rapidly in number and type since the beginning of the 21stcentury, thus increasing diversification and liquidity across the agri-culture sector. Increased participation has been witnessed acrosseach subset of traders, including the commercial, non- commercialand index- trading communities. This growing diversification acrossagriculture markets has raised the bar for money managers andproprietary traders alike who are seeking to exploit positive risk–reward opportunities. This chapter will provide descriptions of thesetypes of traders, their behaviour and objectives. This chapter isarranged into three sections, which look at, respectively, the partici-pants in the agriculture markets, trading in these markets and thestrategies utilised.

PARTICIPANTS IN AGRICULTURE MARKETSCommercial tradersIt is important to consider the commercial subset of traders, andbetter understand their activities and objectives. Commercial tradersas defined by the US Commodity Futures Trading Commission(CFTC) are those who use futures or option contracts in a givencommodity for hedging purposes. Commercial traders hold posi-tions in both the underlying commodity and in the futures (oroptions) contracts on that commodity. In agriculture, commercialscan be producers, merchants and end- users, all of which come to themarket to manage business, price and margin- related risks.

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10

Agriculture TradingPatrick O’Hern

Sugar Creek Investment Management


Commercial trading activity has grown to become more sophisti-cated over time, as businesses have dedicated more capital to buildout trading desks by instituting structured commodity marketingand risk- mitigating hedging plans for themselves and theircustomers.

Figure 10.1 illustrates the growth in commercial participantvolumes traded across agriculture markets since the year 2000. Theexpansion among the commercial trading community is viewed asimperative as the globalisation of agriculture commodities hasincreased the volatility in profit margins for all types of physicalcommodity businesses. The increased volatility in profit margins hasdriven commercials to put more emphasis on managing margin risk.For instance, consider a large livestock feeding operation that takespart in purchasing, feeding and selling the stock. The focus for thisoperation is not only on hedging or marketing the sale price, but alsothe purchase price and the input costs, including feed and energyusage. Profit margins can vary greatly over the ownership perioddue to changes in the price of input costs that can create enormousbusiness risks for the producer. For non- commercial traders, it hasbecome increasingly important to understand the behaviour andunderlying economics of these commercial trading entities, as thebusiness risk imbedded within participants such as the livestockfeeder are just as crucial as the supply and demand of the commodityitself.

Figure 10.1 also illustrates the difference in the level of participa-tion between commercial and non- commercial participants. Thisdifference highlights the importance for non- commercial partici-pants to be more aware of the business and economic decisions beingmade by commercial market participants, as they generally accountfor 50–60% of the aggregate trading volume and total open interestacross agriculture markets. In commodities, open interest is the totalnumber of futures and/or options contracts in a contract month,while total open interest accounts for the total amount of contractsacross the forward curve per commodity.

Generally speaking, the non- commercial participation rangesaround 40–60% of the commercial participation. As seen in Figure10.2, CME Feeder Cattle non- commercial volumes are larger thanthat of commercial volumes. This is due to an unusual amount ofcommercial hedging activity falling into the non- reportable category,


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AGRICULTU

RETRADING

251

Figure 10.1 Growth in commercial and non-commercial trading across agriculture markets

Source: US Commodity Futures Trading CommissionNote: Total participation: commercial (black) and non-commercial (grey).

0

1,000,000

2,000,000

3,000,000

4,000,000

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6,000,000

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1/4/2000 1/4/2001 1/4/2002 1/4/2003 1/4/2004 1/4/2005 1/4/2006 1/4/2007 1/4/2008 1/4/2009 1/4/2010 1/4/2011 1/4/2012

CBT wheat, KCBT wheat, corn, MGE wheat, oats, soybeans, soybean oil, soybean meal, cotton, rough rice, orange juice, milk, lean hogs,live cattle, feeder cattle cocoa, sugar and arabic

Con

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10 Chapter C

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age 251

thus being exempt from reporting. This occurs in all markets, but ismore pronounced in the livestock complex in general. The tradi-tional commercials in live and feeder cattle are the feed yards, mostof which hedge their exposure in the live cattle. While cow/calf andstocker operators utilise the feeder cattle market for hedgingpurposes, the majority of their position sizes fall below the reportingrequirements.

Understanding the economics of physical commodity businessesrequires a strong knowledge of the individual components thatdetermine profit margins. This analysis of market fundamentals cangive traders an edge in generating opportunities and determiningthe best types of trading strategy to implement. By understandingthe nuances of producer and merchant margins, non- commercialtraders can better assess buy- side and sell- side hedging activity thattakes place in the futures market. The most margin- sensitive hedgersare active on both the buy- and sell- side; those include merchan-disers, livestock feeders and processors. More traditional sell- sidehedgers include producers who have less market- related marginrisk, as their input costs are more tied to the operational overheadand productivity. For instance, consider a grain farming operation:in advance of each growing season, the producer must decide whichcrop to plant by assessing a variety of important factors such as theprojected profitability per acre and the soil conditions across theacreage in which the crop will be planted on. While the price of the


252

Figure 10.2 Non-commercial trading as a percent of commercial participant volumes for various agriculture markets

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underlying cash commodity is a driver of the producer’s margins, itis not the sole influence of what a producer ultimately decides toplant. The producer has to account for factors such as soil conditionand potential yield variability based on crop rotation practices thatcan have important implications on the level of production per acre.Equally important to profitability are overhead and input costs suchas seed, machinery, financing, labour and fertiliser. These factorscreate a fixed piece of the margin that producers must account for inadvance of planting their crop; as a result, the selling or marketing ofthat crop is a vital decision.

For non- commercial traders, understanding the economics behindthe sell- side hedger’s decision- making can produce clear signals forthe future change in supply of a particular commodity. For example,a noticeable lack of producer selling could indicate decreasedproduction for a commodity. In agriculture this could be due to apoor growing season that has producers revising their expectedoutput, or it could be driven by the lack of economic incentive toproduce due to poor profit margins at the time of seeding.

Figure 10.3 highlights the growth in commercial trading acrossindividual markets. Note the growth in corn, sugar and soybeans, asthose commodities – aside from traditional uses such as feed andfood – have seen new demand come in the form of renewable energyinitiatives across the world. This relatively modern dynamic has hadboth a direct and indirect impact across the agriculture market,increasing participation by commercials and non- commercialtraders alike.

For example, the US Renewable Fuel Standard (RFS) requiringgasoline refiners to blend corn ethanol was introduced in 2005. In2007, the RFS mandate was increased to a 10% corn ethanol blend ingasoline. The introduction and subsequent increase in the US renew-able fuels mandate has resulted in increased demand andcompetition for the US corn supply (see Figure 10.4). In 2011, around40% of the domestic corn supply was consumed by the ethanolindustry. This additional demand has not only increased corn pricesbut also that of competing row crops. As a result, the US RFS has hada direct and meaningful impact on the US and global grain industry.Consumers of grains have been affected as costs for feed and otherrelated inputs have increased in value. Markets such as livestockhave also been indirectly affected, as profit margins have at times

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Figure 10.3 Commercial trading growth across individual agriculture markets

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been negatively impacted by higher corn values resulting inproducers decreasing herd size or seeking alternative feed rations.Another indirect affect of the US RFS has been on the soybean mealmarket; during the ethanol production process, a third of the caloricvalue of corn is retained in a by product called distillers’ dried grains(DDGs). The introduction and prominence of DDGS have presentedanother source of feed for livestock and poultry producers that havealtered pricing relationships between soybean meal, hay and othersources of protein and roughage.

Non- commercial tradersThis section covers non- commercial traders by providing descrip-tions of each type. This class of trading participant includesfundamental discretionary and individuals trading proprietarycapital, to systematic and technical trading (all of which will bedetailed in this chapter). These traders can incorporate manydifferent forms of risk- taking based on return objectives, opportuni-ties in their market and their approach to trading. Agriculturemarkets present unique challenges and opportunities for non- commercial traders due to risks involving seasonality, liquidity andweather.

The fundamental discretionary trader uses fundamental analysis

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Figure 10.4 Corn usage by segment, illustrating the importance of tracking usage by end-users

Source: USDA ERS, Feed Outlook* projection

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to make trading decisions in the agriculture markets. Many of thefundamental discretionary traders are registered with the US CFTCas commodity trading advisors (CTAs), allowing them to marketthemselves as an investment vehicle and manage client money inindividually separate managed accounts. There are also agriculturespecialist hedge funds that manage client money through onshoreand offshore vehicles. Since the beginning of the 21st century, theagriculture markets have witnessed significant growth in thenumber and size of assets under management and managers. Theincrease in speculative trading across agriculture markets at the turnof the century can be attributed to the evolution of electronic tradingas global speculators were increasingly allowed greater access, trans-parency and flexibility to execute trades on commodity exchanges.Inflationary risks have latterly attracted speculators, as global centralbank’s stimulus and US Federal Reserve policy measures haveincreased the flow of money in the marketplace. Fundamentallyspeaking, agriculture markets have been attractive in regard to theo-ries and scientific research surrounding climate change and itspossible implications for the future of global agriculture production.Additionally, social economics involving population growth,changing dietary habits and emerging market demand have all hadan impact.

These traders commonly come from physical commodity back-grounds – for example, having worked as a grain merchandiser forCargill or a sugar trader at Louis Dreyfus. Other traders that havebuilt out money management businesses have come from the agri-culture trading pits of Chicago, where they were successfulproprietary traders or brokers for large commodity customers. Inmost cases, the fundamental discretionary commodity trader hasspent an invaluable portion of their career working for commoditybusinesses, where they learned the fundamental pillars of whatdrives supply and demand for each commodity they trade. Figure10.5 illustrates the percentage of non- commercial trading relative tocommercial trading across agriculture markets since 2000.

Proprietary traders: individuals and trading groupsProprietary traders are a diverse subset on their own, as this type oftrader fills in all the cracks inside the non- commercial participantspectrum. The most common “prop” trader makes a living trading


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Figure 10.5 Percent of non-commercial trading relative to that of commercial trading

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their own capital. Historically, many of these traders operated on thecommodity exchanges in the trading pits as “locals” (a pit trader whotrades for themself), assuming 100% of their own trading risks. Overthe years, the number of proprietary trading firms (groups of propri-etary traders within one organisation) has grown due to the rise inelectronic trading and also because of the profitability in profitsharing that owning a proprietary trading group can have. This busi-ness can be viewed as a private platform in which the owners of thebusiness hire talented individual traders, provide the overhead –including back office, administrative, accounting and trading tech-nology – for a share of any profits generated by the trader. Othertypes of proprietary traders sometimes get unfairly described as lessknowledgeable or hot money. These are individuals who may not besolely dependent on their success in trading commodities and at thesame time may not be aware of the significant risks that exist intrading commodity futures. Both the type of trader and the amountof capital traded is extremely diverse, from small accounts tradingunder US$100k to multi-million dollar programs. This group of part- time speculators participates in the same market as professionaltraders, and sometimes has very different views of the commoditythey are trading. They may be prone to participate in crowdedor popular trades. In agriculture markets, proprietary tradersand trading groups provide significant daily liquidity for otherparticipants.

Systematic and technical tradersSystematic and technical traders, much like the proprietary tradingsegment, are a vital part of the anatomy of the agriculture futuresand options markets as their trading volume provides commercialsand money managers essential liquidity that allows them to usestructured, fundamentally based strategies. Increased tradingvolume can narrow bid–offer price spreads, allowing all tradingparticipants a better trade execution. In the systematic world, thereare very few money managers that trade purely in agriculture; manyof the commodity systematic programs will allocate a risk buckettoward the agriculture markets in the range of 5–40% of their capital.This is largely due to targeted capacity of assets under managementfor the trading program relative to the capacity in the agriculturemarkets. Additionally, factors such as style, strategy and correlations


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in respect to the systematic program models may dictate how muchthe program allocates to agriculture markets. Pure discretionarytechnical traders can be more opportunistic about their risk alloca-tions across agriculture, which can provide outperformance relativeto other commodity sectors, resulting in an increased risk bucket.

Systematic programs trading in agriculture come in manydifferent forms, such as trend, multi- model, short- term momentumand relative value. There has been considerable growth in systematicprograms which incorporate historical seasonality of prices andspreads that have inherent fundamental ties. Some even will employeconometric supply and demand modeling, which evaluate funda-mental data produced for each commodity and then generates atrading signal by way of a proprietary algorithm. This more sterileand indirect fundamental trading from systematics can increase thecompetitive advantage over discretionary participants due to thediscipline in generating and maintaining the trade. At other times,this detachment can work against them as commodity fundamentalscan occasionally behave counter- seasonally and price patterns candiffer from historical norms – which can give the advantage to thediscretionary manager who has the ability to adapt to the changingenvironment. Counter- seasonal price behaviour can occur due tosupply/demand shocks. In turn, these shocks can be driven byissues such as supply chain logistics, global trade flow and currencyvaluations. On the macro side of things, geopolitical and economicrisks can alter price behaviour.

High levels of adaptability can also be a characteristic of a talentedchart technician who trades breakouts and mean- reversion strategiesacross the market. The chart technician relies on price data, behav-iour and chart formations to produce trading signals, andparticipates in price discovery and provides liquidity to the market.Often, the discretionary technical and fundamental participants whoare into the right side of a breakout do so more quickly. For thefundamental discretionary trader, this can be due to their funda-mental analysis, while for the discretionary technician this can bereactionary as their technical indicators (non- fundamental statisticsderived from the markets price data) signal them to enter a trade. Onthe other hand, multi- model and trend- based systematic programswill often be into a breakout or changing price environment onlyafter a trend in price can be confirmed.

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Commodity index and swap tradingA passive and increasingly common form of trade flows comes fromcommodity index fund and swap trading participation. Acommodity index is an index that tracks a basket of commodities tomeasure their performance. Commodity indexes are often traded onexchanges, allowing investors to gain easier access to commoditieswithout having to enter the futures markets. The value of theseindexes fluctuates based on their underlying commodities, and thisvalue can be traded on an exchange in much the same way as stockindex futures. There is a wide range of indexes on the market, each ofthem varying by their components. The Dow Jones- UBS CommodityIndex (DJUBSCI), which is traded on the Chicago MercantileExchange (CME), comprises 22 different commodities ranging fromaluminium to wheat. Index funds also vary in the way they areweighted; some indexes, for instance, are equally weighted whileothers have a predetermined, fixed weighting scheme. For example,the DJUBSCI is reweighted and rebalanced annually on a price–percentage basis. While index fund trading flows are passive, theyhave become more dynamic in their rebalancing and positioningacross the forward curves. Cleared commodity swap trading hasalso become a larger piece of agriculture trading business by bothfundamental and speculative entities. A commodity swap is aproduct whose exchanged cashflows are dependent on the price ofan underlying commodity. For commercial trading groups, acommodity swap is usually used to hedge against the price of acommodity. Therefore, in the case of a company that uses a lot ofcorn, it might use a commodity swap to secure a maximum price foroil. In return, the company receives payments based on the marketprice. There are also cleared, over- the- counter (OTC) commodityindex swaps that allow investors to have direct exposure to a varietyof commodity or agriculture- specific indexes. Commodity indexswap contracts are based on indexes that are among those mostclosely followed for investment performance in the commoditymarkets. Investors, asset managers and financial institutions usethem to track performance or as benchmarks for their activelymanaged accounts.


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TRADING IN AGRICULTURE MARKETSSpecialist traders in the agriculture sector use a wide range of non- directional strategies, such as calendar /inter- commodity spreads,and geographical and volatility focused arbitrage. The main driversof positive risk–reward opportunities from non- directional strategiescome from the identification of possible structural shifts in the shapeof the forward price curve or term structure, and the expectedvolatilities in between the spot month and deferred futures contracts.By identifying mispricing relative to forecasted expectationsbetween differentials in terms of price and/or volatility, specialisttraders can structure dynamic non- directional strategies across theforward price curve. Time horizons traded across agriculture tradi-tionally have ranged from 1–3 months up to 6–12 months in order toprovide sufficient time in which a strategy can reflect a trader’ssupply/demand forecast.

However, given increased volatility and short- term spikes incorrelation driven by outside market influences, some more tradi-tional intermediate to longterm discretionary fundamental tradershave adapted by ratcheting down their trade durations in responseto increased downside risks coupled with higher rates of return onunderlying strategies over short periods of time. Latterly, outsidemarket influences combined with increased speculative interestacross agriculture markets made more accessible by electronictrading have resulted in short periods of high correlation acrossmarkets. Traders and larger investment funds that manage a diverseset of exposures can now more easily increase and decrease riskacross all markets in a more efficient and timely manner. In the eventof sudden geopolitical or macroeconomic risks, these participantscan now enter and exit trades in a more concentrated fashion –causing cross- asset correlation to rise, typically only over shortperiods of time (inside of one day to one week). At times whereprices skew non- fundamental due to a “risk on/risk off” environ-ment, fundamental specialists are presented with the challenge ofappropriately managing risk while realising attractive tradingopportunities due to mispricing.

Market environments and volatilityThe wider range in volatility across agriculture markets hasincreased shorter- term trading by fundamental traders due to posi-

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tive risk–reward opportunities – ie, allowing traders to avoid tyingup margin dollars for long periods of time while still allowing themto continue trading a longterm theme. There are risks which make shorter- term strategies more challenging, predicated on the traderbeing able to quickly filter possible risk–reward opportunities, allwhile determining an appropriate size of risk allocation that is neces-sary to achieve their profit target. Psychologically, this style oftrading requires steady and consistent discipline due to the limitedtimeframe available to place the trade. Therefore, timing is critical inorder to have success in short trading frames. For traders aiming totrade in and out of deferred contract months, narrow time horizonscan particularly be a challenge as pockets of less liquidity and widebid–offer spreads can cause slippage and dilute trading returns. Forexample, a short- term trade in the 4th option of Kansas City Wheatmay look good on paper, but dried up liquidity as a result of apending crop report could cause wide bid–offer spreads, making itdifficult to implement or exit the strategy. In summary, most of thedifficulties in short- term trading are created by timing, lack of disci-pline and varying degrees of liquidity.

Figures 10.6 and 10.7 illustrate the average true range (ATR) that isa measure of volatility utilised by traders across the agriculturespace. Note the increased volatility in the ATR in both examplesshown.

Strategy selectionAs volume and open interest vary across agriculture markets, so dothe type of suitable strategies and accompanying risks. Generallyspeaking, total volumes and open interest in agriculture sub- sectorsrank in the following order, from largest to smallest: grain/oilseeds,softs/tropicals and livestock/dairy. Given varying liquidity andbehaviour, traders must identify what strategies are best suited forspecific markets. This is especially the case for broadly diversifiedcommodity traders who may prefer taking a one- size- fits- allapproach to implementing and managing strategies across markets.Specialist, individual market traders typically have a stronger handleon risk tolerances and go- to strategies.

For example, relative value strategies in livestock that focus moreon pricing anomalies across the curve and less on absolute directionwork extremely well. While in the grains and oilseeds, more of a mix


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Figure 10.6 Soybeans, weekly price and ATR

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Figure 10.7 No. 11 Sugar, weekly price and ATR


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in options volatility, inter/intra commodity spreads along with flatprice strategies can offer better returns. Inter- spread is a cross- commodity spread, in this case inside the grains and oilseeds sector(for example, selling wheat and buying corn). Intra- spreads involvespreads across the same commodity forward curve. Grains andoilseeds offer traders a wide array of choices in terms of strategy util-isation. The grains and oilseeds sector offer such a diverse andattractive set of opportunities, such as inter- commodity relativevalue – that is, a spread between two commodities. Due to strongcompetition for global production acres and substitutability factorsacross grains and oilseeds products, traders like to implement strate-gies that can exploit these fundamental relationships. Palm oil versuscanola oil or corn versus wheat are basic examples of global marketsthat not only compete for production capacity, but for demand. Thefundamental competition inside the sector and the importance ofthese markets globally is a strong reason why they offer relativelydeeper liquidity due to a more globally diverse set of participants.Other sectors similar to livestock can be found in the tropicalcommodity space, where sugar, coffee and cocoa specialists areheavily reliant on managing relative value spreads and geographicalarbitrage. Table 10.1 outlines five types of trading strategiescommonly implemented across the agriculture commodities space.

Correlation benefitBroadly diversified fundamental commodity traders have strongincentives for including agriculture strategies in their portfolio, notonly because of stark fundamental differences and attractive themesthat exist across the sector. The diversity within the sector createssignificant de- correlation that does not always exist in othercommodity sectors, such as energy and metals. Correlations betweenRBOB Gasoline, WTI Crude Oil, Brent Crude Oil or other energycommodities can be high with each other, and they all tend to beinfluenced by global macroeconomic headline risk and volatile stockmarket fluctuations. Metals markets such as copper, aluminium, zincand palladium also show high correlations to each other. On theother hand, across the agriculture markets one can find a number ofdifferent combinations that offer low correlations – for example, livecattle versus sugar and cocoa versus corn, which helps create naturaldiversification.

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Table 10.2 provides daily correlations across individual agricul-ture commodities and comparative to energy and metalscommodities. The correlations in this table also show the distinct de-


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Strategy types Description

#1 Directional

Example using futuresExample using options

Entering long or short futures and or optionsacross one or more contract months in one ormore commodities.

Outright long December corn futuresLong October No. 11 Sugar 22 cent calls andshort 28 cent calls.

#2 Calendar spreads

Example using futures

Example using options

Simultaneously entering a L/S futures and oroptions position across two different contractmonths in the same underlying months in thesame underlying commodity market.

Long March soybean futures and short Julysoybean futures.Long March soybean calls and long July soybeanputs.

#3 Geographical spreadarbitrage


Simultaneously entering a long and short futuresand/or options position across the same ordifferent contract months in two differentcommodities.

Long May Arabica coffee and short May Robustacoffee.

#4 Crush spreads



Simultaneously entering three legs in the futuresand/or options across three related commoditiesby entering two buys and one sell, or two sellsand one buy. Often related to production marginsof a particular commodity.

Soybean crush: Long soybeans, short soybeanmeal, short soybean oil.Cattle crush: Long feeder cattle, long corn andshort live cattle.

#5 Options volatility

Example

Going L/S or spread commodities based onimplied and historical volatilities.

Relative value: Long December wheat calls at25% volatility, short July wheat calls 40%volatility.

Table 10.1 Strategy types


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Commodity CC KC SB FC LH LC C W S SM BO CT RR CL HO NG HG SI

Cocoa (CC)

Arabica coffee (KC) 72.86

No. 11 Sugar (SB) 69.13 47.75

Feeder cattle (FC) –63.37 –29.38 –54.30

Lean hogs (LH) 3.81 29.79 –23.70 48.38

Live cattle (LC) –49.96 –6.60 –38.31 89.06 51.96

Corn (C) 25.84 46.29 0.49 16.58 63.64 30.97

Soft Red wheat (W) 47.38 23.57 32.39 –43.55 9.07 –32.20 57.21

Soybeans (S) 4.69 –9.04 –7.37 19.07 36.71 13.70 69.23 61.29

Soybean meal (SM) –14.21 –36.58 –23.40 20.08 23.46 9.78 50.74 55.22 94.10

Soybean oil (BO) 50.93 61.83 40.09 2.71 36.19 11.30 69.97 44.28 55.30 25.37

No. 2 cotton (CT) 83.91 72.27 53.60 –51.97 –0.69 –37.89 23.22 41.66 –0.66 –20.02 55.57

Rough rice (RR) 0.87 19.63 12.57 20.24 32.41 30.09 44.74 6.38 33.36 22.20 34.07 –25.90

WTI crude oil (CL) –2.08 31.11 –17.89 43.36 30.28 49.29 38.81 –10.17 10.66 –5.29 50.28 25.00 –9.47

Heating oil (HO) –11.22 33.30 –22.32 67.18 61.20 76.27 59.52 –15.80 23.82 5.90 52.91 –0.38 35.96 81.16

Natural gas (NG) 79.97 72.04 61.03 –74.30 –1.03 –59.59 16.69 41.39 –12.04 –28.39 33.90 69.12 1.17 –15.42 –25.36

Copper (HG) 80.12 63.62 67.44 –38.82 5.94 –34.09 30.69 43.83 17.80 5.54 72.08 77.27 –3.11 29.91 11.48 59.89

Silver (SI) 36.77 72.67 13.51 20.42 59.34 35.01 60.95 2.32 15.17 –11.59 67.59 34.22 42.24 55.74 71.68 29.83 47.29

Gold (GC) –49.45 –1.92 –34.87 72.60 43.93 76.59 29.81 –41.90 7.06 1.77 6.71 –56.82 59.29 27.02 66.97 –46.53 –37.45 47.76

Table 10.2 Daily correlations

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correlation across sectors such as tropical and livestock commodities(ie, cocoa versus live cattle).

Figure 10.8 shows the correlation benefits across various pairingsof agriculture commodities such as corn versus feeder cattle.

Investment flows, seasonality and weatherLow correlations across agriculture commodities are driven by market- specific supply/demand cycles, adverse weather andseasonality, which can create a rich set of diverse trading opportuni-ties. It should be noted that, with increased volumes andparticipants, more traders are leaning on strategies tied to a varietyof historical seasonality features, making it increasingly challengingto generate positive alpha. This has been witnessed in intra- commodity relative value, which is individual commodity spreads.For example, flat price seasonality on spot month lean hog and livecattle markets has pronounced impacts on spreads between thenearby and deferred prices across their respective forward curves.With access to 30-plus years of historical futures spread data, moreand more traders are implementing spreads based on these strongseasonal tendencies, thus at times diluting the risk–reward profilefor spread trades relative to years passed. The popularity of seasonalrelative value trades has also increased mean reverting opportunitiesfor technical contrarians and fundamental specialists that are able toidentify if a spread has moved too far too fast.

The most successful traders are able to decipher the changinginfluence of market participants, such as commercials, systematicand swaps (as detailed in the first section of this chapter), and howthey impact seasonality and contribute to short- and long- termcycles. For example, traditional or first- generation long- only swapsmanagers are known to roll long positions from the fifth to the ninthbusiness day of the month; however, over time, the market responseto this practice by other speculative participants has caused swaps orindex funds to roll long positions earlier and later. In fact, indexfunds have evolved their product suite, offering what are calledsecond- and third- generation products which adjust strategy forcurve contango or backwardation, attempting to capture alpha byshifting their directional bets dynamically across the curve over opti-mised time horizons. In this case, the product suite is the indexproducts created in addition to the conventional style index, such as


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Figure 10.8 90-day rolling correlations


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the second- and third- generation products which incorporatedynamic technical inputs such as open interest, volume and relativeperformance across contract months to determine which contractmonth to trade across the term structure. Traders who can filter andunderstand the impact of important market factors such as weather,demand and policy- related news (for example, changes in the regu-latory environment) in an efficient manner will have a leg up on theircompetition.

Figures 10.9 and 10.10 highlight the overall inconsistent correla-tions between individual agriculture commodities such as wheatand corn relative to gold and crude oil. Note that times of high corre-lation – for example, 90-day rolling correlation between corn andcrude oil +50% – are often due to short- term periods of investmentflows driven by macroeconomic data and speculative rebalancing.

For discretionary agriculture traders, the main take away fromunderstanding index fund activity is that successful strategies mustwithstand short- term periods of strong investment flows. Often,price differentials across the forward curve of agriculture commodi-ties can become skewed by such activity, by pushing prices out ofline with fundamental expectations. This type of price behaviourcaused by money flows often allows discretionary traders the oppor-tunity to place complimentary spread strategies which are designedto profit when the market corrects or reverts.

As previously mentioned, seasonal price behaviour can alsogenerate opportunities, as the underlying physical commodityvalues react to productions cycles, weather events and seasonaldemand tendencies. These factors in normal environments havecreated price activity that has produced consistent patterns over theyears (see Table 10.3 for more information about the planting ofgrains and oilseeds). For example, corn and soybean volatilityseasonally strengthen during the US spring planting season andpeak during the growing season. In the lean hog market during thelate spring and early summer, increased demand for pork coupledwith a seasonal slowdown in production historically have supportedhigher wholesale pork values and relatively higher lean hog futuresmarket prices in the summer contract months. Conversely, in theautumn and winter, increased hog weights due to cooler tempera-tures and cheaper /higher- quality feed create some of the best feedconversions per animal units of the year, typically resulting in large


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Figure 10.9 90-day rolling correlation between wheat and gold


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Figure 10.10 90-day rolling correlation between corn and crude oil


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market- ready supplies that drive cash and futures prices lowerduring the autumn and winter contract months.

Figure 10.11 illustrates the seasonality in lean hog spreads. Thisspecific spread is of the February versus October contracts (samecalendar year). Note the bold black line (2012) has moved lower inprice earlier then previous years seasonal price action, albeit in thesame direction.

Figures 10.12 and 10.13 show the US Drought Monitor for thebeginning of the 2012 US summer growing season and near the endof the US summer growing season. Note the beginning of the USsummer season was dry across much of the US Corn Belt but not indrought (as illustrated in Figure 10.12), while by the end of thesummer most all of the US Corn Belt had fallen into severe droughtconditions.

Figure 10.14 shows the corresponding US crop conditions for cornas the early season dryness evolved into a severe drought across theUS Corn Belt. Note the steep drop- off in conditions during the end ofJune and throughout July 2012. Figure 10.15 illustrates the corre-sponding response in corn prices as conditions were continuallydowngraded during the US summer growing season thusdecreasing production forecasts.

Fundamental data pointsWhen assessing agriculture markets, traders will often structurestrategies around specific data points or fundamental reports that areissued for each commodity. In the US, official government funda-mental supply/demand information is produced by the USDepartment of Agriculture (USDA). The USDA has many domesticfield offices and divisions, along with foreign agriculture attachésstationed in the world’s key agriculture producing regions. Thesedivisions are tasked with compiling, accounting and analysingimportant data involving such things as cash grain receipts, whole-sale and retail meat prices, survey results regarding prospectiveplantings and on- farm grain stocks. Every month, the USDA releasesa world agriculture supply demand estimate (WASDE) thatproduces global and domestic balance- sheet estimates for importantagriculture commodities. This is just one of the many fundamentalreports produced by the USDA. While some traders may not neces-sarily trade off of supply and demand data – for example, growing

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Figure 10.11 Lean hog February versus October spread (10-year seasonal)-1.5250

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Figure 10.12 US Drought Monitor (May 29, 2012)

Source: National Drought Mitigation Center at the University of Nebraska-Lincoln

Figure 10.13 US Drought Monitor (August 14, 2012)

Source: National Drought Mitigation Center at the University of Nebraska-Lincoln


conditions in corn – it is important to be cognisant of the release datesof fundamental reports, as price volatility can fluctuate sharply postthe release of such information. For traders, an equally importantendeavour, aside from analysing the report information, is to filterwhich sentiment indicators or reports best compliment their strategyand style. From a risk management standpoint, traders can judgesentiment more qualitatively by using their discretion regardingcertain data and market response. For example, a trader will gradesurveyed analyst expectations against actual reported information,as this type of methodology can provide them with a strong read on


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Figure 10.14 US crop progress and conditions: corn

Source: National Agricultural Statistics Service (NASS), crop progress report

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market sentiment which can, in turn, help in the management of riskafter the release of a fundamental report.

Global macro commodity managers will utilise macroeconomicindicators as an overlay to trading in agriculture commodities, bothfor risk management and portfolio/strategy structuring. In doing so,some traders will build proprietary models or take advantage ofexperience and intuition when assessing price activity in macromarkets such as stocks, US dollar index and energy and currencymarkets. Fundamentally, the USDA’s National AgricultureStatistical Service and World Supply and Demand Forecasts producea wide range of agriculture research, surveys and periodic reportsfor commodities such as corn, cotton, sugar and live cattle thatprovide important information to the global market, offering guid-ance for future supply/demand expectations. For example, in thegrain and oilseed commodities the USDA reports information onstocks, seeded acres and growing conditions. It is also important foragriculture traders to understand which commodities are most consumer- sensitive or can be most susceptible to macroeconomicrisks. Commodities such as live cattle (beef), cotton and orange juicecan quickly reflect changes in retail demand.

Aside from tracking underlying cash and retail values of thosecommodities, traders will also assess economic data in order to gainan understanding of consumer sentiment, such as US monthlyemployment data and the Consumer Confidence Index (CCI). On a less- frequent basis, country- specific policy changes regarding suchthings as global trade and renewable energy initiatives can have a

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Figure 10.15 Corn futures price, weekly line


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meaningful longterm impact on supply/demand and the globaltrade of agriculture commodities. For example, in 2011 the USentered into a bilateral free trade agreement with Colombia, whichcame into effect in 2012. This comprehensive trade agreement elimi-nates tariffs and other barriers to US exports, expands trade betweenthe two countries and promotes economic growth for both. TheInternational Trade Commission (ITC) has estimated that the tariffreductions in the agreement will expand exports of US goods bymore than US$1.1 billion, supporting thousands of additional USjobs. The ITC also projected that the agreement will increase US GDPby US$2.5 billion.

Many agricultural commodities also will benefit, as more than halfof US farm exports to Colombia will become duty- free immediately,and virtually all the remaining tariffs will be eliminated within 15years. Colombia will immediately eliminate duties on wheat, barley,soybeans, soybean meal and flour, high- quality beef, bacon, almostall fruit and vegetable products, wheat, peanuts, whey, cotton andthe vast majority of processed products. The agreement alsoprovides duty- free tariff rate quotas (TRQ) on standard beef, chickenleg quarters, dairy products, corn, sorghum, animal feeds, rice andsoybean oil. This is an example of a trade policy between two nationsthat will have a longterm impact on prices and the supply chain ofsome commodities.

Technical inputsFrom a technical chart trading standpoint, agriculture marketsprovide a good platform to trade a range of styles, includingbreakout, mean reverting and trend following. Technical indicatorssuch as Fibonacci retracements, relative strength index (RSI), marketprofile and a variety of moving averages are utilised by traders.Studying open interest and volume as well as viewing charts acrossdifferent time horizons – such as intraday, daily, weekly andmonthly – help put medium- to longterm strategies into perspec-tive.

For fundamental discretionary traders, technical indicators do notnecessarily have to generate trade ideas, but rather provide a confir-mation for the entry or exit of a strategy. An example would be atrader who has an underlying bearish directional bias in a marketbased on demand concerns, and at the same time recognises that the


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RSI indicator has fallen below the overbought level; just below thisprice level, if the price weakness can be sustained, the price will beable to drop below both the 20- and 100-day moving averages. Thisconfluence of signals can help confirm a potential entry point for thebearish directional strategy. Using this methodology helps in addingdiscipline, as it forces traders to adhere to the price action relative tothe technical signals, which can often indicate future longer- termprice movements before actual fundamental developments can berealised. This is an important filter that can temper traders’ expecta-tions behind their fundamental conviction about a commoditymarket, and helps them to be patient in expressing strong convic-tions. Overall, there are a variety of technical indicators that can beused in assessing the agriculture markets and, most importantly,they offer a non- biased overlay to discretionary decision- making.

Figure 10.16 illustrates a combination of technical indicators thatcan be used to signal a trading opportunity. Note, the movingaverage cross as the 20-day crosses over the 100-day to the downside.Additionally, in advance of this cross the RSI had been testing over-bought territory, which indicates that the market maybe reaching atop. In the case of this illustration, this was true and the movingaverage cross provided a confirmation and a sell signal. Figure 10.17illustrates a combination of Fibonacci retracement and movingaverage cross that can be used to signal a trading opportunity andprovide the trader with a back drop in which to balance expectations.

STRATEGIES AT PLAY IN THE AGRICULTURE MARKETSThe previous section provided a general description of the types,behaviour and objectives of traders in the agriculture markets. Thissection will categorise the specific types of strategies being employedby those participants, along with their risks and management of suchstrategies. There are five main strategy types covered: directional,calendar spreads, geographical arbitrage, crush spreads and optionsvolatility. Methodologies used in trading strategies involve theresearch and analysis of seasonality, forward curve structure andfundamental factors. Using those factors, traders are then taskedwith choosing the most suitable strategy that aligns with their funda-mental thesis or return objective.

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Figure 10.16 Confluence of technical indicators signalling a trading opportunity


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Figure 10.17 Use of Fibonacci retracement and moving average cross to identify a trading opportunity


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DirectionalDirectional trading strategies are a very common style of tradeemployed by both commercial and non- commercial trading partici-pants. Commercial traders using this strategy will utilise flat pricetrades to market or hedge production or commodity risk. In itssimplest form, this can be implemented as a flat price futures buy orsell or as a hedge against an underlying physical commodity expo-sure. For non- commercial traders, the flat price exposure is a sourceof beta that compliments their speculative ideas on future pricedirection. Flat price trades among the non- commercial and commer-cial trading community can be expressed in many different forms.Different style of directional bets include options spreads, risk rever-sals such as owning a call and selling a put against the sameunderlying contract month, and synthetic options that involvetrading futures and options in the same contract month.

Prior to entering a directional trade, traders must evaluate avariety of risk–reward factors such as selecting the appropriatecontract month across the forward curve and choosing the expectedtime horizons for the trade, while also establishing risk allocation,profit targets and stop/loss level(s). Experienced traders looking toplace a directional bet in an agriculture market are always aware ofthe calendar as seasonality plays a large role in the risk profile of adirectional trade. After taking into account seasonal factors, thetrader will determine which contract month can best express theirideas on fundamental price movements. Since many commoditiesfutures in the agriculture sector span multiple crop years, tradershave to make sure their fundamental thesis ties to the appropriatetime horizon in which they are trading.

For example, during the month of May, an oilseed trader becomesbearish and decides to sell the US soybean market on expectationsfor an above- average new crop production, but sells the old crop Julycontract in order to express their bearishness; while being short is thecorrect directional position, in this case it is not the correct contractmonth or season to be short based on the fundamental thesis. Thistrader is taking significant risk by holding a short position in an oldcrop contract that may be trading off of different supply and demandfundamentals. Additionally, the risk–reward expectation for such atrade could greatly underperform due to muted trade duration asthe July contract will have expired before new crop production is


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harvested, therefore never allowing the market to fully price in thetrader’s fundamental price forecast. See Table 10.3, as the global croptimetable shows traditional planting and harvest time periods forcorn, soybeans and wheat produced around the world.

After determining the best point to be positioned, traders mustdecide how much risk or what level of conviction they have in thetrade. This determination comes from a confluence of factorsinvolving the price forecast, market volatility and expectations fortrade duration. If a trader has strong confidence in their fundamentalthesis and longterm price forecast, but the market volatility is highdue to shorter- term factors, they may take a “scale in” approach totheir directional position. Scaling into a strategy is a methodology inwhich a trader increases risk by adding positions to the existingstrategy. This allows them to ultimately reach a high conviction orrisk allocation, while withstanding near- term volatility pressures.Regardless of the conservative approach, traders still need to deter-mine levels in which they will stop out of the directional position andgo to the sidelines. While liquidity in executing directional trades isoften better than liquidity available for more complex relative valuestrategies, the returns on an unhedged directional trade can often bemore volatile, which makes risk management and position sizingimportant.

Figure 10.18 offers an example of this type of risk differential,showing the difference in ATR between an old/new crop cornspread versus the individual components of the spread. Note theATR of the individual components, in this case July 2012 andDecember 2012 corn traded as much as two or three times more on adaily basis than that of the July–December 2012 calendar spread.Additionally, note the convergence and divergence of the spreadrelative to the individual components, as the tug of war between oldand new crop supply/demand played out over time.

Calendar spreadsCalendar spread strategies have grown in popularity among thespeculative trading community due to their embedded alpha gener-ation and strong relationship with fundamental price relationships.As defined in Table 10.3, a calendar spread trade is a strategy inwhich a buy and sell are simultaneously placed across the samecommodity futures curve. Calendar spreads provide fundamental

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COMMODITY

INVESTIN

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Futures contracts symbols F G H J K M N Q U V X Z

Wheat Jan Feb March Apr May June July Aug Sep Oct Nov Dec

US Winter Harvests Plants

Soft Red Winter (W) WH WK WN WU WZ

Hard Red Winter (KW) KWH KWK KWN KWU KWZ

US Spring Plants Harvests

Hard Red Spring (MW) MWH MWK MWN MWU MWZ

Canada Plants Harvests

France Harvests Plants

Milling Wheat (PM) PMF PMH PMK PMQ PMX

Germany Harvests Plants

UK Harvests Plants

Ukraine Harvests Plants

Turkey Harvests Plants

Egypt Harvests Plants

Kazakhistan Harvests Plants

Russia Winter Harvests Plants

Russia Spring Plants Harvests

Iran Harvests Plants

Pakistan Harvests Plants

India Harvests Plants

China Harvests Plants

Table 10.3 Global crop timetable and futures contracts

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Australia Plants Harvests

Brazil Plants Harvests

Argentina Plants Harvests

Soybeans Jan Feb March Apr May June July Aug Sep Oct Nov Dec

Brazil Harvests Plants

Argentina Harvests Plants

US Plants Harvests

Soybeans (S) SF SH SK SN SQ SX

China Plants Harvests

Corn Jan Feb March Apr May June July Aug Sep Oct Nov Dec

Brazil Harvests Plants

Argentina Harvests Plants

US Plants Harvests

Corn (C) CH CK CN CU CZ

China (North) Plants Harvests

China (South) Plants Harvests

France Plants Harvests

Spain Plants Harvests

Ukraine Plants Harvests

Russia Plants

India Plants Harvests

South Africa Harvests Plants

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Figure 10.18 20-day ATR: old versus new crop corn spread relative to outright contracts


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traders with a non- directional bias and the opportunity to trade rela-tive fundamentals across the term structure of a commodity. Non- directional reasons to trade calendar spreads can involve pricerelationships regarding cash basis and seasonality.

From a directional standpoint, some traders may entertain tradinga calendar spread as a hedge against being directionally positionedat different points on the futures curve or as a more conservative beton directional price expectations against one leg of the spread. Thereare many possible fundamental and technical drivers for tradingcalendar spreads. Some of the most compelling calendar spreadstrategies can be seen in Table 10.4.

Geographical spread arbitrageGeographical arbitrage is another form of inter- commodity spread inwhich a trader buys and sells the same type of commodity producedacross different regions of the world. These commodity futurescontracts often exist on different exchanges and have differentquality or grade characteristics. An example of trading a geograph-ical spread would be purchasing Arabica coffee and selling Robustacoffee. Trading a geographical arbitrage strategy is mainly carriedout by fundamental specialists due to the high level of specificknowledge needed to understand the pricing relationships. For tech-nical traders, this type of commodity spread can have appeal from amean reverting standpoint, as the trader will seek opportunitieswhen the spread between the two related commodities reachesextreme levels. Purely trading geographical arbitrage from a tech-nical standpoint, however, does come with significant risk as the flatprice direction of an individual leg of the spread can move oppo-sitely for sustained periods of time based on specific micro- fundamental factors. Other inter- commodity spreads canhave strong quality- based and seasonal aspects, such as trading lower- grade US soft red winter wheat versus higher- grade hard redspring wheat.

See Table 10.5, which details fundamental drivers for trading inter- commodity or geographical arbitrage. The same technicaldrivers can apply for these types of spreads as that outlined forcalendar spreads.

Figure 10.19 shows how the soybean to corn ratio provides anexample of blending technical, seasonals and fundamentals while

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assessing a possible geographical arbitrage spread opportunity. The eight- year seasonals show the behaviour of the ratio to be ratherinconsistent, but do provide a range of expectations. It is up to thetrader to deduce what fundamental drivers will result in the futureperformance of such types of geographical arbitrage, as each yearcan be extremely different from the next.


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Fundamentalreasons

#1 Old crop S&D versus new crop S&D forecasts.

#2 Individual flat price biases: bullish and bearish acrossdifferent time horizons.

#3 Seasonality of basis (cash minus futures) versusforecasted basis.

#4 End-user and producer profit margins impact onunderlying cash values.

Technical reasons #1 Seasonality of the spread differential.

#2 Bull or bear spread as a hedge against directional bias.

#3 Bull or bear spread as a theoretical conservative bet ondirectonal bias.

#4 Commitment of traders data.

Geographicalspread arbitrage

Example usingfutures

Simultaneously entering a long and short futures and oroptions position across the same or different contractmonths in two different commodities.

Long May Arabica coffee and short May Robusta coffee.

Crush spreads

Example usingfuturesExample usingfutures

Simultaneously entering three legs in the futures and oroptions across three related commodities by entering twobuys and one sell, or two sells and one buy. Often relatedto production margins of a particular commodity.

Soybean crush: long soybeans, short soybean meal, shortsoybean oil.Cattle crush: long feeder cattle, long corn and short livecattle.

Options volatility

Example

Going L/S or spread commodities based on implied andhistorical volatilities.

Relative value: long December wheat calls at 25%volatility, short July wheat calls 40% volatility.

Table 10.4 Strategy types: calendar spreads


Crush spreadsA crush spread is a form of arbitrage predominately used bycommercial traders in order to manage production- related marginrisk. Typically, a crush spread includes two or three individualcomponents. Speculative participants with a keen understanding ofproduction margins often like to implement crush or reverse crushspreads as a proxy as it allows them to participate synthetically in

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Fundamentalreasons

#1 Trading differences in supply and demand acrossdifferent regions.

#2 Trading differences in quality grades of a similarcommodity.

#3 Trading differences in localised demand and its impacton underlying cash prices.

#4 Trading the flow of a similar or competing commoditybased on supply, demand and logistics.

Technical reasons #1 Seasonality of the spread differential.






Simultaneously entering a long and short futures and oroptions position across the same or different contract intwo different commodities.


Crush spreads



Soybean crush: long soybeans, short soybean meal, shortsoybean oilCattle crush: long feeder cattle, long corn and short livecattle

Options volatility

Example



Table 10.5 Strategy types: geographic arbitrage



290Source: DTN ProphetX

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Figure 10.19 March 2013 soybeans to corn ratio (eight-year seasonal)

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physical commodity margins. A good example of a crush spread inagriculture can be found in soybeans, where a trader can replicate aphysical soybean crushing plant, by purchasing soybeans and sellingthe output including soybean meal and soybean oil contracts. Otheragriculture commodities in which crush trading is popular are live-stock, where producers in the pork and beef industries will actively

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Fundamentalreasons

#1 Trading the production economics or margins of aspecific commodity.

#2 Trading differences in margins of a particularcommodity across the forward curve via calendar crushspreads.

#3 Trading the reverse crush by taking the opposing side ofthe relationship typically held by the physical commodityproducer.

Technical reasons #1 Seasonaility of the spread differential.






Simultaneously entering a long and short futures and oroptions position across the same or different contract intwo different commodities.


Crush spreads




Options volatility

Example



Table 10.6 Strategy types: crush spreads


purchase soybean meal and corn against the lean hog or live cattlefutures. Table 10.6 details the fundamental drivers for placing acrush trade.

Options volatilityTrading of options volatility strategies offers traders with a widerange of dynamic opportunities on a standalone basis, and also whencoupled with futures directional and relative value spreads. Tradingopportunities in options include individual commodity spreads anddirection or across commodities in the form of arbitrage.Experienced relative value option specialists in agriculture arefrequently able to find attractive opportunities by trading differen-tials in volatility on an inter/intra commodity basis. Additionalstrategies involve trading put versus call skews across one or morecontract months in one or multiple commodities.

Directional trading is also prominent in options by way of owningnet, absolute gamma or premium in any contract month. An exampleof a net gamma options play would be to own a bull call spread inwhich the trader purchases an at- the- money call and sells an outof- the money call against it at a slightly lesser value, resulting in a netpayment of premium and a net long volatility position. The numberof options strategies which can be expressed across agriculturemarkets is seemingly endless, and they provide traders with uniqueand niche opportunities to generate profitable returns. Table 10.7details the three different types of options strategies that are oftentraded across the agriculture space.

CONCLUSIONThe speed of information flow and the sudden correlations acrossmarkets from time to time can in some ways be attributed to thesuccess and growth of electronic trading, as a more diverse set ofspeculative participants from around the world have virtual aroundthe clock access to trade and manage risk in most commoditymarkets. In general, this new normal in price behaviour andvolatility offers more opportunities for multi- strategy and relativevalue driven traders. Periods of high volatility and relatively widerprice ranges can frequently distort prices relative to perceived funda-mentals, which can create unique opportunities. These types of priceenvironments are often associated with adverse market conditions;


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those traders which can realise the difference between an event thatis normally anticipated (seasonal or fundamental data point) and onethat is rare and unexpected will find success in trading andmanaging risk in agriculture markets.

The ability to recognise, filter, and accurately assess changingmarket developments is critical in making trading decisions. With

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Price distribution Trading the difference or skew in the options pricingagainst same underlying futures contract.

This can be done by trading straddles or strangles based onoptions pricing differentials.

Relative volatility Trading statistical differences in volatility betweencorrelated and or non-correlated commodities.

This can be done by selling relatively high volatility in onecommodity and purchasing relatively cheap volatility inanother.

Trading the difference between implied volatility andhistorical volatility in one commodity.

This can be done by buying or selling volatility in onecommodity based on the relationship between implied andhistorical volatility.

Relative value


Trading the price relationship of an underlying futuresspread by way of using options.This can be done by trading put, call and butterfly optionsspreads on an inter/intra commodity basis.


Crush spreads




Options volatility

Example



Table 10.7 Strategy types: options


the globalisation of agribusiness and trade expected to grow, so willthe expansion and enhancement of global agriculture futures andoptions markets, which will further increase the set of trading oppor-tunities available to all traders.


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Many institutional investors now allocate to commodities alongsidethe traditional asset classes of equities and fixed income, based onthe primary motivations of diversification and protection against therisk of inflation. While commodities have indeed been less correlatedto equities and many other risky assets (and offered comparablerisk–return trade- offs, see Gorton and Rouwenhorst, 2008), in thischapter we will show that those diversification benefits may beenhanced through a deeper understanding of how and why differentpassive and active strategies tend to perform in different marketenvironments. Our conceptual frame of reference views most invest-ment strategies as either convergent or divergent – performing wellin either “normal” or more dislocated periods – and is applicable atany level of aggregation, from individual securities to sectors andmarkets as well as combinations of asset classes.

We will begin with a brief review of commodity benchmarks,highlighting the degree to which they are considerably less passivethan traditional equity and fixed income benchmarks, as well as anoverview of active approaches to commodity investing. We will thenpresent a detailed discussion of the convergent/divergent paradigm,and demonstrate with an example how it can be applied within anactive commodities strategy, underscoring its effectiveness duringthe most recent global financial crisis when traditional approaches todiversification largely failed.

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Quantitative Approaches to CapturingCommodity Risk Premiums

Mark Hooker and Paul LucekState Street Global Advisors and SSARIS Advisors


REVIEW OF COMMODITY BENCHMARKS AND OVERVIEW OFACTIVE APPROACHES TO COMMODITY INVESTINGCommodity benchmarks differ from their stock and bond counter-parts in two main ways. First, there is no straightforward analogue tomarket capitalisation for determining component weights – eg, theindex weight of crude oil relative to that of soymeal. Instead, variouscommodity indexes, including the prominent DJ- UBS and GSCIindexes, use factors such as trading volumes of the futures contractsand worldwide production statistics to derive individual componentweights. The collection of production and trading volume data isalso subject to a series of decisions regarding sources, timing, datarevisions and additional factors. Commodity benchmarks are there-fore subject to a much greater degree of subjectivity in determiningbenchmark weights. Second, since commodity futures contractshave a limited time before their expiration, a set of rules must beconstructed to determine when contracts are rolled from the nearmonth to a later- dated month. These rules must indicate whetheradjacent contract months are used, or if certain months are skipped.In order to minimise the impact of the change over from one contractto the next, the roll usually occurs over a period of several days,which also must be specified within the rules. In this sense, passivecommodity investing should be considered semi- active.

Active commodities strategiesThe broad universe of commodity futures contracts exhibits a verylow average correlation of its components, a wide dispersion of indi-vidual commodity futures returns, high volatility and largedrawdowns. For example, pairwise correlations between industrygroup returns in the MSCI World Equity Index average about 0.5,while analogous correlations between the constituents of the DJ- UBScommodity index are closer to 0.2, and volatilities average about 50%greater for commodities, at 30% versus 20% for equities.1 Thisvolatility and dispersion provides considerable opportunity forskilled active managers to implement strategies that can providealpha over commodity index beta, while using risk controls toreduce volatility and preserve capital. The combination of theseopportunities for active managers, in conjunction with the semi- active nature of the commodity index providers, makes a strong casefor active commodity management within an institutional portfolio.


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Strategies for active commodity management generally fall withintwo categories: discretionary and systematic. In this chapter, we willfocus on systematic strategies, which are quantitative in nature,using historical data to develop models that drive trading decisionsdirectly from feeding live data through the models. Quantitativetrading strategies are of course sensitive to the performance of theirunderlying models, which typically have positive periods wherethey perform as designed, and negative periods where factorsexternal to those included within the model drive more of the marketmovements. For this reason, diversification of differently designedtrading models and approaches can greatly enhance the overall effi-ciency of a quantitatively managed portfolio of commodity futures.

CONVERGENT AND DIVERGENT STRATEGIESThe convergent/divergent paradigm was introduced in Chung,Rosenberg and Tomeo (2004). It focuses on distribution of monthlyreturns, their statistical properties and the cross- correlationsbetween those returns and aspects of the market environment.

Convergent return streams have monthly return distributionsrepresented by the shaded curve in Figure 11.1, and are generallyderived from fundamental or value- based methodologies. In aconvergent strategy, a manager often calculates an intrinsic or “fair”value for an asset: a target price. If the asset is trading below theintrinsic target price, the manager would seek to buy the under-valued asset. Conversely, if the asset is trading above the intrinsictarget price, the manager would seek to sell the overvalued asset.The goal of the traded position would be for the current asset price toconverge to the target price and generate a positive return. Themanager seeks over- or undervalued assets with the expectation thatthese assets will move toward their fair values, allowing them toexploit the temporary mispricing. Convergent strategies tend to bebased upon fundamental methodologies, although certain quantita-tive methods – for example, mean reversion strategies – also tend toproduce convergent return streams.

Most passive investments – indexes – are also convergent in theirnature. Passive index investing has the goal of capturing the riskpremium of the asset class. Fixed income risk premiums come fromcredit and duration risks. The equity risk premium is associated withearnings growth. Commodity risk premiums are derived from the

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inventory levels of the underlying commodities (Gorton, Hayashiand Rouwenhorst, 2006). A passive investor in these indexes islooking for the index return to converge to the expected riskpremium. Furthermore, as emphasised in Ilmanen (2011), asset classrisk premiums tend to be larger for investments that performpoorly during crisis periods (“bad times”) so that they have somecharacteristics of selling insurance. Assets that produce positivereturns in normal periods and suffer large losses in crisis periods areconvergent.

Convergent investments normally exhibit fairly consistent returnstreams with a high frequency of small positive returns. Their consis-tency and low volatility can give them high Sharpe ratios and makethese types of strategies very attractive to investors. One of the weak-nesses that convergent strategies exhibit is their negative skewness.As shown in Figure 11.1, the convergent return distribution has asignificant left- hand fat tail. After a series of several monthly returnsclumped around zero with a positive mean, market events can occurwhere convergent strategies experience significantly (2–3 standarddeviation or more) negative returns. These events tend to be termed“crisis events”, such as the 1987 stock market crash, the 1997/98Asian currency/Russian debt/LTCM crises, the 2007–08 global


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Figure 11.1 Convergent and divergent monthly return distributions

Source: SSARIS/SSgA

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financial crisis (GFC) and the 2011 European debt crisis. During thesecrisis events, fundamental and value- based strategies often havesignificantly negative performance. When behavioural financeconcepts such as “fear” and “greed” drive market movements, assetprices succumb to panic and overshoot their fair values. Convergentapproaches have great difficulty in this type of crisis environmentbecause, as an asset price drops due to panic and fear, the convergentmodel suggests that the asset is an even more attractive buy. Themodel will eventually be correct when the asset price hits a bottomand the crisis passes, but trading positions taken along the way mayexperience heavy losses. Unrealised losses in commodity futurescontracts force future commission merchants (FCMs) to issue margincalls. If further capital is not produced, the manager’s positions areliquidated and the losses are realised. This situation was aptlydescribed by a quote attributed to John Maynard Keynes: “Marketscan remain irrational longer than you can remain solvent.”

A prime example of crisis price dynamics is illustrated in Figure11.2: the price of the December 2008 Nymex crude oil futurescontract. Within a span of 10 months, the contract rose fromUS$84.62 per barrel to US$146.68, before sinking to US$49.62.Somewhere within the range of a 73% run- up and a 66% decline wasan intrinsic value for crude oil, but the price had been driven farbeyond fair value in both directions.

During market dislocations, such large directional moves arecommon. The most striking characteristic of these crisis events is anincrease in market volatility (almost, by definition, a crisis eventincludes an increase in market volatility). A secondary effect is anincrease in magnitudes of correlations. Assets that previously exhib-ited low correlations tend to become correlated during a crisis. Atertiary effect is the increase in serial price correlation or autocorrela-tion within individual markets. Table 11.1 shows these three effectsduring the 2007–08 GFC: over 2007, the DJ- UBS index had an annu-alised volatility of 12%, average correlation of its components of 0.15and near- zero autocorrelation of those components’ returns. During2008, each of those statistics more than doubled, with serial correla-tion rising more than five- fold. When markets become drivenbeyond fair value and fundamental convergent methodologies fail, itis the divergent category of strategies that can capitalise on theincrease in market autocorrelation.


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Divergent strategies capitalise on directional market moves. Thesestrategies, which include momentum and other trend- followingapproaches, seek to take positions based upon the analysis of histor-ical price data and the direction the market is currently moving. Theytend to perform well as the rate of change in volatility levelsincreases, which is also when markets tend to exhibit morepronounced degrees of autocorrelation.

While in normal or rational market environments convergent anddivergent strategies are usually uncorrelated, during crisis eventsand irrational market environments the two become negativelycorrelated. Divergent strategies perform well and experience right- hand tail events at the same time that convergent strategies havetheir negative left- hand tail events. When markets exhibit strongdirectional moves such as with crude oil in 2008, momentum/trendstrategies can capitalise on the shifts away from fair value. Divergentstrategies in this sense are directly opposite to convergent strategies.As crude oil rose in 2008 and became more and more expensive,


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Figure 11.2 WTI crude oil contract (December 2008)

Source: Commodity Systems Inc

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convergent models saw the asset as overpriced. Divergent modelssaw the increasing price as a trend and favoured the asset. Divergentstrategies require a significant retracement in market price beforethey will change their assessment of a market trend. Similarly,during the market decline in the second half of 2008, as crude oilbecame less and less expensive, convergent models favoured theasset, while the strong downward move forced divergent trendmodels to sell the asset.

The contrasting styles of convergent and divergent strategies andthe diversification of their respective return stream distributionsleads to the benefit of allocating to both strategies. Commoditymarkets facilitate this diversification, because with the wide disper-sion and low average correlation within the commodity universe,divergent events can occur in one commodity sector while othersectors exhibit a largely convergent environment. For example, in2012 significant bullish moves in agricultural markets took place dueto the drought conditions in the US, with corn up more than 60% andsoybeans, meal and oil up roughly 25% between mid- June and earlyAugust. These markets exhibited strong directional trends thatdivergent strategies were able to capture, while other market sectorsprovided strong performance from convergent strategies.Investment strategies that allocate to successful convergent anddivergent techniques will outperform those that allocate to only oneor the other on a risk- adjusted basis.

A CONVERGENT/DIVERGENT ACTIVE COMMODITIES EXAMPLEIn order to demonstrate the beneficial effect of a convergent/diver-gent quantitative approach to commodity investing, we present hereexamples of the two trading methodologies, their individual perfor-mance relative to a passive benchmark index and the increased


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Table 11.1 DJ- UBS index – trailing 52-week statistics, weekly data points

Annualised Average Averagestandard correlation of auto-correlationdeviation components within components

2/1/2008 0.120 0.150 0.02531/12/2008 0.293 0.429 0.145increase 2.44× 2.87× 5.81×

Source: DJ- UBS index


efficiency achieved by their combination. The sample period for thisanalysis is January 1996 to October 2012.

A simple divergent momentum strategy described by Spurgin(1999) uses three lookback timeframes in order to determine long orshort positions in each market traded. The three timeframes – 15, 27and 55 days – were selected to best replicate an index of commoditytrading advisor (CTA) managers trading a broad range of futurescontracts. The signal derived from the momentum strategy is gener-ated by comparing the price at time t with the price at a fixed numberof days ago. For example, in the 15-day system, if Pt > Pt-15, themarket trend is considered to be positive and a long position is taken.If Pt<Pt-15, the market direction is considered to be negative and ashort position is taken. If the prices are the same, no market directionis indicated and no position is taken. Trading signals from the threelookback timeframes are averaged. In this chapter, we modify themomentum trading methodology by not taking any net short posi-tions. If an aggregate signal is negative, indicating a short, a flatposition is taken instead. This modification has the effect of makingthe momentum system track the index more closely, since the indexitself does not take short positions. Signals for each of the 20 compo-nents of the DJ- UBS index are generated on a daily basis. Profits andlosses were calculated by equally weighting the system’s returnstreams on each of the 20 components and rebalancing on a monthlybasis. The DJ- UBS index is presented as a benchmark for comparisonin Table 11.2. The momentum strategy outperforms the DJ- UBSindex while exhibiting less than half of its volatility.

For convergent strategies, we investigate two quantitativeapproaches. The first relates to the Gorton–Hayashi–Rouwenhorstview that inventories are the key fundamental for commodityfutures returns, and that the degree of contango or backwardation inthe futures curve reflects those fundamentals. Here, we construct analpha signal for each commodity each month. That alpha is a simplefunction of the relationship between the second- from- expirationfutures price and the next- to- expiration contract price.

The second convergent approach models each commodity’sreturns using a Markov switching model. Here, each commodity’smonthly return is assumed to be drawn from one of three differentnormal distributions, called regimes, each with its own mean andvariance. The process moves from one distribution to another (a


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differentnormaldistribution contributing the returndraw) accordingto a set of Markov transition probabilities. Expected returns arecomputed each month for each commodity by multiplying the esti-matedprobabilities of being in each regimeby their respectivemeans.Those means, variances and transition probabilities are estimatedperiodically through time and evolve as new data accumulates.

The alpha estimates from these two convergent approaches areaveraged, and then run through a mean- variance optimisation toproduce over- and under- weights relative to the DJ- UBS CommodityIndex benchmark weights that also satisfy various constraints, suchas limits on short positions and on active exposures to individualcommodities and commodity groups. The return stream produced issummarised in the final column of Table 11.2. The convergentstrategy also outperformed the DJ- UBS index, but with about a thirdgreater volatility, for a return- to- risk ratio somewhere in between theindex and the divergent strategy.

As mentioned above, convergent strategies tend to perform wellwhen assets move toward intrinsic or fair valuations, which tends tobe when markets are in more normal regimes or recovering towardnormality after severe shocks. By contrast, divergent strategies tendto perform well during crises when markets are driven beyond fairvalues. Consistent with this logic, the convergent backwardation–Markov switching strategy had average excess returns that wereslightly negative in 2008 as markets reacted severely to the globalfinancial crisis, but outperformed by more than 17 percentage points


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Table 11.2 Performance of divergent and convergent strategies versus the DJ- UBS

DJ-UBS Divergent Convergent Combinedcommodity index momentum backwardation & convergent &total return strategy (15-27-55 regime switching divergent strategy

day lookbacks) strategy (50%/50%)*

Annualisedreturn 0.048 0.053 0.106 0.083

Annualisedstandarddeviation 0.167 0.077 0.203 0.132

Return/riskratio 0.289 0.681 0.523 0.626

Data based upon DJ- UBS Commodity Index returns (January 1996–October 2012)* The combined convergent/divergent strategy is rebalanced monthly


in 2009 as markets dramatically recovered. In contrast, the divergentmomentum strategy outperformed the index by more than 45% in2008, and underperformed by more than 12% in 2009.

The most striking aspect of the convergent and divergent excessreturn streams is how their correlation varies across time. The overallcorrelation is slightly negative, at –0.16. However, during the crisisand recovery period of 2008–09, it rose dramatically (in absoluteterms) to –0.51, demonstrating the very powerful benefit through thediversification achieved by combining these two return streams. Aprogramme allocating one half of the portfolio to each strategy andrebalanced on a monthly basis showed an 8.3% annualised rate ofreturn with a 13% annualised standard deviation (outperforming thebenchmark index by more than 3%, with volatility reduced by morethan 3%) in a backtest over the test period of January 1996–October2012.

CONCLUSIONMost approaches to diversification focus on average correlationsover periods of time that encompass full market cycles – indeed, thatis often by design, implicitly assuming that fluctuations in correla-tions through time are primarily noise and so should be averagedout. That approach has disappointed investors during crises asrealised correlations, particularly within the dominant convergentset, “all go toward one”. The convergent/divergent paradigm, bycontrast, views variations in correlations – particularly as a functionof market stress and stability – as fundamental attributes of astrategy that should be incorporated into portfolio design.

The example in this chapter showed that combining convergentand divergent active quantitative strategies can provide significantalpha over a passive index and stabilise the overall return stream ofthe portfolio, especially during crisis event periods. In practice, wehave seen this concept can apply to fundamental active strategies,passive strategies, other asset classes and the overall portfolio levelas well, where it may help overcome the challenge that diversifica-tion has often worked least well when it has been needed most.

1 There are 23 industry groups in the MSCI GICS system versus 20 commodities in DJ- UBS, sothis level of aggregation makes them reasonably comparable. Correlations and volatilitiesare computed using monthly returns from 1999 to 2009.


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REFERENCES

Chung, S., M. Rosenberg and J. Tomeo, 2004, “Hedge Fund of Fund Allocations Using aConvergent and Divergent Strategy Approach”, The Journal of Alternative Investments,Summer.

Gorton, G., F. Hayashi and K. G. Rouwenhorst, 2007, “The Fundamentals of CommodityFutures Returns”, Yale ICF Working Paper No. 07–08.

Gorton, G. and K. G. Rouwenhorst, 2006, “Facts and Fantasies about CommodityFutures”, Financial Analysts Journal, 62(2), March/April.

Ilmanen, A., 2011, Expected Returns: An Investor’s Guide to Harvesting Market Rewards(Hoboken, NJ: Wiley).

Spurgin, R., 1999, “A Benchmark for Commodity Trading Advisor Performance”, Journalof Alternative Investments, Fall.


Investors can get exposure to market- neutral returns in commodities– commodity alpha – in many different ways. Active management is,of course, one means of obtaining exposure to commodity alpha.However, as commodities have grown as an asset class, a largenumber of rules- based strategies (examples of which will beprovided throughout the chapter) designed to generate market- neutral returns have emerged. These systematic commodity alphastrategies exploit structural characteristics of commodity markets,and will be referred to here as “structural commodity alpha strate-gies”. They are not a source of risk- free returns. Their returns are thereward for taking on risks that other market participants areunwilling to take on a systematic basis.The main goal of this chapter is to outline the major investment

themes among these structural commodity alpha strategies andsuggest a simple methodology for combining them into an absolutereturn portfolio. After briefly introducing the three major investmentthemes in commodities alpha, this chapter will examine each in turn.First, it will cover curve placement strategies, discussing their ratio-nale and relationship to storage economics. This section will alsoexplore how curve placement strategies are generally constructedand their risks, as well as issues including seasonality and marketsegmentation. The next section looks at momentum strategies, goingover the principle behind such strategies and their relationship withthe economic cycle. The section discussed the construction ofmomentum strategies using price as a signal, and also using the

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12

Structural Alpha StrategiesFrancisco Blanch; Gustavo Soares and Paul D. Kaplan

Bank of America Merrill Lynch; Macquarie Funding Holding Inc.and Morningstar, Inc.


shape of the forward curve as a signal. It also assesses the distinctionbetween absolute and relative momentum.The third section covers volatility strategies, examining the ratio-

nale of such strategies and comparing different implementationsusing variance swaps and variance swap calendars and the risk–return profile expected of each implementation. The final mainsection discusses how to combine different alpha strategies into abasket in order to achieve particular risk–return goals for the overallportfolio. In the process, it also explores how the approach ofchoosing a set of relatively simple, uncorrelated strategies is a meansto mitigate backtesting bias relative to outof- sample performance.Finally, we conclude by highlighting the main points consideredthroughout the chapter.In general, there are three major investment themes on structural

commodity alpha: curve placement, momentum and volatility.Strategies that provide price insurance and liquidity to marketparticipants on a systematic basis are rewarded with positivereturns. These strategies are not riskless, but can be constructed to bemore or less independent of the factors that affect commodity price.As a result, their returns have low correlation with market returns.Even although these strategies are not independent of market funda-mentals, they cannot be replicated by simply getting exposure to a broad- based commodity market benchmark. Hence, they constitutepure commodity alpha.

Curve placement strategies are one of the most basic and popularways to generate commodity alpha. In essence, they exploit marketsegmentation (ie, the fact that different market participants havedifferent hedging needs) across different commodity forwardcurves, as consumers, producers and index trackers tend to usedifferent contracts for their hedging needs. Curve placement strate-gies aim to generate returns by taking advantage of the differences inhedging needs between producers, consumers and index trackers. Inaddition, curve placement strategies are rewarded for providingliquidity to market participants in less liquid parts of the forwardcurve.

Momentum strategies can be used by commodity investors as asource of alpha in many different ways. Momentum alpha is the


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reward for taking price risk from market participants ahead of themarket using the fact that commodity prices and inventories followpersistent fundamental economic trends. In particular, roll returnsare closely linked to inventory cycles. Given that changes in invento-ries are the differences between supply and demand, momentum incommodities is ultimately the result of persistence in inventorylevels and changes. As inventories build (or draw) slowly over time,momentum generates alpha by identifying the commodity marketsthat need to create incentives for market participants to balancemarkets by moving physical commodities in and out of storage, orincentivising changes in demand or supply.

Volatility strategies provide insurance to market participants and arerewarded for taking price risk from market participants that areunwilling to bear those risks. As in any other derivatives market,implied volatility in commodity markets serves as the key parameterfor market participants – eg, producers, consumers, processors andinvestors – to match supply and demand for options. However, thereis often a structural imbalance between buyers and sellers of optionsin commodity markets. Market participants’ hedging needs causebiases in the options markets, offering a source of market- neutralalpha for investors.

CURVE PLACEMENT STRATEGIESTo understand how alpha can be generated by curve placementstrategies, we need to understand how commodity forward curvesare shaped and how different market participants segment them-selves across the curves. Moreover, we need to be aware of when andhow these market participants position themselves throughout theyear and the trading flows associated with these positions.

Forward curves and the market value of storageCommodity forward curves embed not just expectations aboutfuture prices, but also the net costs of carrying physical commoditiesover time. Hence, storage and financing costs largely explain theshape of the curve in most commodity markets. A simple arbitrageargument shows how the cost of physical storage should be equal tothe difference between forward and spot prices (see Figure 12.1). Ifthe net cost of physically storing a commodity is higher (lower) than

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the difference between spot and forward prices, owners of the phys-ical commodity would rather sell (buy) the commodity on the spotmarket and buy (sell) it forward than store it. This dynamic wouldforce spot prices down and forward prices up when the forwardcurve is not steep enough to compensate for storage costs. Similarly,it would force spot prices up and forward prices down if the forwardcurve is too steep.However, storage costs interact with market expectations and the

need of physical players to own the physical commodity. Dependingon market conditions, one of the factors may be more relevant thanthe others. For commodities that are hard and expensive to store –such as natural gas, crude oil and lean hogs (a commonly used typeof pork that is traded in Chicago) – the cost of storage tends to playan important role in determining the slope of the forward curve, butthe shape of the forward curve can, at times, deviate widely from thestorage cost implied contango.But what determines the curvature of the forward curve? That is,

what determines the difference between the 1M–2M timespread andthe 3M–4M timespread? Using the same physical arbitrage argu-ment, it should be the cost of financing and storing a commodity fora month starting in one month versus the cost of financing andstoring a commodity for a month starting three months from now.


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Figure 12.1 Storage economics determines the shape of the forward curve: cost of financing plus storage costs

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Risk premium: a producer would accept a lowerprice than expected in exchange for locking-in hismargins: F0,T – S0 = E0(ST) – S0 – Risk Premium


Hence, if having access to a storage facility now is more valued bythe market than having access to a storage facility only available inthree months, then our cost of storage argument implies a certainconcavity in the shape of the forward curve. Concavity in the shapeof forward curves suggests that the 4M contract should experienceless price decay as time passes than the 2M contract. In other words,the price of the 4M contract falls less as it becomes the 3M contractthan the price of the 2M falls as it becomes the 1M contract. Curveplacement strategies take advantage of concavity in the forwardcurves by rolling long positions in commodity futures further out inthe forward curve versus rolling short positions in commodityfutures closer to maturity. Historically, this strategy has generatedconsistent outperformance and a longer date exposure produces abetter risk- adjusted return. These strategies can be implementedwith any set of weights. However, using DJ–UBS weights has beenthe most popular way of implementing the curve placement strate-gies (see Figure 12.2).

Market segmentation across commodity forward curvesCurve placement strategies exploit differences in the market value ofstorage across contract maturities, and can also take advantage ofmarket segmentation across the forward curve. Producers andconsumers are willing to pay a premium to protect their profit


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Figure 12.2 Risk-return of DJ-UBS based curve placement strategies (bubble sizes and labels are information ratios)

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margins against large price movements, as do index hedgers.However, these market participants tend to favour transactionsacross differing tenor segments of the forward curve.Producers tend to hedge their long positions on the back- end of

the curve, often accepting a lower price than they would expect inorder to secure the profitability of their investments. In contrast,consumers tend to hedge their short positions on the frontend of thecurve, often accepting to pay a premium for the physical ownershipof the commodity. Finally, index hedgers provide systematic buyingand selling pressure, selling the frontend contract and buying thesecond or third most nearby contract, on a recurring basis.Curve placement strategies benefit from market segmentation

because they roll long positions in contracts further out in theforward curve (facing the producers as they search to offload theirnatural long price risk) and roll short positions in contracts close tomaturity (facing the buying pressure created by consumers and theindex hedgers in the frontend of the forward curve).

Seasonality in curve placement strategiesSeasonality patterns can be found in a variety of commodity markets– such as livestock, refined products, natural gas, grains, sugarand coffee. Seasonality can then be used to enhance the risk–return profile of curve placement strategies by leveraging theseasonal patterns of contract liquidity, storage needs and marketsegmentation.Because of the seasonality in production, certain commodities

tend to come into the market at a very specific time of the year. Forexample, corn tends to be harvested in the northern hemisphere –where the bulk of the world production is located – between themonths of August and November. Inventories reach their lowestpoint in Q3, just as the corn harvest starts in the northern hemi-sphere. Hence, there is seasonality in the market value of storage.There is also seasonality in the hedging needs of consumers and

producers. For example, corn producers have to decide how much toinvest in terms of seeds, fertilisers and other production inputsduring the planting and growing seasons. In order to fix theirmargins, they increase their hedging demand during the plantingseason and decrease it during the harvest season. In particular,producers start closing down their short corn futures positions on


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the September contract (when the harvest starts coming in) duringthe months of June, July and August, exerting some buying pressureon the September contract during those months.

Risks of curve placement strategiesOne of the characteristics of curve placement alpha is that it facesheadwinds when beta commodity investments perform well. It is astylised feature of commodity forward curves that spot prices tend totilt the forward curve into backwardation as they move higher. Asdemand starts outpacing supply, the spot price tends to rise andinventories tend to draw. Lower levels of inventories shoulddecrease storage costs, and push down forward prices relative tospot prices. In fact, a backwardated curve is the market’s way ofoffering storage holders an incentive to supply the commodity intothe spot market. Therefore, when the market is tightening, spotprices move up and the forward curve flattens or moves into back-wardation.This link between spot prices and the shape of the forward curve

is behind one of the most important characteristics of curve place-ment alpha: its negative correlation with the market. By being longcontracts with longer maturity and short the contracts on the frontend of the curve, curve placement alpha strategies tend to get hurtwhen commodity prices rally. A common solution to this problem isto use the monthly rebalancing of the curve alpha strategy toneutralise its exposure to the market by having less notional expo-sure on the short leg than on the long leg. The result is a beta- neutralised curve placement strategy. While beta neutralisationis not a performance enhancement feature, it eliminates the negativecorrelation between curve placement alpha and commodity beta. Asa result, investors are left with a purer alpha strategy that is not nega-tively impacted by commodity price rallies. Investors who arelooking for a more “market- neutral” strategy should neutralise theirbeta exposure in their curve placement alpha strategies.Another often proposed solution to this problem is the dynamic

allocation across maturities. If curve placement alpha suffers whenforward curves move into backwardation, then it may be better todynamically adjust the strategy according to the shape of theforward curve. For instance, the investor may choose the position inthe forward curve that has the best- implied roll yield (ie, the


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expected price appreciation/depreciation of the contracts as itapproaches expiry) to the contract with lower maturity. In principle,this would give us a forward- looking estimate of how much impliedroll costs will be the following month.However, this seems to be an overly simplistic approach. These

strategies do not seem to work in practice. The fact that these strate-gies fail to differentiate themselves from static exposure is illustratedin Figure 12.3. The Dow Jones–UBS Roll Select Commodity Index,which rolls into the futures showing the most backwardation or theleast contango, seems to underperform simple static allocations suchas the Dow Jones–UBS 5M Forward Commodity Index.Perhaps, this is not surprising. The current “yield” or current

“implied roll cost” of a contract can only be a good predictor of itsfuture performance if the shape of the forward curve remains thesame. However, the slope and curvature of commodity forwardcurves are not random walks and their best predictors are not theircurrent values. It is fairly easy to statistically reject the random walkhypothesis for the slope and the curvature of the forward curveacross most commodities. In fact, the slope and the curvature ofcommodity forward curves tend to show a large degree of mean


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Figure 12.3 Dynamic curve placement strategies have failed to differentiate themselves from static exposure

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reversion. Hence, “yield” or “implied roll cost” strategies areunlikely to work in practice because they do not even work in theory.

MOMENTUM STRATEGIESMomentum strategies can be used by commodity investors as asource of alpha in a range of different ways. For instance, manymanaged futures funds (also called commodity trading advisors,CTAs) employ computer- based algorithms that aim to identifyupward and downward price trends across a variety of markets.Most of these algorithms work under high frequency and try to takeadvantage of statistical patterns and inefficiencies not only incommodities, but across a many futures markets. Alternatively,momentum strategies can be profitably implemented in low- frequency models such as those based on monthly returns.High- frequency systematicmomentum trading (as defined above)

can be a profitable strategy in certain market circumstances, and islikely tobeadiversifying strategyonabroadportfolioof alpha trades.However, they are hard for investors to access outside of a fundformat. Low- frequency momentum, on the other hand, can be easilyimplemented. Most importantly, high- frequency momentum and low- frequency momentum are not competing strategies, but rathercomplementary on a broad basket of commodity alpha strategies.Where does low- frequency momentum come from? For

commodities such as crude oil, refined products and base metals,momentum comes from their cyclical nature – that is, from the factthat demand follows the upward and downward trends of the busi-ness cycle (see Figure 12.4). More broadly, persistence is a directconsequence of determined demand growth combined with theinability of production to respond immediately to demand shocks, inaddition to low short- term elasticity of supply. Hence, momentumresults from persistence in the supply and demand fundamentals ofthe commodity.Given that changes in inventories are the differences between

supply and demand, momentum in commodities results from asustained trend in the levels of inventories. As such, we should findan even stronger link to the shape of the forward curve. In fact, statis-tical analysis shows momentum in the shape of the curve seems to bemore prevalent and easier to detect than momentum on returns (seeFigure 12.5).


315


Whether using excess returns, momentum or momentum basedon the shape of the curve, momentum strategies exploit persistencein the levels of inventories. Months of low inventory levels – whichare associated with backwardation, low roll costs and rising prices –


316

Figure 12.4 t-statistics of previous month performance for DJ-UBS ER sub-indices

-4 -3 -2 -1 0 1 2 3 4

CoffeeGold

Lean hogsWheatSilver

CottonSoybean

Soybean oilNatural gas

CornZinc

Live cattleDJ-UBS ER

SugarGasoline

NickelHeating oil

Crude oil (WTI)Copper

Aluminium

Jul-05 to Jul-10 Jul-00 to Jul-10

Statistically insignificant at the

10% level

Figure 12.5 Degree of persistence on the shape of the forward curve (From Jan-06 to Dec-10)

2.6 4.4 4.9 5.4 5.5 5.6 6.0 6.0 6.3 6.7

8.4 8.9 9.0 9.0

10.5 13.7

15.3 15.7

24.2 39.2

0 10 20 30 40 50

Wheat Live cattle

Cocoa Soybean oil

Soybean Lean hogs

Natural gas Gasoline

Crude oil (WTI) Corn

Cotton Heating oil

Sugar Aluminium

Copper Zinc

Nickel Coffee Silver Gold

All commodities have

statistically significant

persistance in the shape

of the forward curve


tend to be followed by months of low inventories. Hence, positiveprice performance and low roll costs are likely to be persistent.Similarly, months of high inventory levels – which are associatedwith contango, high roll costs and falling prices – tend to be followedby months of high inventories. Momentum generates alpha by iden-tifying the commodity markets that need to create persistentincentives for the market to balance supplies with demands. Thestrategy is then rewarded for taking price risk in anticipation offuture price movements. In that regard, momentum strategies arenot pure alpha strategies designed to be market- neutral at all times,quite the contrary; in times of price appreciation, momentum strate-gies would be expected to participate – and have notable positivecorrelation – with the market. In times of price declines, short posi-tioning would result in negative correlation with the market.Still, momentum strategies do not have an inherent bias to one

side or another, and in that sense are market- neutral alpha strategies.The drawback of momentum strategies is that they are only likely togenerate returns when markets are trending, and are less likely togenerate much alpha in range- bound markets.While price and curve momentum strategies roughly exploit the

same source of alpha, there is an important difference when it comesto implementation. Momentum can be used as a relative value signalthat generates cross- commodity alpha by selecting which commodi-ties to go long. For instance, some strategies select the most“backwardated” commodities or the least “contangoed” to constructa long- only portfolio. These are relative momentum strategies thatallow investors to pick the best–performing commodities, imposingthe constraint to be 100% long at all times.However, one can also use momentum as an absolute value indi-

cator – ie, as a signal to be used when deciding whether to go long orshort a particular commodity. A portfolio using an absolutemomentum strategy does not need to be 100% long at all times.Unlike relative momentum strategies, a portfolio using this type ofimplementation could be long for some commodities without anyoffsetting short positions. Similarly, the portfolio could have onlyshort positions or stay neutral depending on the combination ofsignals it receives.


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VOLATILITY STRATEGIESCommodity volatility can also provide a powerful source of alphafor investors. Just as in any other derivatives market, impliedvolatility in commodity markets serves as the key parameter formarket participants – such as producers, consumers, processors andinvestors – to match the supply and demand for options. However,there is often a structural imbalance between buyers and sellers ofoptions in most commodity markets.Market participants’ hedging needs cause persistent biases in the

commodity options markets, offering a source of market- neutralalpha for investors. Commercial market participants are naturalbuyers of insurance against large price swings – ie, buyers ofvolatility. Producers and consumers are willing to pay a premium toprotect their profit margins against large price movements. At thesame time, there are few natural sellers of volatility in thecommodity options markets apart from speculators.Because of the relatively low participation of speculators in

commodity options markets, this imbalance between buyers andsellers of volatility has helped to create structural alpha opportuni-ties for investors. For market participants willing to take on pricerisk, there is an opportunity to profit from this demand for insur-ance. Generally, there is high demand for long option positionsamong commercial market participants, such as producers,consumers and distributers.Option sellers collect the premium of an option and often delta

hedge their exposure to the underlying contract. However, at incep-tion, option sellers do not know whether the final profits of deltahedging the position will be positive. The difference between theoption price change and the profit or loss of the underlying deltahedge is the hedging error that affects the profit and loss (P&L) ofselling options and delta hedging. Hence, option market makersneed to be compensated for the risk of losing money on their deltahedges.This hedging risk is a function of the “gamma” of the option, as

well as the future realised price volatility of the underlying future.As a result, option prices (and consequently implied volatility) needto be high enough to compensate market makers for the risk ofvolatility realising at high levels over the lifetime of the option. Whilethe high demand for options from commercial players pushes up


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implied volatility away from realised volatility, current realisedvolatility is an estimate of the risk of delta hedging. Hence, theimplied versus realised volatility spread can be seen a measure of theimplicit supply and demand for volatility in the options market. Asthe P&L of delta hedging short options positions is linked to thespread between implied and subsequently realised volatility, optionmarket markers then embed a premium on implied volatility overrealised volatility.Investors can benefit from the imbalances between buyers and

sellers of commodity volatility through variance swaps. The payoutof a variance swap depends directly on the difference betweenimplied volatility and the subsequently realised volatility of theunderlying asset. Hence, commodity variance swaps can be utilisedto take advantage of the structural premium between implied andrealised volatility in commodities. Systematic variance selling offersa sources of structural alpha for investors. A variance swap can besold short at the close of the day that the previous swap expires sothat short variance positions can be rolled over continually (seeFigure 12.6).Selling variance is vulnerable to large price movements common

in many commodity markets. One way to mitigate this risk is tohedge the short variance position with a long variance position with


319

Figure 12.6 MLCX WTI vol arbitrage excess return index MLCXCVA1 index

95

115

135

155

175

195

215

-5%

-4%

-3%

-2%

-1%

0%

1%

2%

3%

4%

5%

Dec-02 Jun-04 Dec-05 Jun-07 Dec-08 Jun-10 Dec-11 Jun-13

Monthly returns Index levels (rhs)


longer maturity, further out in the volatility term structure. Suchcalendar spread variance swaps can be used to create consistentstructural commodity alpha strategies in many commodity markets.Combining strategies with different tenors and then adjustingnotionals to make the exposure less sensitive to changes in impliedvols – ie, vega neutral – is a classic way of mitigating the tail risk inshort volatility strategies.Options have convex payouts and their price movements are not

exactly equal to the movements in the underlying delta hedge. Themore convex the option, the harder it is to delta hedge, the higherrisks the option seller faces and the higher the compensation forbearing those risks should be. Hence, the alpha generated by system-atically selling variance should be proportional to the degree ofconvexity of the option payout function – ie, to the option’s“gamma”. As a result, systematically selling variance in short matu-rity tenors should outperform systematically selling variance in longmaturity tenors. This is so because the short- dated options havehigher gamma than long- dated options, everything else beingconstant. Similar “gamma” strategies can also be used to generatestructural commodity alpha in many commodity markets (see Figure12.7).


320

Figure 12.7 MLCXCVSB index with monthly returns (WTI crude oil 1M versus 3M variance swap calendar spreads)

95

105

115

125

135

145

155

165

175

-4%

-3%

-2%

-1%

0%

1%

2%

3%

4%

5%

Dec-02 Jun-04 Dec-05 Jun-07 Dec-08 Jun-10 Dec-11 Jun-13

Monthly returns Index levels (rhs)


PUTTING IT ALL TOGETHERThe objective of any alpha portfolio should be to capture the benefitsthat a diversified portfolio of different sources of alpha can provide.In general, a portfolio’s risk is a function of the number of strategiesheld in the portfolio as well as of the correlation between them.Hence, a portfolio of a few alpha strategies, more or less independentfrom each other, generally will produce better risk- adjusted returnsthan any single strategy by itself.A simple example illustrates the power of a diversification in an

alpha portfolio. Suppose we have an alpha strategy (strategy A) thatgenerates returns of 5% and volatility of 2% per annum, providing aninformation ratio of 2.5. At the same time, suppose we have a set ofthree uncorrelated strategies (strategies B, C and D), each generatingannual returns of 3% with the same 2% volatility, each of theseproviding an information ratio of 1.5. Despite each of those strategiesbeing far worse than strategy A on an individual basis, it turns outthat an equally weighted portfolio of strategies B, C and D producesa better allocation in risk- adjusted terms than an allocation tostrategy A.

Backtesting biasIn any historical backtest, it is hard to identify whether the goodhistorical performance of a strategy is a product of its design or pureluck. This is a classic problem with backtests and other types of model- selection algorithm. By searching for the alpha holy grail, wemay end up spuriously choosing a methodology that would haveperformed well in that period by sheer luck. Complex strategies mayperform well on a backtested basis not because of any fundamentalreason, but only because their many bells and whistles were chosenso the strategy would perform well on the backtest in the first place.In fact, a set of sufficiently complex rules can overfit history and giveany strategy a great backtest.One way to mitigate the risk of backtesting bias is to always

choose simple implementations of each investment theme. Simplestrategies have fewer degrees of freedom and therefore are likely tosuffer less from the overfitting problem that complex strategiesintrinsically embed. Overfitting rules and parameters are what ulti-mately generate the backtesting bias. Investors are better offcombining simpler strategies – potentially with worse backtested


321


performance, but for which they can understand the sources ofreturns – than more complex versions of the same strategy.The three major investment themes in the structural commodity

alpha space outlined above – curve placement, momentum andvolatility – can be accessed through extremely simple rules- basedstrategies. Of course, one could think of many ways to try to improvethe performance of these simple strategies. However, in practice,many of the more complex strategies only marginally improve the risk- adjusted returns of the simpler strategies. At the same time,simpler strategies are less vulnerable to the backtesting bias problempurely because they have less rules and parameters to be calibrated.

Weaving an alpha basketThe simple example above suggests an easy methodology forcreating high- quality alpha in any asset class. Using simple strate-gies, investors should create an alpha portfolio that capturesdifferent sources of risk premium. Generally, the diversified alphaportfolio will produce better risk- adjusted returns than any singlestrategy if the strategies are combined in an optimal way.However, what are the weights that should be given to each alpha

strategy when constructing a portfolio of alpha strategies? Theconcept of “basket weights” is somewhat meaningless forcommodity alpha investments. What really matters is not “notionalweight” but “volatility weight” – ie, the contribution of each strategyto the overall risk of the portfolio.To make alpha strategies comparable, investors can dynamically

adjust the allocation to the strategy in order to target a desired levelof volatility. This technique, called volatility targeting, is a means ofcreating a level playing field for different alpha strategies. Becausecommodity alpha strategies are typically unfunded, changing thedegree of participation is only limited by the amount of collateralneeded to fund the strategy.On the level playing field of volatility- targeted strategies, how do

we then best combine different sources of commodity alpha into asingle alpha portfolio? After defining expected returns and ameasure of risk (eg, volatility) for every alpha strategy, we canconstruct an efficient frontier by finding the portfolio that maximisesexpected returns for a given amount of risk. Because commodityalpha strategies are unfunded, the weights of a commodity alpha


322


portfolio do not need to add to 100% as they do in the classicMarkowitz’s efficient frontier problem.1 In the absence of any weightconstraint, the efficient frontier for commodity alpha portfolios is astraight line in which all portfolios have exactly the same informa-tion ratio as that of the maximum- information ratio portfolio (MIP)of a frontier obtained with portfolio weights constrained to add to100% (see Figure 12.8).However, defining expected returns and a measure of risk for

each commodity alpha strategy involves the real problem of trying tooptimise different sources of commodity alpha into a portfolio. Thatis, it is a statistical rather than a financial issue. One way to avoid thestatistical difficulties relating to asset allocation decisions is to simplyput statistics aside and follow an appropriate rule of thumb. On thelevel playing field of volatility- targeted strategies, an equallyweighted (EW) basket is a straightforward way to combinecommodity alpha strategies. One advantage of the EW basket is itsrobustness over time, as the information ratios of the EW basket tendto be more stable than the ones for individual strategies. However,the EW basket is more than just a naïve diversification technique.In our selected set of commodity alpha strategies, a case could be

made for all pairwise correlations being zero on average. After all,


323

Figure 12.8 The application of CAPM to commodity alpha suggests that investors should only care about the “Maximum Information Ratio Portfolio”

Efficient frontier: max returns given a targeted level of risk

3.0%

3.5%

4.0%

4.5%

5.0%

5.5%

6.0%

0.75% 0.95% 1.15% 1.35%

Weights constrained to 100%No constraints (weights adding to more than 100%)No constraints (weights adding to less than 100%)

MIP: Maximum Information Ratio Portfolio with weights adding to 100%

Portfolios with unconstrained weights have the same

information ratio as the MIP

Targetedvolatility

Returns

Source: BofA Merrill Lynch Global Commodity Research


the strategies were designed to be more or less independent sourcesof alpha. If that is the case, then on the level playing field of volatility- targeted strategies, we know that the EW basket is one with weightsadding up to 100% with the lowest level of volatility. Formally, if allpairwise correlations are equal to zero and the volatility of eachstrategy is equal to s, then the EW basket is the portfolio withweights adding to 100% with the lowest level of volatility. Underthose assumptions, this is the minimum- variance portfolio (MVP).Of course, the weights of a commodity alpha portfolio in the effi-

cient frontier do not need to add to 100%. However, once the MVP isfound, investors can leverage weights up and down proportionallyto achieve any level of targeted volatility on the overall portfolio. Inpractice, this technique can get investors quite close to the MIPwithout having to ever care about estimating expected returns ofcommodity alpha strategies (see Figure 12.9).

CONCLUSIONSystematic commodity alpha strategies attempt to capture differentrisk premiums, such as insurance and liquidity, prevalent incommodity markets. These systematic strategies capture riskpremiums that are structural to commodity markets and therefore


324

Figure 12.9 In practice, the EW basket or the “Minimum-Variance Portfolio” can get investors quite close to the efficient frontier

Efficient frontier: max returns given a targeted level of risk

3.0%

3.5%

4.0%

4.5%

5.0%

5.5%

6.0%

0.8% 1.0% 1.2% 1.4%

Weights constrained to 100%No constraints (weights adding to more than 100%)No constraints (weights adding to less than 100%)

MVP: Minimum-Variance Portfolio with weights adding to 100%

Efficient frontierwith unconstrained weights

Targetedvolatility

Returns

Leveraging up and down the MVP

Source: BofA Merrill Lynch Global Commodity Research


likely to be a robust source of market- neutral returns in the long run.In addition, a portfolio of alpha strategies more or less independentof each other will yield higher risk- adjusted returns than any standalone, single commodity alpha strategy. This paves the way for asimple recipe for generating high- quality alpha in commodities.Investors should pursue simple and easy to understand strategiesthat exploit a broad range of sources of alpha. In particular, usingequally weighted baskets of volatility- targeted strategies is a simpleand robust way to construct a longterm allocation to structuralcommodity alpha strategies.

Reprinted by permission. Copyright 2013 Merrill Lynch, Pierce,Fenner & Smith Incorporated. Further reproduction or distribution isstrictly prohibited.

APPENDIX: PUTTING MOMENTUM INTO COMMODITIESPaul D. Kaplan

Strategies that take a momentum- based long/short approach tocommodity investing serve investors better than long- only strate-gies. Following weaker investor activity in 2011, investment flowsinto commodities grew in 2012, with a net inflow of US$5.3 billionacross all sectors alone in August 2012, with commodity exchange- traded products (ETPs) one of the fastest- growing asset classes.Commodity ETPs hit an all- time high of US$207.4 billion total assetsin September 2012. In comparison, commodity ETPs saw inflows ofUS$10 billion throughout the entire year of 2011, and the year endedwith total assets in commodities ETPs of US$152 billion.This surge in assets mirrors equally impressive gains in many

commodities’ spot prices. Unfortunately, and to the chagrin of manyinvestors, products linked to commodity indexes often experiencemuch lower returns. Negative roll yield (which occurs when distantdelivery prices exceed near delivery prices) means that manyinvestors lose out even as prices rise. In response, a growing numberof commodity investors are eschewing the traditional long- onlyapproach in favour of alternative strategies that are better able tomanage roll yield.With the rise of more innovative strategies, there is reason to ques-

tion how well investors are being served by the traditional long- onlycommodity indexes either as benchmarks or proxies for investment


325


products. Traditional approaches to representing pure beta expo-sures work well for stocks and bonds but not so well for thecommodities “asset class”. In fact, we argue that there is no suchthing as commodity beta. Moreover, we also assert that new passivestrategies that use a momentum- based long/short approach ratherthan the long- only approach of the most common commodityindexes are better benchmarks for active strategies.

NO SUCH THING AS COMMODITY BETAFor many asset classes, it is very easy to take a pure beta exposure.Multiple asset class proxies are available, many of which are reason-able substitutes for each other. The Russell 3000, S&P 500 and DowJones Wilshire 5000 indexes, for example, are representative of thebroad stock market and have similar performance characteristics,just as the Citigroup Broad Investment- Grade (BIG), Barclays CapitalUS Aggregate and Merrill Lynch US Domestic Master bond indexesmirror the wider fixed income market and perform alike. However,for commodities fewer choices and more disparity exist among theindex options.

NOT ALL INDEXES ARE ALIKEFigure 12A.1 illustrates the similar risk and return characteristics ofthe broad stock and bond indexes and the disparity among the threetraditional commodity indexes – the S&P GSCITM CommodityIndex, Dow Jones UBS Commodity Index, and Reuters/JefferiesCRB Index. When we plot standard deviation and compound annualreturn for each index over a common time period (January 1991–September 2012), we see that the nearly identical risk and returncharacteristics of both the stock and bond indexes place the plotpoints on top of one another. The commodity indexes, however, donot display the same level of consistency. Dramatic differences inconstituents and weighting schemes as well as rebalancing rules arelikely the cause of the performance differences in the commoditiesindexes. The S&P GSCI index, for example, has about double theweighting to the energy sector as the Dow Jones UBS Commodityand Reuters/Jefferies CRB indexes and only one third of theweighting to agriculture.


326


BOTH LONG AND SHORT POSITIONS FOR POSITIVE RISKPREMIUMSLong- only commodity futures strategies can prove inadequate inproviding investment exposure to commodities, which is whyprofessional CTAs tend to take both long and short positions incommodity futures, often based on trends in prices.

SOURCES OF EXCESS RETURNA futures strategy generates excess return (ie, return in excess of the risk- free rate) from two sources:


327

Figure 12A.1 Standard deviation versus compound annual returns for various indexes

Source: Morningstar

Standarddeviation %

Compoundannual return % Stock indexes

Morningstar long/short commodity

Morningstarlong-only commodity

Reuters/Jefferies CRB(inception: 02/01/1994)

Dow Jones UBS commodity

BarCap USagg. bond

Bond indexes Commodity indexes


1. changes in futures prices; and2. the roll yield – which can be either positive or negative – that

results from replacing an expiring contract with a further outcontract in order to avoid physical delivery yet maintain posi-tions in the futures markets.

A complete understanding of these two sources of return requires ananalysis of three interrelated markets for each commodity:

1. spot market – the cash market for the commodity itself;2. futures market – the market for contracts to deliver the

commodity in the future for a price set today; and3. storage market – the market for the service of storing the

commodity on behalf of its owner.

What happens in spot markets is important to futures investorsbecause changes in spot prices impact futures prices. The storagemarket is important because it interacts with the spot market andinfluences the slope of the futures price curve, which is the source ofroll yield.At times of high demand, spot prices will be strong and the

futures price will be lower than the spot price so that the further outthe futures contract, the lower the price. When this is the case, we saythat there is “backwardation” in the futures market or that thefutures curve is “backwardated.” Investors who are taking long posi-tions in futures contracts can realise this compensation monetarily byreplacing the contracts that they are holding with longer- term ones,thus locking in profits.This component of excess return realised by investors is referred

to as “roll yield”. As Figure 12A.2 shows, in backwardated marketsroll yields are positive. Likewise, when the marginal benefits ofowning spot supplies are low, the relationship between time to expi-ration and the futures price is positive, a condition known ascontango. In contango markets, roll yields are negative becausereplacing contracts with ones of later maturity results in locking in aloss.When a commodity is scarce spot prices are strong, leading to

backwardation and positive roll yields. Conversely, plentiful spotsupply leads to contango and negative roll yields. Since inventory


328


conditions in some commodities are slow to adjust due to the time ittakes to increase their production, backwardation or contango couldpersist for a period of time, causing investors to consistently experi-ence positive or negative roll yield over the period. Thus, a passiveinvestor should benefit from a trend- following strategy that incorpo-rates roll yield into its signal.


329

Figure 12A.2 Futures price curves

Positiveroll yield

Negativeroll yield

Cont

ract

pric

eCo

ntra

ct p

rice

0 (spot)months to delivery

0 (spot)months to delivery


ROLL YIELD AND EXCESS RETURNThe effect of roll yield on excess return can be substantial. In fact,several studies have shown that excess return is attributableprimarily to roll yield, not to changes in futures prices. Long- termexcess returns on commodities that exhibit mean reversion in priceand that tend to trade in contango will generally be negative, andthose that tend to trade in backwardation will generally be positive.This behaviour can be seen in Figure 12A.3, which shows the rela-

tionship between roll yield and excess returns on the commoditieslisted for the 21-year period April 1990–September 2011.Commodities that tended to trade in contango experienced negativeexcess return, while those that tended to trade in backwardation sawpositive excess return.Of particular interest here are natural gas futures. Because the

price of natural gas grew at 4.1% per year over the 21-year period,one might have expected a natural gas futures index to provide acomparable rate of return. However, because natural gas futurestraded in contango (and consequently experienced negative rollyield), the excess return was an abysmal negative 12.5%.

BUILDING A BETTER STRATEGYPassive strategies that use a momentum- based long/short approachrather than the long- only approach of the most common commodity


330

Figure 12A.3 Roll yield and excess return

Positive excessreturn

positive roll yieldnegative roll yield

Negative excessreturn

Nat

ural

gas

Soyb

ean

oil

Whe

at, h

ard

win

ter

Lean

hog

s

Heat

ing

oil

Live

cat

tleGa

solin

e-oi

l-pet

role

um

WTI

cru

de

Bren

t cru

de

Soyb

ean

mea

l


indexes can better serve investors by attempting to capture the fullexcess return from a futures strategy. Such passive strategies are alsolikely to prove a better benchmark for the active strategies of profes-sional futures investors.To make this idea operational, we created a family of commodity

indexes that includes combinations of long commodity futures, shortcommodity futures and cash (see Figure 12A.4). The primary index,called the Morningstar Long/Short Commodity Index, holdscommodity futures both long and short based on momentumsignals. The other indexes are derived from this long/short index.The family includes a long/flat version, which holds cash in place ofthe short positions in the primary version so that investors who donot want or cannot have short positions can still get some benefits ofa momentum- based long/short strategy. The family also includes ashort/flat version for investors who already have long- only expo-sure to commodities and want some benefits of the momentumstrategy without having to replicate or drop their long- onlyexposure.We created a set of single commodity indexes to serve as

constituents for the long/short index and the related compositeindexes by calculating a “linked” price series that incorporatesboth price changes and roll yield. The weight of each individualcommodity index in each of the composite indexes is the productof two factors: magnitude and the direction of the momentumsignal. We initially set the magnitude based on a 12-monthaverage of the dollar- weighted open interest of the commodity.We then capped the top magnitude at 10% and redistributed anyoverage to the magnitudes for the remaining commodities. Thedirection depends partly on the type of composite index and, aswe explain below, partly on the type of commodity in the long/short index.In the long/short index each month, if the linked price exceeds its

12-month daily moving average, the index takes a long position inthe subsequent month. Conversely, if the linked price is below its 12-month moving average, the index takes the short side. An exceptionis made for commodities in the energy sector. If the signal for acommodity in the energy sector is short, the weight of thatcommodity is moved into cash – that is, we take a flat position.Energy is unique in that its price is extremely sensitive to geopolitical


331



332

Morningstar commodity indexes construction. The Morningstar commodity index family consists of five indexes that employ different strategic combinations of long futures, short futures, and cash. The long/short commodity index is a fully collateralised commodity futures index that uses the momentum rule to determine if each commodity is held long, short, or flat

Figure 12A.4 Morningstar commodity indexes construction

Commodity universe

Morningstar commodity universe

Individual commodity indexes

Long-only

Short-only

Long/flat

Long/short

Long/flat


events and not necessarily driven purely by supply/demandimbalances.For the remaining indexes, the direction is set as follows:

long- only – always long for every commodity;�

long/flat – same long positions as the long/short index, but�

replaces the short positions with flat positions;short/flat – same short positions as the long/short index, but�

replaces the long positions with flat positions; andshort- only – always short for every commodity.�

HOW THEY STACK UPFigure 12A.5 shows the general performance statistics of theMorningstar Long/Flat, Long- only, and Long/Short Indexes fromFebruary 1991 to September 2012, compared with other indexes (forthe sake of simplicity and clarity, we focus our discussion of resultson these three Morningstar indexes).Generally, the Morningstar commodity indexes’ return and risk

characteristics rank favourably relative to other benchmarks. Note,for example, the Morningstar Long/Short Index’s better return andmoderate risk compared with the S&P GSCI and Dow Jones–UBSCommodity indexes. The diversification characteristics of theMorningstar commodity indexes can be seen in Figure 12A.6, whichshows a correlation matrix from February 1991 to September 2012.


333

Figure 12A.5 Morningstar commodity indexes: risk–return profile

r d dr

s


DOWNSIDE PROTECTIONWhile all long- only commodity indexes tend to provide strongprotection when the stock market is down and in inflationary envi-ronments, the Morningstar Long/Short Commodity index limitsdownside risk while negotiating ups and downs in the commoditymarkets themselves. The Long/Short index’s maximum drawdownin the February 1991–September 2012 period, as seen in Figure 12A.5,was substantially lower than that of the S&P GSCI and Dow Jones–UBS Commodity indexes. We also compared maximum drawdownsexperienced by the listed indexes during five- year sub- periodswithin that overall period, and the Morningstar Long/ShortCommodity index suffered much smaller drawdowns in all sub- periods. Clearly, a long/short strategy is better equipped to tap into


334

Figure 12A.6 Commodity index correlation index


the underlying momentum of commodity prices, thereby limitinglosses in down markets.

THE LONG AND SHORT OF ITThe long- only strategies that dominate the commodity index marketdo not best serve investors as investment vehicles or as benchmarks.Since futures price changes and roll yields are the sources of excessreturn, long- only indexes have no way to capture the returns avail-able from shorting futures when there is downward price pressureor a positively sloped futures price curve. Long- only indexesgenerate negative roll yields when markets are in contango (whendistant delivery prices exceed near delivery prices), and thus canhave negative returns when commodity prices are rising.Furthermore, since many actively managed CTAs invest in long andshort futures based on momentum trading rules, the long- onlyindexes are not appropriate benchmarks, rendering traditionalapproaches to representing beta exposure unsuitable.By using a momentum- based approach that takes into account

both price change and the slope of the futures price curve, theseMorningstar indexes aim to maximise both sources of excess return –price change and roll yield – to produce better performance. In addi-tion, these indexes are logically consistent with the underlyingeconomics of commodities futures markets, and backtested resultsshow an attractive risk profile, low downside risk and low correla-tions to both traditional asset classes and long- only commodityindexes. As passive investment alternatives, these rules- basedindexes could offer easier access to actively managed commoditiestrading strategies.

2013 Morningstar. All Rights Reserved. Used with permission.Further reproduction or distribution is strictly prohibited.

The views and the opinions expressed here are those of the authorsand do not represent the opinions of their employers. The authors arenot responsible for any use that may be made of the contents of thischapter. No part of this text is intended to influence investment deci-sions or promote any product or service.

1 The Markowitz efficient frontier problem is: how do you select a portfolio with the lowestpossible risk given a targeted expected return for the portfolio? The solution was developedby several authors in the 1950s and 1960s, but Harry Markowitz’s 1952 paper “PortfolioSelection” (Journal of Finance, 7(1), March) was among the first to address the problem.


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In this chapter, a geometric average Spot Energy Index (SEI) will beconstructed before its performance is reproduced with stock portfo-lios. The investment methodology employs two self- adaptivestochastic optimisation methods, superior to other rival approacheswhen applied to this indextracking problem. To test the perfor-mance of the tracking baskets, three different rebalancing scenariosare examined, that also take transaction costs into consideration. Itwill be shown that energy can be effectively tracked with stock port-folios selected by the investment methodology used here.

Passive investment strategies are becoming increasingly popular.Sharpe (1991) argues that, on average, active managers cannot beatpassive strategies and active trading strategies are a zero- sum game.Other studies have found that passive strategies outperform activestrategies on average (Malkiel, 1995; Sorenson et al, 1998; Frino andGallagher, 2001). In addition, Barber and Odean (2000) claim that inactive trading strategies the presence of high transaction costs, andsometimes the overconfidence of investors in their predictions,reduces profits substantially and potentially leads to losses.

One of the most popular forms of passive trading strategies, indextracking, attempts to replicate/reproduce the performance of anindex. Portfolio managers can choose between two methods. Fullreplication, purchasing all the stocks in an index, has some practicallimitations and disadvantages. According to Beasley et al (2003),replicating exactly an index entails frequent revisions1 to reflect theupdated weightings in the index, leading to high transaction costs.

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13

Energy Index TrackingKostas AndriosopoulosESCP Europe Business School


One- to- one replication also suffers from the disadvantage that somestocks can be very illiquid. For these reasons, many passive strategymanagers prefer the alternative of partial replication, wheremanagers hold the subset of stocks chosen to replicate the index mosteffectively.

Since the early 2000s, impressive gains have been witnessed incommodity prices. This has attracted investors’ attention and led togrowth of index investing in the commodity markets. In general,there are three major ways of investing in a commodity index: first,by choosing an index and replicating it by following the related rulebook; second, by investing in a fund that replicates the chosen index;finally, a popular approach is buying the shares of an exchange- traded fund (ETF) that mimics the commodity index. This trendtoward commodity index investing prompted the first commodityETF in November 2004.2 As of January 2010, the market capitalisa-tion of ETF exceeded US$39 billion. Many other ETFs investing inphysical commodities, futures and commodity- related equities havefollowed.

This chapter will propose a new approach that reproduces theperformance of a geometric average SEI by investing only in a subsetof stocks from various equity pools. For the purposes of our analysis,the Dow Jones Composite Average, the FTSE 100 and BovespaComposite indexes, and two pools that include only energy- sectorstocks from the US and the UK, respectively, are used. Daily data areanalysed and the indextracking problem addressed by two evolu-tionary algorithms – the differential evolution (DE) algorithm andthe genetic algorithm (GA). The performance of the resulting invest-ment strategy is tested under three different scenarios: buy- and- hold, quarterly and monthly rebalancing, accounting fortransaction costs.

This chapter has the following structure. The next section willdiscuss the importance of commodity index investing and majorinnovations, before we present the innovative approach used in thischapter for replicating the price behaviour of an energy commodityindex using equities. We then explain in more detail the optimisationmodel, describing the two evolutionary solution techniquesemployed. The following section will outline the methodology usedfor developing the constructed energy commodity index, along withthe commodity and equity data used, and discussion of the results


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from tracking the energy index with the proposed investmentapproach will then follow.

COMMODITIES INDEX INVESTINGCommodity indexes have been around for many years, used mostlyfor benchmarking and to track spot commodity prices. One of thefirst published commodity indexes was the Economist’s Commodity- Price index, which started in 1864. Then, in 1957, theCommodity Research Bureau (CRB) index was established, trackingspot commodity processes; after undergoing major revisions in itscomposition, it is still published today. Nevertheless, since the early1990s, the development of commodity indexes has witnessedtremendous changes. The first generation of investable commodityindexes appeared only in 1991, when the S&P GSCI (originally theGoldman Sachs Commodity Index) was introduced. In 1998, theDow Jones–UBS Commodity Index (originally the Dow Jones–AIGCommodity Index) and the Rogers International Commodities Index(RICI) were both launched. Both the S&P GSCI and the RICI indexesare heavily weighted towards the energy sector, while the DowJones–UBS, because of the rule that no sector can weigh more than one- third of the index, has energy at its limit; in many instances, thislimit is exceeded between the annual rebalancing periods.

The common characteristic, and a major disadvantage of theseearly indexes, is that they invest in commodity futures contracts thatare close to expiration, thus they roll forward their futures positionsmore frequently – making it very expensive to follow an index- replication strategy using ETFs. In addition, holding a long futuresposition via an index that invests in the front of the curve is subop-timal, especially latterly, because many commodity futures curveshave been experiencing steep contango (a state when the futuresprice curve is upward sloping) at the front end of the curve, whichdiminishes the ultimate returns.

This previous observation was the main driver behind the creationof the so- called “second- generation” commodity indexes such as theUBS Bloomberg Constant Maturity Commodity Index and the JPMorgan Commodity Curve Index. Both of these indexes have aconstant weighting scheme across commodities, but their invest-ments allocation is spread across several contract expirations withinindividual commodities. The DJ–UBSCI 3-Month Forward index

ENERGY INDEX TRACKING

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takes a similar approach and invests in contracts farther out thefutures curve, reducing the effect of backwardation (a state when thefutures price curve is downward sloping) or contango as the curvetends to be flatter for longer maturities. These type of indexes outper-form the first- generation indexes because, when the front end of thecurve is in steep contango, as has been the case with crude oil, thelosses tend to be mitigated or reversed across the longer maturitycontracts. Nonetheless, the opposite happens when futures marketsare in backwardation, since the concentration usually occurs at thefront end of the curve. It can be argued, however, that the chronologyof the indexes has a significant impact on their construction method-ology, and hence their performance, as later ones have had thebenefit of improving on the methodology used by previously devel-oped indexes.

The latest addition to the family of commodities indexes are third- generation indexes that attempt to improve the returns of theprevious two by incorporating commodities selection, over-weighting or including only commodities that are expected todeliver higher returns in the near future, while underweighting oromitting completely commodities that are expected to performpoorly. The UBS Bloomberg CMCI Active Index introduced in 2007and the SummerHaven Dynamic Commodity Index introduced in2009 are two examples of the third- generation commodity indexes.The latter index includes 14 equally weighted commodities from atotal of 27, rebalancing its futures portfolio every month using basisand momentum to identify the greatest possible risk premium. Theformer index, on the other hand, uses the discretionary approach ofits research analysts who adjust the component weightings of theindex according. However, these types of indexes carry with them anew risk since the method of the research analysts used to select thecommodities and their respective weightings can be unsuccessful,and thus underperform passive indexes.

Commodity indexes attempt to replicate the returns equivalent toholding long positions in various commodities markets withouthaving to actively manage the positions. Being uncorrelated with thereturns of traditional assets such as stocks and bonds, commodityindex investments’ returns provide a significant opportunity toreduce the risk of traditional investment portfolios. This explains theeconomic rationale for including a commodity index investment in


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institutional portfolios such as those of pension funds and universityendowments. There are now numerous publicly available futures’indexes, with different risk and return profiles, offering exposure tocommodity markets; each of these indexes also offers specific expo-sure to certain commodity sectors via their traded sub- indexes.

Commodity index investing is still relatively “young” – comparedto other more established asset classes such as stocks and bonds –and we would expect continued interest and innovation by marketplayers in the coming years.

AN INNOVATIVE APPROACHThe above addresses a question that has received almost no attentionin the literature: can returns of equity portfolios be used to replicatethe performance of physical energy price returns, proxied by a spotindex? The aim of this chapter is to replicate the price behaviour ofdirect energy commodity investment using equities. The proposedapproach is based on previous research findings that the returns ofequally weighted long- only portfolios of commodity futures aresimilar to those of stocks (Bodie and Rosansky, 1980; Fama andFrench, 1987; Gorton and Rouwenhorst, 2006). In addition, after the2000s, commodities went through a financialisation process,exposing them to the wider financial shocks (Tang and Xiong, 2010).The replication method uses two very efficient strategies, the DEalgorithm and the GA, to solve the indextracking problem for theconstructed SEI. These low trackingerror strategies provide severaladvantages to investors: they result in better- diversified portfolios,make the long- only constraint of a fund manager less binding and, ingeneral, tend to provide higher returns for equity strategies.

The performance of the SEI is reproduced by investing in a smallbasket of stocks picked either from the stocks comprising three well- known financial indexes, or from two pools of energy- related stocks.In particular, the cases of the US, UK and Brazilian investors areconsidered under the assumption that they want to invest in the SEIand prefer to access only their local stock markets due to cost savingsand/or better knowledge of the respective markets. They representtwo developed and one developing stock market, with the latterhaving its unique energy significance in the global scene. Reformsand regulations that have taken place in Brazil have brought trans-parency, sophistication and additional liquidity to its financial


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markets. Oil and other energy prices influence companies’ earningsand thus their stock prices. Hence, based on intuition and previousresearch findings, the two pools of energy- related stocks used in theanalysis should perform very well in tracking the SEI. Moreover, thestocks of various companies operating in other, non energy- relatedindustries will still be affected by the movements in energy prices.The methodology implemented can track the SEI or any other bench-mark index by investing in a basket of stocks that each of theevolutionary algorithms will determine. Baskets of a maximum of10, 15 and 20 stocks are selected from the following stock pools: DowJones Composite Average, FTSE 100, Bovespa Composite, and thetwo pools of energy- related stocks from the US and the UK stockmarkets.

The SEI represents a basket of energy commodities and serves as aperformance benchmark with limited ability for direct investment.However, the proposed approach provides investors with an optionto track the performance of this SEI using a basket of equities that areliquid and fully investable. This allows investors to get closer to theunderlying commodity market price trends, something they cannotachieve using a futures price index. Historically, futures indexreturns have lagged price index returns, with this decoupling ofperformance being a constant frustration for index investors. Forcomparison, the performance of two well- established energy excessreturn indexes are reported, namely the Dow Jones–UBS Energy Sub- Index and the Roger’s Energy Commodity Index, against theperformance of the constructed SEI and the selected portfolios.

This chapter’s findings have several positive implications forinvestors. They provide a low cost – compared to actively managedfunds – means of accessing the energy spot markets. In particular,sector rotation investment managers can benefit from the findings.By tactically shifting assets, they can over- or under- weigh specificsectors according to their economic outlook or market objective.

Index tracking and problem formulationIn the search for optimally replicating an index, different studies(Gaivoronski et al, 2004; Frino and Gallagher, 2001) focus on theperformance deviations of the tracking portfolio – ie, the trackingerror. Additionally, single- factor and Markowitz models (Larsen- Jrand Resnick, 1998; Rohweder, 1998; Wang, 1999) have been used to


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replicate the performance of an index. Furthermore, the use of thecointegration concept in building portfolios for index tracking ishighlighted by Alexander and Dimitriu (2002) and Dunis and Ho(2005).

This chapter follows the approach used in Andriosopoulos et al(2012) for reproducing the performance of an international marketcapitalisation shipping stock index and two physical shippingindexes by investing only in US stock portfolios. First, the trackingerror is measured through the root mean square error (RMSE) crite-rion. In particular, is assumed that there exist price data on N stocksand the price of an index over an (in- sample) time period [1, 2, …, T].The goal is to create a tracking portfolio consisting of at most K stocks(K < N) that replicates, as closely as possible, the index for an (outof- sample) period [T + 1, T + Δt]. The replication error of the trackingportfolio is defined as follows:

(13.1)

where rt and Rt are the returns for the tracking portfolio and theindex, respectively.

Second, except for the replication error, the return of the trackingportfolio is also of interest. To this end, the mean excess return (ER) isconsidered over the benchmark index, defined as follows:

(13.2)

Let Pit denote the price of stock i at time t, C the available capital andxi the number of units bought of stock i. The complete formulation ofthe objectives and constraints used to solve the index trackingproblem can then be expressed as follows:

Minimize: (13.3)Subject to:

(13.4)

(13.5)

RMSE = rt !Rt( )2 /Tt=1

T

!

ER = rt !Rt( )/Tt=1

T

"

f = ! !RMSE" 1!!( )!ER

PiTxi =Ci=1

N

!

zi!C ! PiTxi ! ziC "i = 1,...,N


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(13.6)

(13.7)

where 0 ≤ l ≤ 1 is a user- defined parameter that outlines the trade- off between the two objectives (tracking error and excess return). Inthe case l = 1, the tracking portfolio has as its objective to minimisethe tracking error (pure index tracking), whereas when l = 0, theportfolio’s goal is to maximize the excess return. Constraint 13.4guarantees that the value of the portfolio at the end of the in- sampleperiod is equal to the available capital C. This budgetary limitationensures that for all alternative tracking portfolios an identicalamount C is invested at the beginning of the outof- sample period.Constraint 13.5 associates a binary variable zi to each stock i, whichis used to consider whether stock i is included in the tracking port-folio (zi = 1) or not (zi = 0). The parameter e is used to impose alower bound on the proportion of the capital invested in each stock(in this study e is equal to 0.01). Finally, constraint 13.6 defines themaximum number of stocks K that can be included in the trackingportfolio.

Evolutionary solution techniquesThe optimisation model of equations 13.3–13.7 is a complex combi-natorial problem that is difficult to solve with analytical techniques.Thus, evolutionary algorithms have become particularly popular inthis context. Evolutionary algorithms were first used for addressingthe indextracking problem by Goldberg (1989), who apply a geneticalgorithm for index replication. More recent applications of geneticalgorithms in indextracking and portfolio optimisation can be foundin the works of Oh et al (2005), Chang et al (2009) and Soleimani et al(2009). Beasley et al (2003) propose an evolutionary populationheuristic, accounting for transaction costs and the possibility for revi-sion of the tracking portfolio. Their results indicate that deriving theoptimal portfolio directly from past data and not from the distribu-tion of stock returns ultimately achieve better results. Maringer andOyewumi (2007) apply DE for tracking the Dow Jones IndustrialAverage assuming different cardinality constraints in their selected

xi ! 0, zi ! 0,1{ } !i = 1,...,N

zi ! Ki=1

N

!


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portfolios. They report that the maximum number of stocks includedin the tracking portfolio must be roughly 50% of the benchmarkindex to achieve good results; any additional stocks only marginallyimprove the algorithm’s performance. The DE algorithm has alsobeen used in other studies using hybrid and multiobjective schemes(Krink et al, 2009; Krink and Paterlini, 2011), as well as in the contextof loss aversion (Maringer, 2008) and mutual fund replication(Zhang and Maringer, 2010). Other proposed algorithmic proce-dures include immune systems (Li et al, 2011), hybrid algorithms (Ruiz- Torrubiano and Suárez, 2009; Scozzari et al, 2012), robust opti-misation (Chen and Kwon, 2012) and mixed- integer programmingformulations (Canakgoz and Beasley, 2008; Stoyan and Kwon, 2010).An overview of different methods can be found in Woodside- Oriakhi et al (2011).

In the context of this chapter, the DE algorithm and a genetic algo-rithm are employed. Both are well established in the computationalintelligence literature, easy to implement and well suited forcomplex financial optimisation problems, particularly in the contextof index tracking and constrained portfolio optimisation. The appli-cation of both algorithms enables the examination of the robustnessof the results under different solution approaches.

GAs are probably the most popular evolutionary techniques. Theyare computational procedures that mimic the process of naturalevolution for solving complex optimisation problems (Goldberg,1989). A GA implements stochastic search schemes to evolve aninitial population (set) of solutions through selection, mutation andcrossover operators until a good solution is reached.

Similarly to the GA framework, DE is also a stochastic optimisa-tion method. It was developed by Storn and Price (1995) as analternative to existing metaheurtistic approaches, and it is wellsuited to continuous optimisation problems. According to Storn andPrice (1997), compared to other rival approaches, the main advan-tages of DE include its fast convergence, the use of a small set oftuning parameters, its reduced sensitivity to the initial solutionconditions and its robustness. Overall, comparisons on variousbenchmark problems show that DE is superior when compared toother evolutionary algorithms (Sarker et al, 2002; Sarker and Abbass,2004).

Both algorithms are implemented with a real- valued solution


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representation scheme. In particular, each solution is represented bya real- valued vector x ∈ °N, where N is the number of stocks in thesample. The largest positive elements of x are used to identify thestocks comprising the tracking portfolio,3 and after normalisation (tosum up to 1) they define the corresponding stock weights (w1, …,wN). The number of units bought from each stock can then be speci-fied as xi = Cwi / PiT. The appendix to this chapter provides a briefdescription of the implementations of the two evolutionary methodsused here. The parameters of the algorithms were calibrated afterexperimentation in order to achieve a good balance between thequality of the results and the solution times. The selected parametersare summarised in Table 13A.1.

BENCHMARK ENERGY INDEX, SPOT AND EQUITY DATABecause many commodities lack centralised trading, the most reli-able spot prices are for those that trade active and liquid futurescontracts, since these are typically used as a pricing benchmark. Inthe case of energy commodities, the Nymex is the world’s largestfutures exchange. Initially, a spot price energy index is constructed,constituting daily prices of the following six energy commodities,which also trade futures contracts on the Nymex:

1. Heating Oil, New York Harbour No. 2 Fuel Oil, quoted in USdollar cents/gallon (C/gal);

2. Crude Oil, West Texas Intermediate (WTI) Spot Cushing,quoted in US dollars/barrel;

3. Gasoline, New York Harbour Reformulated Blendstock forOxygen Blending (RBOB), quoted in US C/gal;

4. Natural Gas, Henry Hub, quoted in US dollars/million Britishthermal units (Btus);

5. Propane, Mont Belvieu Texas, quoted in US C/gal; and6. PJM, Interconnection Electricity Firm on Peak Price Index,

quoted in US dollars/megawatt hour.

The SEI is constructed as an unweighted geometric average of theindividual commodity ratios of current prices to the base periodprices, set at January 31, 2006, until February 1, 2010. The base datefor the SEI is the same date that the equity sample is obtained.Considering that the boom in commodity index investing is a rela-


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tively new phenomenon, more recent data are utilised to test theproposed investment strategy. The index’s construction method-ology is similar to that of the world- renowned CRB Spot CommodityIndex. The SEI is designed to offer a timely and accurate representa-tion of a long- only investment in energy commodities using atransparent and disciplined calculation.

Geometric averaging provides a broad- based exposure to the sixenergy commodities, since no single commodity dominates theindex. It also helps increase the index diversification by giving eventhe smallest commodity within the basket a reasonably significantweight. Gordon (2006) finds that a geometrically weighted index ispreferred to alternative weighting schemes, because the daily rebal-ancing allows the index not to become over- or underweighted. Thisavoids the risks that other types of indexes are subject to, such aspotential errors in data sources for production, consumption,liquidity or other errors that could affect the component weights ofthe index. Furthermore, through geometric averaging the SEI iscontinuously rebalanced, which means that the index constantlydecreases (increases) its exposure to the commodity markets thatgain (decline) in value, thus avoiding the domination of extremeprice movements of individual commodities. As Erb and Harvey(2006) point out, the indexes that rebalance annually eventuallybecome trend followers because commodity prices movementsconstantly change the weightings, whereas those that rebalance dailystay closer to the original intent of the index. In addition, Nathan(2004) shows that the indexes that use geometric rebalancing, andthus rebalance their weightings daily, generally exhibit lowervolatility.

The mathematical specification used to calculate the geometricaverage SEI is the following:

(13.8)

where, SEIi is the index for any given day, i represents each one of thesix commodities comprising the index, Pt

i is the price of eachcommodity for any given day, and P0

i is the price of each commodityin the base period.

The SEI provides a stable benchmark structure for the index,making SEI suitable for institutional investment strategies. The

SEIt =Pti

P0ii=1

i

!"

#$

%

&'

1n

!100 =Pt

1

P01 !

Pt2

P02 !…!

Pti

P0i

n !100; i = 1,2,…,6;n = 6


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stable composition of the index is an important element, becausewhen the composition of an index changes over time, the averagereturn of the index does not equal the return of the average indexconstituent, especially when indexes are equally weighted. The lattermakes historical index performance a bad proxy to prospective indexreturns, thus distorting the information that investors seek (Erb andHarvey, 2006). Moreover, it is a better means for evaluating themovement in energy commodity prices because it is based on spotprices and not on prices for future delivery that are subject to rollyields driven by contango and backwardation. The equity dataincludes daily prices for stocks picked from the Dow JonesComposite Average, FTSE 100 and Bovespa Composite indexes. Theequity dataset also includes stocks from a unique pool of energy- related stocks from the US and UK stock markets. The selection of theequities included in the two pools is made according to the IndustryClassification Benchmark (ICB) jointly developed by Dow Jones andthe FTSE (see Appendix at the end of this chapter). In the sampleused, the two filtered pools include all stocks from the US and UKstock markets that are engaged in the various phases of energyproduction and processing, listed in the following four sectors: oiland gas producers; oil equipment, services and distribution; alterna-tive energy; and electricity. After applying the filtering procedure tothe US and UK stock markets, two energy- related stock pools areconstructed, hereafter named US Filter and UK Filter, respectively.

To test the proposed heuristic approach and the efficiency of boththe DE and GA as indextracking methodologies, five datasets areselected. All stock prices are closing prices adjusted dividendsaccording to the annualised dividend yield, and they are all obtainedon a daily basis for the period January 31, 2006 to February 1, 2010from Thomson Financial Datastream. All stock prices are in USdollars, thus reflecting the local currency exchange rate against theUS$ at every point in time for the period examined. Should acompany cease trading due to an event (merger, bankruptcy, etc)within the test period, it is dropped from the sample – that is why thetotal number of stocks in the FTSE 100 and Bovespa pools is less thanthe total number of stocks included in each index. Moreover, afteradjusting for all US and UK bank holidays, 1,008 observations aresorted to calculate daily returns for each stock. Considering 252trading days in a calendar year, the heuristic approach is tested


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under various assumptions by selecting the first year as the in- sample period and the last three years as the outof- sample period.The final five datasets have the following number of stocks: N = 41(UK Filter), N = 53 (Bovespa Composite), N = 65 (Dow JonesComposite Average), N = 77 (US Filter) and N = 97 (FTSE 100 index).

TRACKING THE SPOT ENERGY INDEXThe performance characteristics of the proposed strategy are exam-ined. The stocks picked by both the DE and the GA are used to trackthe performance of the SEI. The initial capital of the investment port-folio is set equal to C = US$100,000, where both the DE and the GAconverge at the end of the in- sample period. In the empiricalanalysis, tracking portfolios consisting of maximum K stocks areused with K = 10, 15 and 20. Three different trade- offs betweentracking error and excess return are also considered, with l = 0.6, 0.8and 1, thus moving from maximising excess return to minimisingtracking error. The heuristic is then repeated 10 times with the sameset of parameters per run, from which the best solution is chosen.

Figure 13.1 displays the SEI against quarterly rebalanced portfo-lios selected from the DE and GA, respectively. The portfolios consistof a maximum of 15 stocks, the FTSE 100, DJIA, Bovespa and UKFilter and US Filter, respectively; results are shown for l = 1. Lookingat the figures, it is clear that during and towards the end of the reces-sion period, the benchmark index can be best tracked with theBovespa baskets, followed by the UK Filter baskets; whereas, duringthe last year (2010) it is the US Filter and DJIA baskets that performbetter. The portfolios comprised of optimally selected energy- relatedstocks can successfully track the SEI, generating similar returns formost of the outof- sample period. The US Filter and UK Filter resultsverify that, when energy- related stocks are selected, they can betterreplicate the risk and return trade- off of the SEI. The same applies forthe Bovespa baskets, since the Brazilian stock exchange has a largenumber of energy- and commodity- related listed companies thatwould closely follow any developments in the international energymarkets. In addition, between the DE and GA selected portfolios,from the graphs it seems that the latter ones can follow more closelythe performance of the SEI, achieving highest excess returns for thefinal outof- sample year.

Table 13.1 presents the RMSEs and the mean excess returns of


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both the genetic and differential evolution algorithms employed,under all three rebalancing strategies: buy- and- hold, monthly andquarterly rebalancing. Using formal statistical evaluation criteria, thebetter tracking performance of the UK Filter and US Filter baskets isalso confirmed. In terms of the competing portfolios’ RMSEs, the DEis more consistent across the various portfolios, whereas the GAselects portfolios that exhibit larger differences between the worst-and best- performing ones. Additionally, in general, GA tends toselect portfolios that have fewer tracking errors and thus track better


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Figure 13.1 Out-of-sample tracking of the SEI with the Bovespa, DJIA, FTSE 100, UK Filter and US Filter baskets respectively; λ = 0.8, with maximum 15 stocks in the basket, rebalanced quarterly

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the benchmark index when compared to the ones selected from theDE.

Another interesting observation is that, although the RMSEs areimproved when rebalancing occurs, increasing the frequency fromquarterly to monthly has only a marginal effect. These results aremore profound for the portfolios selected by the DE, and align withDunis and Ho (2005), who find that, when comparing alternativerebalancing frequencies, a quarterly portfolio update is preferable tomonthly, semi- annual or annual reallocations. In terms of theirexcess returns, in most cases the portfolios selected by the GA tend tooutperform the ones selected by the DE. The UK Filter and US Filterbaskets, which also have the lowest tracking errors (see panels D andE on Table 13.1), have excess returns that in some cases are positive,indicating that the selected portfolios, on average over the outof- sample period, outperform the SEI.

In the case of the US Filter baskets selected by the GA, the index isconstantly outperformed in terms of excess returns (8.10% for K = 20and l = 0.6 under monthly rebalancing, and 6.14% for K = 15 and l =0.6 under quarterly rebalancing); there is only one exception for bothrebalancing frequencies, in which l = 1 and K = 10 when the portfo-lios underperform the index. This is an indication that the trade- offcriterion does work, and leads to portfolios that compromise anyexcess return over a better tracking performance as expressed by thesmaller RMSEs. Thus, taking into account the fact that commodityindexes performed better compared to the financial indexes over the three- year outof- sample period (except the Bovespa Composite),with the methodology employed the performance of the SEI isclosely replicated, and in the case of the energy- related stock portfo-lios, the benchmark index is even outperformed.

For Table 13.1, panels A, B, C, D and E report the outof- sampledaily RMSE and mean daily percentage excess returns, as defined inequations 13.9 and 13.10, respectively. Under both rebalancingstrategies, the weights of the tracking portfolios are estimated basedon the available data in the rolling window in- sample period (oneyear) every month and quarter, respectively. Portfolios’ returns areadjusted for transaction costs of 0.5% for each transaction.

In terms of the risk–return trade- off (l), it is observed that resultsare very similar among portfolios where l = 0.8 and 1. In most cases,the risk–return trade- off criterion tends to perform well, selecting


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COMMODITY

INVESTIN

GANDTRADING

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Table 13.1 Out- of- sample index tracking performance of the selected portfolios.

No rebalance Monthly rebalance Quarterly rebalance

RMSE Mean ER (%) RMSE Mean ER (%) RMSE Mean ER (%)

(K) (λ) DE GA DE GA DE GA DE GA DE GA DE GA

Panel A: Bovespa10 0.6 0.0346 0.0344 0.0136 0.0324 0.0331 0.0329 –0.0432 –0.0104 0.0333 0.0332 –0.0389 0.0134

0.8 0.0343 0.0359 0.0176 0.0347 0.0330 0.0326 –0.0480 –0.0471 0.0332 0.0329 –0.0438 –0.04161 0.0343 0.0362 0.0189 0.0133 0.0330 0.0327 –0.0545 –0.0689 0.0333 0.0332 –0.0472 –0.0236

15 0.6 0.0345 0.0359 0.0161 0.0239 0.0331 0.0327 –0.0427 –0.0063 0.0333 0.0332 –0.0411 –0.01480.8 0.0343 0.0361 0.0181 0.0334 0.0330 0.0327 –0.0487 –0.0298 0.0332 0.0331 –0.0431 –0.02801 0.0343 0.0356 0.0180 0.0238 0.0330 0.0327 –0.0533 –0.0418 0.0332 0.0333 –0.0442 –0.0312

20 0.6 0.0345 0.0354 0.0148 0.0233 0.0331 0.0331 –0.0436 0.0094 0.0333 0.0335 –0.0417 0.02090.8 0.0343 0.0358 0.0186 0.0329 0.0330 0.0327 –0.0488 –0.0052 0.0332 0.0333 –0.0427 0.00001 0.0343 0.0357 0.0164 0.0284 0.0330 0.0328 –0.0541 –0.0346 0.0333 0.0334 –0.0461 –0.0210

Panel B: DJIA10 0.6 0.0319 0.0328 –0.0232 –0.0257 0.0318 0.0315 –0.0479 –0.0115 0.0319 0.0319 –0.0302 –0.0243

0.8 0.0319 0.0330 –0.0238 –0.0210 0.0318 0.0316 –0.0511 –0.0312 0.0318 0.0318 –0.0323 –0.02731 0.0319 0.0330 –0.0249 –0.0218 0.0318 0.0313 –0.0522 –0.0274 0.0319 0.0317 –0.0314 –0.0172

15 0.6 0.0320 0.0329 –0.0244 –0.0200 0.0319 0.0315 –0.0503 –0.0332 0.0319 0.0318 –0.0297 –0.01720.8 0.0319 0.0330 –0.0240 –0.0250 0.0318 0.0314 –0.0515 –0.0244 0.0319 0.0319 –0.0311 –0.01921 0.0319 0.0328 –0.0246 –0.0239 0.0318 0.0313 –0.0515 –0.0410 0.0319 0.0319 –0.0314 –0.0283

20 0.6 0.0319 0.0328 –0.0228 –0.0251 0.0319 0.0315 –0.0514 –0.0239 0.0319 0.0319 –0.0313 –0.00050.8 0.0319 0.0329 –0.0235 –0.0289 0.0318 0.0315 –0.0529 –0.0300 0.0319 0.0318 –0.0301 –0.03321 0.0319 0.0328 –0.0253 –0.0323 0.0318 0.0313 –0.0505 –0.0344 0.0319 0.0317 –0.0308 –0.0051

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Panel C: FTSE 10010 0.6 0.0315 0.0318 –0.0450 –0.0359 0.0309 0.0299 –0.0597 –0.0260 0.0308 0.0303 –0.0438 0.0106

0.8 0.0317 0.0316 –0.0469 –0.0246 0.0309 0.0302 –0.0701 –0.0416 0.0309 0.0305 –0.0475 –0.02551 0.0316 0.0314 –0.0495 –0.0193 0.0310 0.0300 –0.0735 –0.0635 0.0310 0.0307 –0.0461 –0.0334

15 0.6 0.0315 0.0318 –0.0512 –0.0253 0.0309 0.0303 –0.0674 –0.0327 0.0308 0.0303 –0.0468 –0.01800.8 0.0316 0.0313 –0.0477 –0.0220 0.0309 0.0302 –0.0634 –0.0449 0.0309 0.0306 –0.0416 –0.01271 0.0316 0.0312 –0.0490 –0.0175 0.0310 0.0303 –0.0699 –0.0682 0.0310 0.0306 –0.0456 –0.0349

20 0.6 0.0315 0.0317 –0.0507 –0.0271 0.0309 0.0303 –0.0705 –0.0311 0.0308 0.0305 –0.0442 –0.00920.8 0.0316 0.0313 –0.0484 –0.0297 0.0310 0.0303 –0.0681 –0.0656 0.0309 0.0305 –0.0445 –0.01451 0.0316 0.0313 –0.0492 –0.0245 0.0310 0.0301 –0.0679 –0.0600 0.0310 0.0306 –0.0449 –0.0208

Panel D: UK Filter10 0.6 0.0318 0.0309 –0.0900 –0.0834 0.0299 0.0294 –0.0712 0.0019 0.0300 0.0296 –0.0681 –0.0032

0.8 0.0315 0.0312 –0.0818 –0.0834 0.0300 0.0290 –0.0680 –0.0725 0.0301 0.0296 –0.0611 –0.04121 0.0317 0.0307 –0.0809 –0.0751 0.0300 0.0292 –0.0713 –0.1371 0.0301 0.0297 –0.0632 –0.1049

15 0.6 0.0312 0.0309 –0.0825 –0.0519 0.0299 0.0294 –0.0782 –0.0427 0.0300 0.0298 –0.0711 –0.03410.8 0.0313 0.0309 –0.0847 –0.0408 0.0300 0.0293 –0.0720 –0.0501 0.0300 0.0296 –0.0707 –0.04101 0.0313 0.0308 –0.0846 –0.0531 0.0300 0.0293 –0.0782 –0.1083 0.0301 0.0297 –0.0601 –0.0459

20 0.6 0.0311 0.0305 –0.0796 –0.0586 0.0299 0.0297 –0.0764 –0.0508 0.0300 0.0299 –0.0717 –0.04460.8 0.0311 0.0303 –0.0858 –0.0451 0.0299 0.0294 –0.0752 –0.0790 0.0300 0.0298 –0.0697 –0.03911 0.0311 0.0304 –0.0763 –0.0516 0.0300 0.0295 –0.0747 –0.0794 0.0301 0.0296 –0.0676 –0.0494

Panel E: US Filter10 0.6 0.0307 0.0329 –0.0258 –0.0442 0.0306 0.0297 –0.0449 0.0710 0.0309 0.0307 –0.0364 0.0249

0.8 0.0308 0.0321 –0.0265 –0.0780 0.0309 0.0295 –0.0603 0.0607 0.0310 0.0300 –0.0345 0.02401 0.0309 0.0318 –0.0234 –0.0314 0.0310 0.0294 –0.0688 –0.0278 0.0310 0.0298 –0.0367 –0.0172

15 0.6 0.0307 0.0321 –0.0246 –0.0581 0.0309 0.0306 –0.0497 0.1241 0.0310 0.0308 –0.0322 0.06140.8 0.0308 0.0327 –0.0244 –0.0511 0.0309 0.0296 –0.0575 0.0212 0.0310 0.0301 –0.0336 0.00161 0.0308 0.0322 –0.0254 –0.0566 0.0309 0.0295 –0.0648 –0.0027 0.0310 0.0302 –0.0342 0.0204

20 0.6 0.0307 0.0327 –0.0261 –0.0668 0.0309 0.0301 –0.0510 0.0810 0.0310 0.0308 –0.0274 0.03450.8 0.0308 0.0319 –0.0251 –0.0320 0.0309 0.0296 –0.0603 0.0210 0.0310 0.0303 –0.0329 0.03691 0.0307 0.0311 –0.0226 –0.0649 0.0309 0.0294 –0.0662 0.0071 0.0310 0.0301 –0.0352 0.0126

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portfolios with higher returns and also relatively higher RMSEs.Moreover, the portfolios selected by the GA tend to be more consis-tent when the risk–return trade- off rule is applied, compared to theones selected by the DE. Overall, when considering both the trackingperformance and the excess returns of the various portfolios, thosewith l = 0.8 should be preferred. As far as the criterion regarding themaximum number of stocks is concerned, in all three rebalancingscenarios, portfolios with K = 10 tend to perform worst in terms ofRMSEs, but do slightly better in terms of excess returns, for both theDE and GA selected portfolios. This is also an indication that themore stocks that are included in the portfolio, the higher the transac-tion costs when a rebalancing occurs. Overall, it is suggested thatportfolios with a maximum of 15 stocks should be selected, as therestill seems to be a valuable compensation for the additional informa-tion and diversification when rebalancing, against the extrarebalancing costs.

According to the results, for both algorithms, monthly rebalancingis overall the best option in terms of RMSEs, closely followed byquarterly rebalancing, whereas when looking at excess returns, quar-terly rebalancing appears to improve portfolio performance. Thereturn of a buy- and- hold portfolio may be higher than that of a rebal-anced portfolio when transaction costs are considered, but it isimportant to determine the source of the higher return – whether it isgreater capital efficiency as expressed by a higher Sharpe or informa-tion ratio, or greater risk. Plaxco and Arnott (2002) showed thatrebalanced portfolios typically have higher Sharpe ratios than buy- and- hold portfolios, a finding that suggests that the possibleoutperformance of a buy- and- hold portfolio may be the result ofgreater risk. Results are more apparent for the GA portfolios, as forthe DE portfolios the difference between monthly and quarterlyrebalancing is only marginal. In the case of the UK Filter basketpicked by the GA, there is an obvious difference in performancewhen rebalancing quarterly as opposed to monthly rebalancing. Amore in- depth analysis comparing the portfolios’ information ratiosis presented in the following section. On average, based on theresults from Table 13.1, K = 15 and l = 0.8 is the most desirablecombination providing the best results for most tracking portfolios.It is, of course, up to the investors’ risk–return appetite to decidewhether rebalancing the portfolio quarterly, which comes with an


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extra cost, is better than no rebalancing at all. The same appliesregarding whether l = 0.8 should be used instead of the more risky trade- off when l = 0.6.

Statistical properties of selected portfoliosTable 13.2 presents some distributional statistics of the selected port-folios’ returns under the quarterly rebalancing scenario.4 Also, inpanel F, the statistics and relevant performance measures for thefollowing indexes are reported for comparison reasons: two totalreturn energy commodity indexes – the DJ UBS- Energy and RogersEnergy Commodity; the three stock indexes from which stocks weredrawn to construct the tracking portfolios – Bovespa, DJIA and FTSE100; and, finally, the most commonly used benchmark in the financeindustry, the S&P 500. According to the historical annualised volatil-ities for the outof- sample period, the SEI is more volatile than the DJ UBS- Energy and Rogers Energy Commodity Indexes – 48.40% ascompared to 36.21% and 41.11% respectively. The respectivevolatility of the equity indexes is in the range of 27–38%. However,when comparing the information ratios, only the Bovespa index isable to generate a better risk–return performance compared to theSEI.

Table 13.2 presents the annualised returns and volatilities of thetracking portfolios, the skewness and kurtosis, the correlation coeffi-cient between the returns of the benchmark index and the portfoliothat is used each time to replicate this benchmark, and the informa-tion ratio under the no rebalancing strategy. Panels A, B, C, D and Erepresent the portfolios that include stocks picked each time from theDow, FTSE 100, Bovespa, UK Filter and US Filter stock pools. Panel Fpresents, for comparison, the relevant performance measures for twototal return energy commodity indexes, the DJ UBS- Energy andRogers Energy Commodity; for the three stock indexes from whichstocks were drawn to construct the tracking portfolios, Bovespa,DJIA and FTSE 100; and the S&P 500.

Moving from no rebalancing to monthly rebalancing, the informa-tion ratios tend to go down in all cases, except in the case of the USFilter baskets for GA, and that of the UK Filter baskets for both DEand GA. This can be explained by the higher transaction costs, whichhave a greater impact on the portfolios’ returns, especially in fallingmarkets. It can be argued that when rebalancing, the additional


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Table 13.2 Distributional statistics of portfolios’ daily returns under quarterly rebalancing

An. Ret (%) An. Vol. (%) Skewness Ex. Kurtosis Correl. Info Ratio

(K) (λ) DE GA DE GA DE GA DE GA DE GA DE GA

Panel A: Bovespa10 0.6 –6.79 6.38 35.68 38.32 –0.572 –0.588 7.688 7.146 23.76 27.67 –0.185 0.064

0.8 –8.04 –7.48 35.39 36.15 –0.541 –0.499 7.696 7.198 23.72 26.04 –0.209 –0.2001 –8.88 –2.94 35.49 37.28 –0.537 –0.565 7.846 7.791 23.62 26.46 –0.225 –0.113

15 0.6 –7.36 –0.73 35.72 38.38 –0.578 –0.516 7.699 7.113 23.84 28.06 –0.196 –0.0710.8 –7.86 –4.05 35.49 37.33 –0.548 –0.620 7.910 7.932 23.79 26.89 –0.206 –0.1341 –8.14 –4.87 35.45 36.76 –0.532 –0.461 7.734 7.889 23.65 25.36 –0.211 –0.149

20 0.6 –7.49 8.27 35.73 38.45 –0.570 –0.494 7.661 7.896 23.95 26.36 –0.199 0.0990.8 –7.77 3.01 35.42 37.53 –0.544 –0.481 7.675 7.498 23.57 26.21 –0.204 0.0001 –8.62 –2.29 35.50 37.69 –0.534 –0.485 7.801 8.467 23.64 25.94 –0.220 –0.100

Panel B: DJIA10 0.6 –4.61 –3.13 19.76 22.72 0.543 0.329 12.944 9.405 8.96 13.36 –0.151 –0.121

0.8 –5.14 –3.87 19.79 22.40 0.563 0.444 13.201 9.707 9.13 13.44 –0.161 –0.1361 –4.90 –1.33 19.76 22.87 0.630 0.437 13.884 10.343 8.97 14.63 –0.156 –0.086

15 0.6 –4.48 –1.33 19.85 22.44 0.536 0.405 12.659 10.195 9.01 13.63 –0.148 –0.0860.8 –4.83 –1.83 19.80 23.63 0.563 0.210 13.169 8.742 9.04 14.64 –0.155 –0.0951 –4.91 –4.12 19.87 24.36 0.600 0.475 13.712 12.793 8.97 15.65 –0.156 –0.141

20 0.6 –4.87 2.88 19.84 22.41 0.543 0.335 12.801 7.553 9.00 12.49 –0.156 –0.0020.8 –4.58 –5.36 19.83 24.40 0.542 0.355 13.054 9.969 9.07 16.10 –0.150 –0.1651 –4.75 1.72 19.86 23.42 0.587 0.526 13.684 10.842 8.93 15.57 –0.153 –0.026

Panel C: FTSE 10010 0.6 –8.03 5.68 25.87 28.61 0.040 –0.010 5.981 6.623 24.57 30.30 –0.225 0.056

0.8 –8.96 –3.41 25.82 29.42 –0.019 0.082 5.743 8.084 24.11 30.01 –0.244 –0.1321 –8.62 –5.42 26.14 28.74 0.039 0.018 6.319 8.876 24.07 28.52 –0.236 –0.173

15 0.6 –8.78 –1.54 26.18 29.32 0.006 0.060 6.170 7.373 25.07 31.08 –0.241 –0.0940.8 –7.49 –0.19 26.03 28.89 0.004 –0.026 6.140 7.309 24.12 29.36 –0.214 –0.0661 –8.47 –5.78 26.26 30.48 –0.016 –0.106 6.310 7.594 24.01 30.57 –0.233 –0.180

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20 0.6 –8.12 0.68 26.12 29.30 0.033 0.091 6.108 7.646 25.00 29.88 –0.228 –0.0480.8 –8.22 –0.64 26.12 29.07 –0.023 0.076 6.140 7.321 24.23 30.02 –0.229 –0.0751 –8.32 –2.24 26.17 29.43 –0.037 0.068 6.138 7.613 23.62 29.92 –0.230 –0.108

Panel D: UK Filter10 0.6 –14.16 2.21 18.43 23.56 –1.545 –0.908 11.532 5.806 22.81 29.94 –0.360 –0.017

0.8 –12.40 –7.37 18.40 23.53 –1.540 –1.322 11.741 8.974 22.59 30.14 –0.323 –0.2211 –12.91 –23.42 18.47 22.11 –1.506 –1.353 11.692 9.453 22.38 28.14 –0.333 –0.560

15 0.6 –14.91 –5.58 18.45 23.98 –1.556 –0.908 11.403 4.967 23.06 28.91 –0.376 –0.1810.8 –14.81 –7.32 18.57 23.19 –1.602 –1.126 12.077 6.813 22.93 30.08 –0.373 –0.2201 –12.13 –8.57 18.59 24.84 –1.560 –0.947 11.759 5.099 22.40 30.45 –0.317 –0.245

20 0.6 –15.06 –8.22 18.38 24.71 –1.595 –1.115 11.618 6.180 22.97 29.35 –0.379 –0.2370.8 –14.55 –6.86 18.38 24.85 –1.600 –0.995 11.910 5.192 22.74 30.23 –0.368 –0.2091 –14.03 –9.44 18.48 23.93 –1.611 –1.037 11.846 6.240 22.36 30.48 –0.357 –0.265

Panel E: US Filter10 0.6 –6.16 9.29 20.51 26.77 –0.303 0.650 28.721 16.322 17.51 26.39 –0.187 0.129

0.8 –5.70 9.06 20.64 24.56 –0.246 0.018 27.642 6.268 16.95 28.30 –0.177 0.1271 –6.23 –1.33 20.68 24.22 –0.289 –0.217 29.105 7.641 17.55 29.06 –0.188 –0.091

15 0.6 –5.12 18.48 20.57 26.87 –0.252 –0.104 28.952 5.516 17.48 25.97 –0.165 0.3170.8 –5.47 3.41 20.63 25.42 –0.200 –0.165 28.577 8.188 17.38 28.33 –0.172 0.0081 –5.62 8.15 20.73 24.86 –0.194 0.000 28.466 6.699 17.42 27.10 –0.175 0.107

20 0.6 –3.91 11.69 20.58 27.18 –0.289 –0.154 28.874 5.360 17.46 26.41 –0.141 0.1780.8 –5.27 12.30 20.65 26.32 –0.206 0.287 28.549 7.590 17.31 27.99 –0.168 0.1931 –5.87 6.19 20.84 26.44 –0.235 0.371 28.229 11.545 17.32 29.28 –0.180 0.067

An. Ret (%) An. Vol. (%) Skewness Ex. Kurtosis Correl. Info Ratio

Panel F: IndexesSEI 3.01 48.40 0.094 2.283 – –Bovespa 13.21 38.04 0.026 4.875 20.09 0.185DJIA –7.07 28.03 –0.053 4.636 12.90 –0.191FTSE 100 –6.01 27.42 –0.009 5.374 24.34 –0.182S&P500 –9.46 30.07 –0.162 5.999 14.51 –0.235DJ UBS Energy- TR –18.94 36.21 –0.166 1.102 43.83 –0.477Rogers Energy Commodity- TR –6.15 41.11 –0.189 2.099 44.02 –0.192

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information available from the latest price data does make a differ-ence in reducing the portfolios’ volatility, but the small returnimprovement coupled with the rebalancing costs outweighs thevolatility benefits. Results are consistent for all cases for the risk–return trade- off l. Between monthly and quarterly rebalancing, thedifferences are relatively small, but the information ratios are, inmost cases, higher for the quarterly rebalanced portfolios. Under the buy- and- hold scenario, the best performance in terms of informationratios is reported for the Bovespa portfolios, and under both monthlyand quarterly rebalancing this is reported for the US Filter portfolios.In most cases, negative information ratios are reported, indicatingthat these portfolios over the outof- sample period underperformagainst the benchmark, as they are associated with the lowest excessreturns.5 This observation can be explained by the fact that energymarkets, as represented by the SEI, have been resistant to theeconomic recession, even although they have experienced one of themost severe up- and- down trends in their history.

The relatively low correlations of the selected equity portfolioswith the SEI (between 9% and 31%) suggest that investors who wantto participate in the energy sector can still benefit from the additionof the selected baskets. This observation aligns with the findings ofBuyuksahin et al (2010), that the correlation between equity andcommodity returns is not often greater than 30%. Also, correlation isnot the most appropriate performance measure, as it only measuresthe degree to which the selected equity baskets and the SEI move intandem, and does not capture the magnitude of the returns and theirtrajectories over time. Equity returns deviate from a normal distribu-tion, displaying skewness and fat tails. The same is true for thereturns of the SEI that exhibit positive skewness and relatively highexcess kurtosis. Both futures commodity indexes have excesskurtosis similar to the SEI, with their skewness, however, beingnegative. Most equity portfolios selected by both the DE and GAexhibit negative skewness, indicating that the equity portfolios havemore weight in the left tail of the distribution, in contrast with theSEI, which has more weight in the right tail.

Finally, as a robustness check, a “naïve” strategy of randomlyselected stocks has been tested, forming equally weighted portfoliosconstituted of 10, 15 and 20 stocks. The stocks are selected from thesame five equity pools used by the EAs from a uniform distribution,


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thus giving equal probability for all stocks chosen. The evidenceconfirms that the strategy and methodology used in this chapter aremuch more efficient and stable in achieving a good tracking perfor-mance (low RMSEs), and good returns relative to the SEI (positive orvery small negative ERs). Under the “naïve” strategy, there is a largedispersion of outcomes and no consistency.6

CONCLUSIONSIn this chapter, a geometric average Spot Energy Index is constructedand then its performance is reproduced with stock portfolios. This isachieved by investing in small baskets of equities selected from fivestock pools: the Dow Jones, FTSE 100, Bovespa Composite and theUK and US Filters. The investment methodology used employs twoadvanced evolutionary algorithms: the GA and the DE. Both algo-rithms are self- adaptive stochastic optimisation methods, superior toother rival approaches when applied to the indextracking problem.To test the performance of the tracking baskets, three different rebal-ancing scenarios were examined, also taking transaction costs intoconsideration: buy- and- hold; monthly rebalancing; and quarterlyrebalancing. For comparison reasons, the performance of a “naïve”investment strategy of randomly selected stocks forming equallyweighted portfolios was also reported.

It was found that energy commodities, as proxied by the SEI, canhave equity- like returns, since they can be effectively tracked withstock portfolios selected by the investment methodology followedhere. Overall, during the three- year period examined, which reflectsa period before, during and towards the end of the global economicrecession, an investor would realise positive returns by investing incommodities, as the SEI returns suggest. With the methodologyemployed, that performance is closely replicated and, in the case ofthe energy- related stock portfolios and those selected from theBovespa equity pool, the benchmark index is even outperformed. Inmost cases, there seem to be no major differences between the DEand GA selected portfolios, although the GA tends to select portfo-lios that have a lower tracking error. Both algorithms mostly do notutilise the maximum number of stocks allowed to select, with the DEbeing more stable in the number of stocks picked between thevarious cases of the risk–return trade- off; the GA tends to select port-folios quite different in terms of their composition.


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On average, based on the results presented here, portfolios with 15stocks and a risk–return trade- off value of 0.8 are the most desirablecombination providing the best results for most tracking portfolios.Also, it was found that when rebalancing, the additional informationavailable from the latest price data does make a difference onreducing the portfolios’ volatility; the resulting return deterioration,however, outweighs the volatility benefits leading to smaller infor-mation ratios. Moving from the buy- and- hold strategy to quarterlyrebalancing and then to the more frequent monthly rebalancingstrategy, returns tend to deteriorate for most selected portfolios, byboth the DE and the GA. Nonetheless, the same holds for the portfo-lios’ volatilities that also tend to go down when moving from norebalancing to the more frequent one. Between monthly and quar-terly rebalancing, the differences are relatively small in terms of theportfolios’ return and volatility performance; however, the informa-tion ratios are in almost all cases higher for the quarterly rebalancedportfolios. The only exception is for the US Filter in the case of thebaskets selected by the GA. Thus, it was concluded that greatercapital efficiency can be achieved with rebalancing, preferably everyquarter, compared to the buy- and- hold strategy.

The investment approach proposed in this chapter for tracking theperformance of the energy sector with stocks selected by two innov-ative evolutionary algorithms promotes a cost- effectiveimplementation and true investability. While most mutual fundscannot invest in commodities directly, they can track the perfor-mance of the SEI by investing in the stocks selected by theevolutionary algorithms used here. There are many investmenthouses around the globe that use evolutionary algorithms for tacticalasset management.7 The work and findings presented in this chaptercan encourage asset and fund managers to recognise the importanceof the energy sector and prompt them to set up similar funds thatwill track the constructed Spot Energy Index. To that end, theproposed methodology suggests an effective, and at the same time least- expensive, way to operate such a fund, giving the full flexibilityof any investment style, long or short, that equities can provide.


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APPENDIXDifferential evolution algorithmDE is a population- based stochastic optimization algorithm thatemploys mutation, recombination (crossover) and selection opera-tors to evolve iteratively an initial set (population) of NP randomlygenerated N-dimensional solutions. At each iteration (generation),the algorithm applies the aforementioned evolutionary operators toeach one of the available solutions. In particular, let xi

G denote thesolution vector i (i = 1, …, NP) at a generation G, xG

ij be the jth elementof xi

G, and x*G the best solution from generation G (specified

according to the problem’s objective function). Having xiG as the

starting basis, a new solution xiG+1 is constructed replacing xi

G in thenext generation G + 1. The solution updating process is performed inthe following three steps:

1. A mutant solution is constructed by combining xiG with x*

G

and two other randomly selected (different) solutions x’ and x’’from the current generation: vi = xi

G + F × (x*G – xi

G) + F × (x’ –x’’). The mutation constant F ∈ (0,2] controls the rate at whichthe population evolves.

2. The parent solution xiG and the mutant vector vi are recom-

bined to produce a crossover solution ui, using the exponentialscheme as shown in Figure 13A.1 (for simplicity the generationindex G is not shown in the figure), where l and j* are randomlyselected from {1, 2, …, N}, such that the part of ui derived fromvi is analogous to a user- defined crossover probability CR (withhigher values corresponding to a stronger impact of vi).

3. The crossover solution ui is compared against the parent vectorxi,G on the basis of the problem’s objective function f. If f(ui) ≤f(xi

G), then xiG+1 is set equal to ui (ui replaces xi,G in the next

generation); otherwise, xiG+1 is set equal to xi

G.

The iterative procedure terminates when a stopping criterion is met(eg, after a predefined number of generations is explored).

Genetic algorithmSimilarly to the DE algorithm, a GA is also a population- basedstochastic optimisation process. It uses the same evolutionary opera-tors, but implements them in a different way and does not follow the


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greedy approach adopted by DE. Starting with an initial (random)population of solutions, the algorithm proceeds iteratively over anumber of generations. In the GA implemented in this chapter,the following algorithmic steps are performed at each iteration(generation).

1. A pair of parent solutions x and y is selected from the currentpopulation using a tournament selection procedure. Under thisscheme, k individuals (tournament size) are randomly selectedfrom the population with replacement, and only the best indi-vidual (according to the problem’s objective function) isselected as a parent.

2. The parent solutions are used to perform the crossover opera-tion with a pre- specified crossover probability (this probabilitycontrols the frequency with which crossover is performed).Under the arithmetic crossover scheme this operation leads to anew pair of solutions x’ = rx + (1 – r)y { x’, y’} and y’ = (1 – r)x +ry, where r is a random number drawn from the uniform distri-bution in [0, 1].

3. The crossover solutions are subject to mutation. In this studythe uniform mutation strategy is employed, under which pmNrandomly selected elements of a solution vector are replaced byrandom values selected uniformly from a pre- specified range.The mutation probability pm controls the frequency of themutation changes.

The pair of solutions resulting from the mutation operator is placedin the next generation of solutions, and the above three steps arerepeated until the new population is fully formulated. The proce-dure ends as soon as a termination criterion is met (eg, the


362

Figure 13A.1 DE’s exponential crossover scheme

Parent solutionxi1, xi2, …, xiN

xi1 xi2 xiNxi,l–1 vi,l+1 vi, j*–1 xij*vilCrossoversolution

Parent solutionvi1, vi2, …, viN


population converges or the pre- specified number of generations isreached).

INDUSTRY CLASSIFICATION BENCHMARKThe ICB is a company classification system developed jointly byDow Jones and FTSE. It is used to segregate markets into a number ofsectors within the macroeconomy. The ICB uses a system of 10industries, partitioned into 19 super sectors, which are furtherdivided into 41 sectors, which then contain 114 subsectors.

The principal aim of the ICB is to categorise individual companiesinto subsectors based primarily on a company’s source of revenue orwhere it constitutes the majority of revenue. If a company is equallydivided among several distinct subsectors, the judging panel fromboth Dow Jones and FTSE makes a final decision. Firms may appealtheir classification at any time.


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Table 13A.1 Parameters of the algorithms

GA DE

Population size: Population size: Generations: 100 Generations: 100Crossover: arithmetic (80% probability) Mutation: rand- to- best/1 (F = 0.7)Selection: tournament (size = 4) Crossover: exponential (CR = 0.5)Mutation: uniform (0.5% probability)

Industry Super- sector Sector Sub- sector

0001 Oil & gas 0500 Oil & gas 0530 Oil & gasproducers

0533 Exploration &production

0537 Integrated oil & gas

0570 Oil equipment,services & distribution

0573 Oil equipment &services

0577 Pipelines

0580 Alternative energy 0583 Renewable energyequipment

0587 Alternative fuels

7000 Utilities 7500 Utilities 7530 Electricity 7535 Conventional electricity

7537 Alternative electricity

Table 13A.2 Industry Classification Benchmark (ICB) codes


The ICB is used globally (although not universally) to divide themarket into increasingly specific categories, allowing investors tocompare industry trends between well- defined subsectors. The ICBreplaced the old classification systems used by Dow Jones and FTSEon January 3, 2006, and it is still used by the NASDAQ, NYSE andseveral other markets around the globe. All ICB sectors are repre-sented on the New York Stock Exchange except Equity InvestmentInstruments (8980) and Non- equity Investment Instruments (8990).

Table 13A.2 presents the ICB codes used for filtering all US andUK stock markets, creating the two energy- related stock pools: theUS Filter and UK Filter, respectively.

1 Revisions can occur for a number of reasons, including additions or deletions, mergers,splits and dividends.

2 The first listed commodity ETF was the streetTRACKS Gold Shares ETF, with its sole assetsbeing gold bullion and, from time to time, cash.

3 If the number of positive elements of x is smaller than K, then all positive elements of x areused.

4 The results for both the “no rebalancing” and “monthly rebalancing” scenarios are availableupon request.

5 Note that investors who would have taken short positions on these baskets would realisethe highest excess returns.

6 The results of the “naive” strategy are available upon request.7 First Quadrant, a US- based investment firm, started using EAs in 1993 to manage its invest-

ments; at the time, US$5 billion was allocated across 17 countries around the world,claiming to have made substantial profits (Kieran, 1994).


364

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Part III

Market Developments andRisk Management


This chapter will present an enterprise risk management (ERM)framework for energy and commodity physical and financial portfo-lios based on the three usual building blocks of policies andgovernance, methodologies and metrics, and infrastructure.1 Theframework can be used to structure as well as conduct due diligenceon the soundness of the risk-management process for all materialrisks – such as market, credit, operational and liquidity risk – as wellas their interactions.This chapter is divided according to these blocks, with the first

section examining policies and governance, and the need to integraterisk management in the governance structure of the firm. We thendiscuss methodologies and metrics, particularly valuation, risk andperformance metrics for physical and derivatives portfolios, beforemoving on to infrastructure, and delving into people, data, opera-tions and systems.

POLICIES AND GOVERNANCEA “risk governance” framework integrates risk management into thegovernance structure of the firm to ensure that risk groups have theindependence, stature and adequate resources to fulfill their respon-sibilities within the overall business strategy of the firm.Over the years, many risk management groups that were believed

371

14

Enterprise Risk Management forEnergy and Commodity Physical and

Financial PortfoliosCarlos Blanco

NQuantX LLC and MTG Capital Management


to follow “best practices” have been repeatedly unable or unwillingto prevent their institutions from engaging in excessive risk taking,which eventually resulted in heavy losses that bankrupted thosefirms. Some of the key reasons for this are the assymetric compensa-tion structures at trading desks that encourage excessive short- termrisk taking and the lack of stature of the risk groups compared to therevenue generating units, as well as the willingness of managementteams and boards to turn a blind eye when profits are rolling in.Board and senior management teams have the responsibility to

manage the main risks of the firm. However, few board membershave a strong background in financial risk management and few riskmanagers have the breadth of skills and experience required tointeract directly with board members and understand or influencethe firm’s strategy and the process that sets the risk appetite andassociated boundaries.The large exposures accumulated in the real estate and credit

markets at large financial institutions such as Bear Stearns, LehmanBrothers, Merrill Lynch and AIG before the global financial crisis of2007–08 caught many boards, senior management teams and riskgroups by surprise. The governance structure in those firms ulti-mately failed to provide the oversight and early warning signals thatwould have prevented firms from taking on too much risk in certainareas.In other cases, even although risk managers informed senior

management about the magnitude of the risks taken and the poten-tial catastrophic consequences for their firms, senior- level executivesdecided to sugarcoat it for their boards, or omitted critical details.Lehman Brothers, AIG, Bear Stearns and BP are painful examples ofrisk groups’ lack of independence and inability to communicate thefirm’s risk all the way to board level. Another example is BP’s lack ofpreparedness and its incompetent response to the oil drilling plat-form explosion and subsequent oil spill in the Gulf of Mexico in 2008,which has become a case study on “crisis mismanagement”.Boards can also ensure that the risk management group roles and

responsibilities are alignedwith value creation (or at least the preven-tion of value “destruction”). Unless the risk management efforts arestructured in the right context, risk management activities will take asecondary role, and may ultimately end up destroying value andnegatively interferingwith the business strategy of the firm.


372


An example of best practices in the integration of risk manage-ment within the overall governance structure is the risk managementpolicy of BHP Billiton PLC, one of the largest diversified resourcescompanies in the world (see Panel 14.1).

ENTERPRISE RISK MANAGEMENT FOR ENERGY AND COMMODITY PORTFOLIOS

373

PANEL 14.1: BHP BILLITON RISK MANAGEMENT POLICY2

BHP Billiton’s risk management policy (see below) defines the group’sapproach to risk management, linkage to the corporate objective and inte-gration into its business processes.

“Risk is inherent in our business. The identification and management�of risk is central to delivering on the corporate objective.Risk will manifest itself in many forms and has the potential to impact�the health and safety, environment, community, reputation, regula-tory, operational, market and financial performance of the group and,thereby, the achievement of the corporate objective.By understanding and managing risk we provide greater certainty and�confidence for our shareholders, employees, customers andsuppliers, and for the communities in which we operate.Successful risk management can be a source of competitive�advantage.Risks faced by the Group shall be managed on an enterprise- wide�basis. The natural diversification in the Group’s portfolio ofcommodities, geographies, currencies, assets and liabilities is a keyelement in our risk management approach.We will use our risk management capabilities to maximise the value�from our assets, projects and other business opportunities and toassist us in encouraging enterprise and innovation.Risk management will be embedded into our critical business activi-�ties, functions and processes. Risk understanding and our tolerancefor risk will be key considerations in our decision- making.Risk issues will be identified, analysed and ranked in a consistent�manner. Common systems and methodologies will be used.Risk controls will be designed and implemented to reasonably assure�the achievement of our corporate objective.The effectiveness of these controls will be systematically reviewed�and, where necessary, improved.Risk management performance will be monitored, reviewed and�reported. Oversight of the effectiveness of our risk managementprocesses will provide assurance to executive management, theboard and shareholders.The effective management of risk is vital to the continued growth and�success of our Group.”

Source: BHP Billiton


Any investment and trading operation should clearly articulateand communicate the core investment strategies and the firm’s risktolerance. Written policies can establish the link between its businessstrategy and its tolerance for risk.A final component of the policy dimension is the existence of clear

lines of authority and an appropriate level of risk disclosure, whichmakes the risk transparent to internal and external stakeholders. Thedegree of authority and independence of the risk group is a functionof the relative stature of the group within the firm. For example, arisk group that reports directly to the heads of the business unit incharge of revenue generation is unlikely to have the independenceand authority to prevent excessive risk taking.It is important not to confuse detailed investment strategy and

position- level disclosures (often considered highly proprietary byportfolio managers) with risk disclosures designed to provide assur-ances that the risk levels are within the parameters expected byinvestors. For example, an investment manager that provides value- at- risk (VaR) disclosures to investors is not giving away proprietaryinformation that could be used by counterparties against the firm’sportfolio.

VALUATION AND RISK METHODOLOGIES AND METRICSThe second building block of the risk process consists of the method-ologies and metrics used to measure and manage risk, as well as theirintegration in risk- adjusted performance measures.Given the continued high volatility and extreme moves in the

energy and financial markets, one of the main contributions of therisk groups is the calculation of key risk metrics that can assist decision- makers with the process of identifying, measuring andmanaging the firm’s material risks.Since the first generation financial risk models were published in

the mid-1990s, there have been significant developments in themodelling of market, credit, liquidity and operational risk of energyand commodity portfolios. However, the excitement and quickprogress of the “early years” has now gone, and change tends to beslower and incremental and driven by regulatory and external pres-sures.


374


“At- risk” metrics: Cashflow at risk, VaR and earnings at riskBelow are listed the most commonly used market risk metrics forenergy and commodity portfolios.

VaR is a measure of the potential variability in the mark- to-�

market value of a portfolio for a given confidence level and timehorizon. It is particularly useful as a short- term risk metric fortrading firms.Cashflow at risk (CFaR) measures the potential variability of�

cash inflows and outflows at multiple time horizons as a result ofthe firm’s operation, as well as changes in the value of thehedging portfolio.Earnings at risk (EaR) measures the potential earnings vari-�

ability for multiple time horizons based on a set of earningsrecognition rules that determine in which reporting period thoseearnings fall.

Some of the key elements behind each metric and the key differencesare shown in Table 14.1.Due to the limitations of traditional risk models and metrics such

as VaR when applied to leveraged energy and commodity deriva-tives portfolios, most firms should use them with caution.For firms with portfolios with physical assets and contracts, CFaR


375

Table 14.1 Differences between VaR, CFaR and EaR

VaR Collateral at risk EaR(CaR) and CFaR

Market scenarios YES YES YESMark to market/mark to model YES YES YESMultiple time steps (periods) NO YES YESPortfolio ageing and walk- forward analysis NO YES YESNetting agreements NO YES YESCollateral and margin clauses NO YES YESPortfolio trading/hedging strategies NO YES YESCounterparty default NO YES YESHedge effectiveness rules NO NO YESRating downgrade NO YES YESOperational risks NO YES YESDynamic hedging strategy NO YES YESVolumetric risks NO YES YES

Source: NQuantX


calculations involve modelling costs and revenues related to theoperation of physical assets (for example, generation, storage), spotpurchases and sales in the spot market, as well as profit and lossesfrom the hedging and trading portfolio.The process for calculating CFaR and EaR requires careful

analysis and understanding of each portfolio’s material risks. Forexample, volume- related variability embedded in many physicaland derivative contracts and operations- related constraints – such as ramp- up and ramp- down rates and plant outages – need to beexplicitly accounted for to obtain a realistic value and risk estimates.The same is true for critical operating constraints from assets and thematerial clauses in contracts, as well as the limitations of physicaland financial arbitrage strategies such as market liquidity and avail-able hedging instruments. Figure 14.1 shows some of thecomponents required to compute CFaR and EaR.Failure to incorporate material risks when evaluating a hedging or

trading strategy such as CFaR and collateral implications may haveunintended consequences and expose the firm to unwanted risks.


376

Figure 14.1 Cashflow-at-risk models require the integration of multiple source of risk and portfolio components

Source: NQuantX LLC

MtM changes andvolumetric variability

(market risk)

Collateral changes(MtM-based, AR/AP,

premiums, downgrades)

CFaRandEaR

Counterparty losses(MtM-based, netting guarantees,

AR/AP, collateral)


When energy and commodity prices collapsed in the summer of2008, many firms experienced a large volume of collateral calls thatforced them to go to the capital markets to raise extra capital at a timewhen banks were not extending credit to their counterparties. Firmswithout contingency plans in place that had funding problemsended up having to pay exorbitant borrowing costs or were justunable to continue funding their hedges. For example, airlines thathad hedging programmes in place at the time accumulated large mark- to- market losses in their hedge portfolios when crude oil andjet fuel prices fell over 50% in just a few months in 2008. In addition,the global financial crisis caused a sharp drop in business travelworldwide that impacted their operating revenues. Rating agenciesdowngraded many airlines due to the worsening liquidity picturecaused by the combination of lower forecasted revenues and thelarge cash outflows due to collateral calls from their existing hedges.As a result, hedging costs increased considerably at a time whenfinancial institutions were attempting to reduce their credit riskexposures. The response by many airlines was to discontinue orreduce the size of their hedging programmes.Forward- looking key risk indicators such as CaR can measure the

maximum collateral outflows for a given confidence level, takinginto account initial and variation margin requirements for over- the- counter (OTC)-cleared and exchange- traded contracts, as well as thematerial margin clauses such as the credit support terms for OTCtransactions (eg, thresholds, independent amounts, downgrade trig-gers, eligible collateral).Figure 14.2 shows the potential future collateral outflows for a

derivatives portfolio, as well as the potential counterparty futureexposures as a function of simulated market environments. Bothmetrics in Figure 14.2 are calculated for a 95% confidence level andprovide an indication of the potential magnitude of unsecured creditexposures and potential margin payments.

Stress testsIn order to manage extreme event risk, the risk process should alsoinclude stress tests that question the model assumptions and also the“market consensus view” at any given moment. Stress tests areparticularly relevant in markets that experience large sudden fluctu-ations as well as regime changes, and the results from the analysis


377


COMMODITYINVESTING

AND

TRADING

378

Figure 14.2 Potential future exposure and potential collateral requirement report

Source: NQuantX LLC

–US$30,000,000

–US$20,000,000

–US$10,000,000

US$0

US$10,000,000

US$20,000,000

US$30,000,000

4/1/2010 5/1/2010 6/1/2010 7/1/2010 8/1/2010 9/1/2010 10/1/2010 11/1/2010 12/1/2010 1/1/2011 2/1/2011 3/1/2011 4/1/2011

Collateral walk forward 95% Collateral at risk profile 95% Potential future exposure

Potential future exposure at the95% confidence level

Walk forward – collateralrequirements

Potential collateral outflows at the95% confidence level

14 C

hapt

er C

IT_C

omm

odity

Inve

stin

g an

d T

radi

ng 2

6/09

/201

3 1

0:12

Pag

e 37

8

can provide key insights to portfolio managers to develop contin-gency plans.Stress test committees that have representatives from the main

groups in the firm – such as fund managers, risk managers andanalysts – can proactively identify scenarios that would prove usefulpreludes to market crisis and feed that information into strategicplanning, capital allocation, hedging and other major decisions.If stress test results (as illustrated in Figure 14.3) indicate that the

hedge fund’s losses are beyond their tolerance level or the availablecapital, then immediate instructions could be sent to the fundmanagers to reduce the exposure to such event or increase its capital.Another area where stress tests are critical is liquidity risk

management and capital adequacy. Liquidity risk management isoften mistaken for “crisis management”, as lack of planning oftenforces companies to address liquidity risk management issues only


379

Figure 14.3 Stress-test results for price and volatility changes broken down by desk

Source: NQuantX LLC

Agricultural

Crude oil and products

Power

-US$300,000

-US$250,000

-US$200,000

-US$150,000

-US$100,000

-US$50,000

US$0

US$50,000

US$100,000

Price

-50%

Price

-25%

Price

+25

%

Price

+50

%

Volatili

ty -2

0%

Volatili

ty +20

%

P&L

(thou

sand

s)

Agricultural

Gas

Metals

Power

Crude oil and products

Portfolio


when crises occur. In order to estimate liquidity risk, companiesneed a framework that explicitly addresses the potential demandand supply of cash. This framework should address medium- and longterm horizons as well as the cost of liquidating positions in astressed market environment.Risk managers can use scenario analysis and reverse stress tests to

identify scenarios that could result in liquidity shortfalls, particularlyunder stress market conditions. Funding liquidity risk measurementrequires metrics such as CFaR, CaR and margin- at- risk, which iden-tify potential margin and collateral calls in stress situations.The most sophisticated risk and stress test models incorporate

more realistic assumptions about market dynamics, particularly inproperly modelling the tails of the distribution, including a dynamicapproach to correlation to allow firms to incorporate credit andliquidity considerations, and finally a method to anticipate the risktaker’s response to various market events. A set of principles tomeasure and manage tail risk is presented in Panel 14.2.

BacktestingBacktesting a risk model consists of evaluating whether the riskmodel forecasts are adequately capturing the magnitude andfrequency of profit and losses (P&Ls). There is a wide range of quan-titative and qualitative backtests,3 but most individual tests have lowstatistical significance. As a result, the most common way to performbacktesting is by analysing a chart with P&L series and VaR fore-casts. The backtest procedure consists of comparing the daily VaRwith the subsequent P&L for T+1 as the VaR forecast attempts todetermine the magnitude of future P&L.The most common VaR backtests analyse whether the number

and magnitude of “exceptions” is within the VaR model predictions.Loss exceptions are those losses greater than the prior day VaR,while gain exceptions are gain larger than the prior day VaR on thepositive tail of the distribution (VaR+). Many firms exclusively focuson loss exceptions and ignore gain exceptions, but that may lead tosituations where large gains could go unexplained for long periodsof time and eventually turn into large unexpected losses.An additional test consists of checking whether those exceptions

are autocorrelated, which would result in many exceptions takingplace during short periods of time that potentially could result in


380


cumulative losses or gains considerably higher than the risk modelforecasts. As a general rule, any exceptions should be investigated bythe market risk management group, and the reasons for the excep-tion should be recorded to identify regular “culprits” and correctiveaction taken if necessary. If the backtests show that the risk models


381

PANEL 14.2: TAIL RISK MANAGEMENT PRINCIPLES

Risk management is ultimately the art of managing risk based on the pres-ence of imperfect (and constantly evolving) information. Measuring andmanaging risks in the tail of the distribution is both an art and a science. Afew basic risk management principles for extreme events will now beexamined.

Include all plausible scenarios of material risk factors in the analysisStress scenarios should integrate all material risk factors, such as marketrisks (eg, price, basis, volatility), counterparty risks, and also relevantfunding and market liquidity risks in a coherent fashion. Ignoring key risksmay result in risk information offering a simplistic and inaccurate view oftail risk that can give a false sense of security.

Choose appropriate tail risk metrics and modelling horizonsThe most common risk metrics used, such as VaR and standard deviation,fail to capture the dynamics of the tail of the distribution of potentialoutcomes. Fortunately, there are other metrics, such as stress test results,expected tail loss (ETL) and expected shortfall (ES), spectral risk measuresand probable maximum loss (PML), that can complement a risk limitstructure (see Chapter 2 of Dowd, 2005).

Measurement is just the starting pointAdequate preparation for any future crises requires forward- looking andcreative thinking, as well as carefully designed contingency plans.Designing and conducting realistic stress tests that provide insights intothe likely portfolio gains and losses under particular extreme events isnecessary but not sufficient. The development of contingency plans torespond to those hypothetical extreme events have lagged behind as mostfirms have been shown to be ill- prepared to respond to crises.

Expect the unexpected: Account for model riskWhile crises in financial, credit and energy markets often share some simi-larities with prior ones, each new crisis has differentiating elements thattend to catch most players by surprise. The lesson is that any contingencyplan against extreme events should leave a significant buffer to account forvariations from expected extreme scenarios.


ultimately failed to capture actual P&L variability, they should bereplaced as the primary market risk control tool.

Valuation models for physical assets, contracts and financialderivativesEnergy and commodity markets are dominated by asset- basedtraders that optimise their strategies around physical assets such asstorage facilities, pipelines and power plants. The traders and opera-tors of those assets attempt to maximise risk- adjusted profits basedon observable market spreads and the specific asset operatingconstraints. The valuation, risk metrics and hedging ratios calculatedfrom a static model are not just likely to be inaccurate, but could leadto suboptimal decisions that would impact the profitability.In order to capture the multiple risk dimensions involved in

hedging and trading, a dynamic simulation framework with threecritical components is needed: the ability to handle multiple riskfactors (eg, price risk, credit events, operational issues), multipleinstruments (eg, physical contracts, derivatives) and the ability tocapture events taking place at multiple steps in time.Dynamic risk simulation involves modelling the variability of one

or more metrics (eg, cash flow, earnings, mark- to- market, liquidity)based on a realistic evolution of a set of key state variables, as well asthe firm’s response to those changes (eg, operating, hedging andtrading strategies). The analysis consists on “leaping forward” intime by simulating risk variables at various point in time in thefuture and evaluating a series of value and risk metrics (eg, costs,revenues, profits, VaR) under each of those scenarios.An added benefit of using dynamic simulation- based risk tools is

the potential for risk management to play a larger role in strategicbusiness decisions at various levels of the firm. For example, simula-tion analysis can assist trading and operating groups in developingasset optimisation and hedging strategies based on the evaluation ofrisk–return trade- offs. It can also help finance groups creating forward- looking earnings projections by ensuring they are consis-tent with the risk appetite of the firm. Another critical area is theevaluation of important investment and divestment decisions from amarginal and standalone risk point of view.Advances in financial engineering and computational finance

such as least squares Monte Carlo and dynamic programming have


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allowed for the widespread use of dynamic risk simulation solutionsin energy firms.

Risk- adjusted performance measurementIdentifying, measuring, managing and pricing risk involvesdesigning appropriate policies and systems so different businessunits and risk- taking activities can be evaluated with a risk- adjustedreturn in mind. The practical requirements to implement suchchanges involve modifying performance measurement mechanismsto integrate the risk and return numbers in the bonus allocationprocess, as well as changing the risk systems, to allow for risknumbers calculations at the risk- taking unit level.Risk- adjusted return on capital (RAROC) measures, which are

widely used in the financial services industry, provide a commonmeasurement unit for risk- adjusted returns on allocated (ex ante) andutilised (ex post) risk capital.A RAROC system can assist managers to determine the most effi-

cient generators of revenue on a risk- adjusted basis, as well as set thethreshold returns given the risk assumed to generate them. Let usassume that we are the trading manager of a commodity tradingdesk and have two traders. Both of them made a profit of US$10million, but on average one of the traders used 80% less risk capitalthan the other. If the trading manager only considers the size of thegains, both should get a similar bonus. However, from a risk- adjusted perspective, the trader that took less risk should receive ahigher bonus.An investment evaluation process based on economic capital

considerations, where decisions are based on a risk- adjusted returnbasis, encourages corporate managers to become risk managersbecause they must take risk into consideration when allocatingresources internally and making investment and divestment deci-sions. Determining the economic capital allocated to each activity orbusiness unit provides senior management with a mechanism to linkrisk and return, and therefore provide a risk–reward signal that canbe used at different levels of the firm.

INFRASTRUCTUREThe third building block is the risk infrastructure. The infrastructuresubcomponents are: people, systems, data and operations.


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Risk management functions have too often been built aroundquantitative risk managers who lacked market experience and requi-site managerial skills. A chief risk officer (CRO) with the right skills,experience, independence and courage to perform the job is a criticalcomponent in the risk management organisation. The CRO’s effec-tiveness is a direct function of the power and independence grantedthat position, the quality of the people in the risk organisation, theassociated culture, and incentives and experience. If we were to boildown empowerment to one question, then we might ask an organi-sation if its CRO is empowered to be proactive, as opposed toremaining reactive.The budget of the overall risk management function determines

the scope and depth of activities that can be performed. The educa-tion and experience of the risk management personnel is a directreflection of an organisation’s ability to hire and retain first- class riskmanagers. The stature of risk managers is also important; if riskmanagement personnel do not have the stature to be able to stand upfor their beliefs under pressure, it is a recipe for eventual failure.Risk groups must balance the day- to- day “tactical” issues such as

risk measurement, reporting and limit checking with the morestrategic aspects of evaluating business decisions that have a mate-rial impact on the firm’s risk profile and whose effect may only be feltfurther into the future.

Risk management systems and dataFinancial risk management and technology advances have madepossible the integration of data from multiple sources in order toprovide the firmwide perspective required to forecast risk scenariosinvolving multiple risk dimensions.However, many energy trading and risk systems have failed to

keep up to date with the advances in risk analytics. Those systemsexcel at performing tasks such as scheduling, nomination oraccounting of physical and financial trades, but offer limited riskfunctionality and lag behind in their ability to perform sophisticatedrisk analysis.Many of the pioneering risk software firms that greatly

contributed to important methodological advances in energy riskmodelling are now part of larger software and consulting firms.Experience has painfully shown that their ability to innovate beyond


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pure technology and computational solutions has come to a nearhalt. For example, most valuation and risk models are still based onstatic portfolios over short time periods that ignore material riskssuch as volumetric and operational risks. In addition, most modelsused by risk practitioners still assume that market changes arelognormal, which fail to account for key characteristics of energy andcommodity prices such as mean reversion and jumps.5

One of the major gaps that exists in the risk management processat many firms is the lack of effective communication between riskgroups and the senior management team. A poorly informedmanagement exposes the organisation to risk “blind spots”.Some risk managers have taken a proactive role, and regularly

identify those key risk information gaps (see Table 14.3) and continu-ally develop and improve the tools to breach them.

SUMMARY AND CONCLUSIONSEnergy and commodity markets are some of the most volatilemarkets in the world, and firms operating in those markets shouldapproach risk management with caution.Firms that implement a rigorous enterprise risk management


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PANEL 14.3: RISK MANAGEMENT LESSONS FROM OSPRAIECAPITAL MANAGEMENT4

Ospraie Management LLC, one of the largest commodity trading funds inthe world, was forced to liquidate its largest fund after losing 38.6% in thefirst three quarters of 2008.

The investment firm was run by Dwight Anderson, an experiencedcommodities trader with an excellent trade record up to that point. In aletter to investors after closing the fund, Anderson wrote that, “I amextremely disappointed with this result and the fund’s sudden reversal inperformance. After nine years of striving to be a good steward of yourcapital, I am very sorry for this outcome.”

Just a few months before closing the fund because of the large losses,Anderson told Bloomberg “We do everything that we can to manage therisk, and I think we’re better at it today than we were a year ago.”

The sudden reversal in the fund’s performance caught most investors bysurprise. For example, in 2007 the head of Credit Suisse’s New York- basedalternative investments group, which managed US$134 billion in privateequity and hedge fund assets, said that “Anderson’s the best- in- classplayer in dealing in the world of basic industries and commodities”.


process can develop a competitive advantage that will allow them toweather the storm of adverse market conditions. Being aware of bestpractices and striving to implement them are therefore key not just tosuccess, but to having good prospects for longer- term survival.A risk management process is as effective as its weakest link, and


386

Organisationallevel

Risk management information gaps Reports and metrics

Board ofdirectors

Greater transparency on material risks andbetter understanding of high- level risk–return trade- offs of alternative hedgingalternatives.

Risk and hedging strategydashboards.

Seniormanagement

Knowledge of firmwide exposures andinteractions. Impact of hedging onshareholder value maximisation.Evaluation of risk–return trade- offs andoptimisation based on risk tolerance andmultiple constraints.

Firmwide exposure and “atrisk” reports.Hedge recommendationsStress-test reports.

CFO/Treasury Anticipate potential cashflow shortfalls anddevelop contingency plans.Evaluation of pre- and post- hedgeeffectiveness. Negotiation of key price andcollateral clauses in longterm contracts.Risk- adjusted pricing for large transactions.

CFaR; CaR; hedgeeffectiveness; EaR.

Procurement/logistics groups

Assistance with (re)negotiation of criticalcontract price and volume- related clauses.Increased focus on operational efficiencyaround physical procurement contracts.Benchmarks to determine groupperformance.

Cost- atrisk; operatingcashflow reports.

Market riskmanagers

In- depth understanding of multiple riskdimensions before and after hedging at theportfolio level (market, collateral, liquidity,cashflow…).

Dynamic risk simulation ofmaterial risks. Valuationand risk adjustments.

Credit riskmanagers

Dynamic counterparty risk assessments.Impact of netting and collateral clauses inOTC master agreements (netting andcollateral).Integration of accounts payable andreceivables, as well as potential collateralneeds in the cashflow managementprogramme.

Potential future exposureand credit risk reports. Riskcharges and risk- adjustedpricing.

Table 14.3 Main information gaps from a risk management perspective

Source: NQuantX LLC


therefore it is important to ensure that all the elements of the processare robust and integrated. The framework based on policies andgovernance, methodologies and infrastructure introduced in thischapter can assist energy and commodity market participants designa robust and comprehensive risk management process.

1 For more information, see Blanco and Mark (2004).2 BHP Billiton Risk Management Policy (see http://www.bhpbilliton.com/home/aboutus/ourcompany/Documents/Risk%20Management%20Policy.pdf).

3 There is an excellent overview of backtests in Dowd (2005).4 Burton, K., S. Kishan and C. Harper, 2008, “Ospraie to Close Flagship Hedge Fund After38% Loss”, Bloomberg, September 3.

5 For readers interested in a more detailed discussion of energy and commodity spot andforward price models, see Blanco and Pierce (2012).


387

REFERENCES

Aragonés, J. R., C. Blanco, K. Dowd and R. Mark, 2006, “Market Risk Measurement andManagement for Energy Firms”, in P. C. Fusaro (Ed), Professional Risk Managers’ Guide toEnergy and Environmental Markets (Wilmington, DE: PRMIA Publications): pp. 69–82.

Blanco, C. and M. Pierce, 2012, “Spot Price Process for Energy Risk Management”, EnergyRisk,March.

Blanco, C. and M. Pierce, 2012, “Multi- factor Forward Curve Models for Energy RiskManagement”, Energy Risk, April.

Blanco, C. and R. Mark, 2004, “ERM for Energy Trading Firms: ERM Starts with RiskLiteracy”, Commodities Now, September, pp 78–82.

Blanco, C., 2010, “Collateral, Cash Flow and Earnings at Risk”, WorldPower, IsherwoodPublications.

Blanco, C. and M. Pierce, 2010, “Integrated Risk Modeling for Trading and HedgingDecisions”, WorldPower, Isherwood Publications.

Dowd, K., 2005, Measuring Market Risk (2e) (Hoboken, NJ: Wiley).


Traditionally, counterparty and liquidity risks have been largelyignored in the valuation and risk measurement of energy andcommodity portfolios. The main reasons for this were the lack ofcommonly accepted methodologies to measure and price those risks,as well as the general perception that they were relatively immate-rial. However, large credit losses and funding liquidity problems,significant advances in credit risk measurement technology andchanges in accounting standards and regulations such as the Dodd–Frank Act have led to an increased focus on improving counterpartyand liquidity risk management practices.

At the centre of the credit revolution is the concept of credit valua-tion adjustment (CVA), which likely to play as great a role for creditrisk management as value- at- risk (VaR) did for the practice ofmarket risk management. In this chapter, we will examine theconcept of CVA and show how to calculate CVA at the trade andportfolio levels. We will also discuss the allocation of portfolio CVAand active credit risk management with CVA desks, as well as howto set up a system of credit risk charges.

CVA IN A NUTSHELLIn simple terms, CVA is the price of credit risk for a deal or portfoliowith a given counterparty. When two entities enter into a derivativetransaction, they also exchange an implicit option to default.

389

15

Credit Valuation Adjustment (CVA) forEnergy and Commodity Derivatives

Carlos Blanco and Michael PierceNQuantX LLC and MTG Capital Management; NQuantX LLC


From a valuation perspective, the fair value measurements ofderivative contracts should include a risk adjustment reflecting theamount market participants would demand because of the creditrisk in the future cashflows that will be exchanged through the life ofthe contract. The fair value of the embedded default option thatcould result in a credit loss is the CVA.

Calculating CVA requires estimating the difference between the mark- to- market discounting expected cashflows using the risk- freerate and the credit- adjusted mark- to- market, which consists of incor-porating the credit risk of the transaction into the mark- to- marketcalculations.

CVA = Mark- tomarket (risk- free) – Mark- tomarket (credit- adjusted)

CVA and debt valuation adjustmentCVA can be unilateral or bilateral, depending on whether the adjust-ments are based on one or two of the parties in the deal. In unilateraladjustments, the entity performing the CVA calculations onlydiscounts the derivatives assets (eg, in the money transactions) usingthe credit- adjusted curves of the counterparties, while no adjust-ments are made for liabilities (outof- the- money transactions). Inaddition, the entity performing the calculations could also perform asimilar adjustment for its liabilities, but using its own credit risk- adjusted curves. These adjustments are also known as the debtvaluation adjustment (DVA).

In bilateral adjustments, the entity performing the calculationstakes into account the effect of the counterparty’s credit risk in deter-mining the prices they would receive to transfer an asset, as well asthe effect of the entity’s credit risk in determining the prices theywould pay to settle that liability. The main differences between bilat-eral and unilateral CVA calculations is that the former integrates thecredit risk from the two parties in the transaction, and therefore iscalculated as the net difference between the unilateral CVA and theDVA.

Another risk adjustment is the funding valuation adjustment(FVA), which incorporates funding costs for the position – such asinitial and variation margin, and collateral.


390


Credit- adjusted rate curvesIn order to calculate CVA, one of the critical set of inputs is the credit- adjusted curves for the parties in the deal, which reflect the rates atwhich each counterparty is able to borrow money for different matu-rities in the capital markets.

There are several alternatives for creating credit- adjusted curves,such as using credit default swap (CDS) spreads, corporate bondyield spreads, and default probabilities from rating agencies, hybridmodels and internal rating systems. Table 15.1 provides a summaryof the alternative inputs.

CREDIT VALUATION ADJUSTMENT (CVA) FOR ENERGY AND COMMODITY DERIVATIVES

391

Figure 15.1 Steps in the CVA process

Source: NQuantX LLC

Calculatecredit riskadjustedcurves

DetermineCVA

method

CalculateCVA for eachcounterparty

AllocateportfolioCVA to

individualdeals

Figure 15.2 Encana CDS spreads for various maturities (September 2007–September 2012)

0

50

100

150

200

250

300

350

400

450

500

03/09

/2007

03/09

/2008

03/09

/2009

03/09

/2010

03/09

/2011

03/09

/2012

CD

S Sp

read

s (b

asis

poi

nts)

1Y 3Y 5Y 7Y 10Y


CVA methodsThere are several ways of calculating CVA, but we can group thevarious methods in three categories. The first is the discount rateadjustment, which requires the use of credit risk- adjusted discountcurves to calculate fair values. A common way of creating the credit risk- adjusted curves for a given counterparty is by adding the CDSspread to the risk- free rate curves used for present value calculations.Figure 15.3 shows the CDS spreads for various North Americanfirms, as well as the zero- coupon risk- free curve and the risk- adjusted curves. The risk- free rate curve is the US dollar zero couponcurve for September 3, 2012. The credit- adjusted curves for eachentity in Figure 15.3 are created by adding the CDS spread to the zero- coupon rate for each maturity.


392

Benefits Cons

CDS spreads Research shows CDS premiumchanges provide earlier warningsignals of credit risk problems.Very liquid markets for some of thelarger firms.

Empirical research points that CDSspreads tend to overestimateprobability of default (PD).Not all counterparties have tradedCDS.Changes in credit spreads driven by non- credit risk factors (eg, liquidity,risk premium).

Bond yieldspreads

Very liquid markets for somecounterparties.Credit spreads can be derived fromcorporate bond yields.

Less liquid than CDS.Yield spreads changes driven by non- credit risk factors.

Historicaldefaultprobabilities

More stable than market basedassessments.Readily available for mostcounterparties and sectors based onexternal rating.

Not marketbased.Slow to react to changingconditions.

Hybridmodels

More reactive than external ratings orhistorical probabilities.

Not directly based on credit marketassessments.Likely to follow changes in CDSand bond yields.

Internalratings

Incorporates information from differentsources.Consistent with internal creditassessments.

More subjective elements.Not necessarily marketbased.Slow to react to changingconditions.

Table 15.1 Main inputs used to build credit- adjusted rate curves

Source: NQuantX LLC


The discount rate adjustment is the most widely used method forfair value reporting due to its simplicity. One of the main shortcom-ings is that the credit exposure is assumed to be static, and thereforethe CVA is not dependent on the volatility of the mark- to- market(MtM) of the deal, making it more likely to underestimate the creditrisk of the instrument.

The second type is known as the exponential CDS default method,and requires the estimation of probabilities of default and recoveryrates. We can approximate the probability of default from quotedCDS spreads for a given term by applying the following formula:

where:

PD is the probability of default;�

CDS Spread is the credit spread in basis points;�

PD = 1! eCDSSpread1!R( )

!t years( )10000

!

"#

$

%&


393

Figure 15.3 Sample CDS spreads and credit risk-adjusted curves

Source: NQuantX LLC

0.000%

1.000%

2.000%

3.000%

4.000%

5.000%

6.000%

7.000%

8.000%

9.000%

0 2 4 6 8 10

Risk free Morgan Stanley Chesapeake Encana


T (years) is the maturity of the CDS measured in years; and�

R is the recovery rate (a common assumption made in published�

CDS spreads is that the recovery rate is 40%).

Once the probabilities of default and recovery rates are estimated,we can calculate the CVA for the deal based on the fair value usingthe risk- free rate multiplied by the probability of default and the lossgiven default (LGD), which is 1 less the recovery rate.

CVA = MtM(risk free) × PD × (1 – R)

Again, the credit exposure is assumed to be static with this method,so potential future exposures will tend to be underestimated.

The third method for calculating CVA is the exposure- basedapproach, which requires an estimation of expected exposures (bothpositive and negative) over the life of the deal, default probabilitiesand recovery rates at different time steps. Exposure- based methodscombine market and credit risk elements.

In exposure- based methods, in addition to the forward curves andother market variables, CVA is a function of the volatility of marketprices over time, the timing of cash inflows and outflows, the exis-tence of collateral and netting agreements, the term structures ofdefault probabilities, as well as the expected recovery values.Expected future positive and negative exposures can be calculatedusing closed- form solutions or simulation methods.

Following Stein (2012), CVA is calculated as:

where S(t) is the expected exposure of the deal at time t, and P(t) isthe default probability time function.

As an alternative to calculating the full integral, we can divide thetime interval [0,T] into periods [ti, ti+1] and select t–i ∈ [ti, ti+1].

A common choice for the time unit in each time interval is theaverage between the two points:

where

CVA = 1!R( ) S t( )P t( )dt0

T

!

CVA = 1!R( ) S t( )P t( )0

T

! dt " 1#R( ) S ti( )t=0

T

! P ti( )

P ti( ) = P t( )dtti

ti+1!


394


is the probability of default in the interval [ti, ti+1].If we simplify to just one period, CVA and DVA for a one- period

horizon can be calculated applying the following formula:

CVA = PDcpty × EPE × LGDcpty ≈ Spreadcpty × EPE

DVA = PDown × ENE × LGDown ≈ Spreadown × ENE

where PD is probability of default, EPE and ENE are the expectedpositive and negative exposures, respectively.

We can see the expected positive exposure (EPE) and the expectednegative exposure (ENE) profile for a set of nettable contracts with agiven counterparty in Figure 15.4. The EPE is used for CVA calcula-tions and represents the expected exposure in the event of default byour counterparty at different horizons. The ENE represents theexpected exposure in the event of our own firm defaulting.

Exposure- based methods are the more comprehensive ones, asthey overcome the shortcomings of assuming that the credit expo-sure is static. The three main methods are summarised in Table 15.2.


395

Figure 15.4 Expected positive exposure and expected negative exposure profiles

Source: NQuantX LLC

-$30,000,000

-$20,000,000

-$10,000,000

$0

$10,000,000

$20,000,000

$30,000,000

01/11

/2012

01/12

/2012

01/01

/2013

01/02

/2013

01/03

/2013

01/04

/2013

01/05

/2013

01/06

/2013

01/07

/2013

01/08

/2013

01/09

/2013

01/10

/2013

01/11

/2013

Expected positive exposure Expected negative exposure

Expected positive exposure (EPE)

Expected negative exposure (ENE)


CVA at the counterparty levelTo perform the credit adjustment to the value of a derivatives book,we need to calculate the CVA and DVA for each counterparty in thebook. The net adjustment is the difference between the sum of theCVA minus the sum of the DVA for all counterparties.

The calculation of the CVA and DVA for each counterparty is notsimply the sum of the individual trades CVA and DVA. This isbecause, in order to calculate CVA at a portfolio level, it is necessaryto take into account netting, collateral and other credit risk mitigants.

We can use any of the CVA methods to perform calculations at theportfolio level. The main difference is that, instead of just adding theindividual MtM and potential exposures of each instrument, weneed to perform those calculations after taking into account anycredit mitigants. For example, if we use the discount rate method, wecould calculate the net exposure for each counterparty after applyingnetting and collateral rules, and then calculate CVA by multiplyingthe net exposure times the credit spread.

For portfolio level calculations, it is common to perform simula-tion of risk exposures at the counterparty level for multiple scenariosafter taking netting and collateral into account. The steps involved incalculating portfolio CVA in a simulation framework are shown inFigure 15.5.

Although CVA calculations are based on EPE and ENE, credit riskcharges are often based on potential future exposures at a high confi-dence level. Figure 15.6 shows a potential future exposure (PFE)


396

CVA type Description

Discount rateadjustment (I)

After calculating the MtM, the value is discounted using thecredit spread to calculate the CVA.

Discount rateadjustment (II)

The MtM is calculated discounting each of the flows using the risk- adjusted curves of one or two of the parties in the deal.

Exponential CDSdefault method

Formula- based approach that introduces the CDS spread,survival rates and recovery rates.

Exposure- basedmethods

Involve the estimation of expected potential exposures overdifferent time horizons, probability of default and recoveryrates.

Table 15.2 Main CVA methods

Source: NQuantX LLC


CREDITVALUATIONADJUSTM

ENT(CVA) FO

RENERGYANDCOMMODITY

DERIVATIVES

397

Table 15.3 CVA report at the counterparty level

Counterparty MtM # trades Collateral Net exposure CVA % MtM

Citigroup US$5,034,352 US$45 US$2,500,000 US$2,534,352 US$74,375 1.48%BNP Paribas US$3,775,764 US$32 US$1,000,000 US$2,775,764 US$74,171 1.96%Goldman Sachs US$4,14,604 US$12 US$414,605 US$11,127 2.68%Glencore US$100,651 US$7 US$100,651 US$2,753 2.73%JP Morgan US$9,165 US$23 US$9,165 US$258 2.81%Shell US$6,873 US$11 US$6,874 US$158 2.30%

Source: NQuantX LLC

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report at the counterparty level with the PFE profiles for each coun-terparty.

From portfolio CVA to deal CVAWhen two entities enter into a series of transactions, they alsoexchange an implicit option to default. CVA is the price or cost ofcredit risk for a deal or portfolio with a given counterparty.

CVA can be calculated at the individual transaction or at eachcounterparty portfolio level. The overall CVA for the exposures witha given counterparty is not simply the sum of the individual dealCVA, because of the need to take into account credit risk mitigationrules that apply to those exposures. For example, a trading entitymay have several deals with the same counterparty that have large standalone CVAs, but if those exposures offset each other and thereare netting agreements in place, the overall portfolio CVA will beconsiderably lower than the sum of the individual CVAs.

Despite the non- additive nature of portfolio CVA, it is possible to


398

Figure 15.5 Steps to calculate CVA in a simulation framework

Source: NQuantX LLC

1. Generate spot and forward curve scenarios for multiple time steps

2. Value each deals for each scenario time step

3. Apply netting rules at the counterparty level for each scenario time step

4. Calculate net exposure for each scenario time step

5. Repeat process for multiple scenarios

6. Calculate exposure profile metrics (EPE, PFE, …)

7. Calculate credit valuation adjustment at the counterparty level


CREDITVALUATIONADJUSTM

ENT(CVA) FO

RENERGYANDCOMMODITY

DERIVATIVES

399

Figure 15.6 Potential future exposure (PFE) at the counterparty level

Source: NQuantX LLC

US$–

US$2,000,000

US$4,000,000

US$6,000,000

US$8,000,000

US$10,000,000

US$12,000,000

US$14,000,000

US$16,000,000

1M 3M 6M 12M 2Y 3Y

Citigroup

BNP Paribas

Goldman Sachs

Glencore

JP Morgan

Shell

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allocate it among the various portfolio constituents in a similarfashion than we can calculate marginal VaR for individual portfoliocomponents to determine the contribution to diversified VaR of agiven trade or strategy. The allocation of the portfolio CVA into eachindividual trade is important for individual hedge accounting desig-nations, as well as improving the understanding of the marginalimpact of individual trades on the overall CVA.

The process to allocate portfolio CVA into individual componentsconsists of determining the marginal contribution of each trade to theportfolio CVA. The marginal contribution of a given trade could bepositive, negative or neutral, depending on the change in the port-folio CVA before and after including that trade.

CVA allocation approachesThere are various approaches to allocating CVA among individualportfolio constituents. It is important to understand the differencesbetween them because the choice of allocation method may havematerial implications on how credit risk adjustments of assets andliabilities are reported, as well as hedge effectiveness tests and quali-tative derivatives disclosures.

The most common CVA allocation methods are:1

In- exchange or full credit: the standalone CVA for each deriva-�

tive instrument is directly applied. There is no need to calculateportfolio CVA or apply any allocation methodologies.Relative fair value: the portfolio CVA is allocated to each deriva-�

tive instrument according to the sign and magnitude of the fairvalue of each derivative asset and liability.Relative credit adjustment: the portfolio CVA is allocated to each�

derivative instrument according to the sign and magnitude ofthe standalone CVA for each derivative asset and liability.Marginal contribution: the portfolio CVA is allocated to each�

derivative instrument based on the sign and magnitude of themarginal contribution to CVA of each individual derivative assetand liability.

A numerical example of the application of the relative creditadjustment method is shown in Tables 15.4, 15.5 and 15.6; note thatthe application of this method in particular is investigated because


400


the relative credit adjustment method is relatively easy to imple-ment, and superior to the in- exchange or full credit, and the relativefair value methods.

The first step involves calculating the standalone CVA for eachindividual trade. If we add the standalone CVAs at the trade level,we can calculate the “undiversified” portfolio CVA assuming thatthe portfolio consists of non- nettable, non- collateralised trades, effec-tively ignoring credit- mitigation effects. The magnitude and sign ofeach standalone CVA determines the relative weights for the dealsthat are part of that counterparty’s portfolio. Table 15.5 shows the deal- level breakdown of MtM and CVA of the portfolio with tradesmade with EDF Trading as the counterparty. The bottom row showsthe portfolio MtM, the “undiversified” CVA as a sum of stand- aloneCVA, as well as the credit- adjusted MtM. The relative percentageweights are calculated by dividing each individual CVA by the undi-versified portfolio CVA.


401

Table 15.4 Stand- alone CVA report

Counterparty BP Trading

Mark-to- Stand-alone Relative Credit-adjusted Stand-alone CVA market CVA weights MtM

WTI swap US$1,243,550 US$10,724 109.63% US$1,232,826 Brent swap US$672,946 US$5,114 52.27% US$667,832 Natural gas swap US$(831,503) US$(6,363) –65.04% US$(825,141)Natural gas basis swap US$97,690 US$302 3.08% US$97,388

Totals US$1,182,682 US$9,777 100% US$1,172,905 Portfolio Undiversified Credit-adjusted MtM CVA MtM

Source: NQuantX LLC

Table 15.5 Portfolio- level CVA

Portfolio Netting ALL

Collateral held/(posted) US$1,000,000

Counterparty Mark-to-market Net exposure Portfolio CVA Credit Adjusted MtM

BP Trading US$1,182,682 US$182,682 US$3,654 US$1,179,029

Source: NQuantX LLC


The next step consists of calculating the portfolio CVA based onthe unsecured exposure with each counterparty, which is estimatedafter taking into account any relevant netting and credit- mitigationtools. Portfolio CVA calculations can be made using current orexpected exposure methods for the various trades in the portfolio.The combined MtM exposure, the net exposure and the portfolioCVA are shown in Table 15.5. In our example, as the current expo-sure is largely collateralised, the portfolio CVA is substantially lowerthan the sum of the standalone CVAs. The last column shows thecombined credit- adjusted MtM for the exposures with BP Trading.

As a final step, we can calculate the portfolio CVA portion thatwill be allocated to each individual deal by multiplying the relativeweights calculated in Table 15.4 times the portfolio CVA. This isshown in Table 15.6.

A comparative analysis of the different CVA allocation method-ologies is shown in Table 15.7. The relative credit adjustment and themarginal contribution approaches are the most accurate ones, butrequire the calculation of new metrics such as standalone andmarginal CVAs. Under those approaches, the CVA of a portfolio oftrades with a given counterparty is allocated among its individualconstituents according to the relative weight of each individual standalone or marginal CVA.

The state of the practice of CVA reporting by energy trading firmsis still in its infancy. Several publicly traded entities still do not calcu-late portfolio CVA and most do not allocate portfolio CVA into


402

Trade # Mark-to-market CVAweights

Marginal CVA Credit-adjustedMtM

WTI swap US$1,243,550 109.63% US$4,005 US$1,239,545

Brent swap US$672,946 52.27% US$1,910 US$671,036

Natural gas swap US$(831,503) –65.04% US$(2,376) US$(829,127)

Natural gas basisswap

US$97,690 3.08% US$113 US$97,577

Portfolio MtM(risk free)

Portfolio CVAbilateral

Portfolio credit-adjusted MtM

US$1,182,682 100.00% US$3,652 US$1,179,031

Table 15.6 Allocation of portfolio CVA to individual trades

Source: NQuantX LLC


individual deals. The main reason given is that the allocation ofcredit risk would not materially impact the fair value or the determi-nation of the hedge effectiveness of a hedging relationship.

Credit limits, charges and CVA desksTraditionally, credit risk management at energy and commoditytrading firms has been a passive exercise, where credit risk managersset and monitor exposure limits and traders can continue dealingwith a given counterparty as long as those limits are not breached.

Those limits are often defined based on the current exposure at thecounterparty level, which is generally calculated by taking the mark- to- market value of open positions, adding account receivables andaccounts payable, and open settlements and applying appropriatenetting rules and credit risk mitigation tools. In addition, the limitsmay be scaled as a function of the evolution of the creditworthinessof the counterparty. A sample credit report is shown in Table 15.8with limits based on current exposures as a function of the counter-party’s internal rating.

One of the main problems of this “binary” approach based on


403

Approach Advantages Limitations

In- exchange orfull credit

Simplicity of approach. Noneed to perform portfolioCVA calculations.

Netting and collateral agreementsnot taken into account.Overestimates credit risk at theindividual instrument level.

Relative fairvalue

Simple to implement. Onlyrequires ability to calculateCVA at the counterpartyportfolio level.

Allocation weights based on fairvalue may differ considerablyfrom those based on stand- aloneor marginal CVA.

Relative creditadjustment

Relatively simple toimplement. Allocation basedon actual CVA of eachinstrument.

Stand- alone CVA may notrepresent marginal contribution,but often a good proxy.

Marginalcontribution

Technically, the mostaccurate method. Allocationbased on marginal impact onportfolio CVA of eachderivative instrument.

Marginal CVA contributions maybe volatile. Requires ability tocalculate marginal CVA.

Source: NQuantX LLC

Table 15.7 Portfolio CVA allocation methodologies


limits is that it creates a situation where traders become the frontlinecredit risk managers for the firm while their goals become that ofmaximising profitability, not managing that risk. As a result, the firmmay end up with excessive credit risk concentrations to a givenindustry, geographical region or tenor due to the combined traderexposures. These concentrations could be exacerbated in the future ifthe exposures are driven by the same market risk factors.

To complement static current exposure limits, many credit riskdepartments have also added, as part of the limit structure, forward- looking exposure and risk metrics such as maximum PFE at a givenconfidence interval. Those metrics can alert credit risk managers ofthe exposures that may grow beyond the maximum thresholds as aresult of market fluctuations. In the last column of Table 15.8, we cansee that the existing exposures with Barclays, Morgan Stanley, J.Aron and Southern Company may exceed the credit limits in thefuture, even although they are within the approved current exposurelimits.

The next level in sophistication of the credit risk function involvesdesigning a system of credit risk charges that can ensure that risktakers become actively involved in the risk management process. Asystem of credit risk charges and reserves can assist the front office insetting the right price for each new transaction based on the risksincurred by the firm, and also to create a fund with reserves toprotect the firm against future credit losses.

There are two main types of counterparty risk charge systems. Thefirst is based on setting upfront charges for each new deal as a func-tion of the potential credit losses over the life of the deal using CVA


404

Table 15.8 Sample credit risk limit report (US$ 000s)

Counterparty Internal Current Limit Limit Potential PFE/rating exposure usage future limit

exposure

Barclays 3 US$2,092 US$5,000 42% US$7,540 151%Morgan Stanley 4 US$4,021 US$3,000 134% US$5,650 188%J Aron & Co 3 US$(461) US$5,000 0% US$12,350 247%Shell 2 US$(18) US$2,000 0% US$325 16%BP 3 US$1,682 US$5,000 34% US$4,500 90%Exxon 1 US$1,729 US$10,000 17% US$3,450 35%Southern Company 5 US$714 US$1,000 71% US$1,250 125%


or PFE metrics.2 Traders often strongly oppose these types ofsystems due to the fact that charges for the full life of the deal areapplied upfront, and if the deal is reversed before maturity thatwould result in overcharges.

The other type is known as pay- as- you- go, and consists ofapplying a daily credit risk charge based on current unsecured creditexposures.3 Pay- asyou-go systems are relatively easy to implementas the only metrics required are the unsecured credit exposureagainst each counterparty and the daily cost of capital of thatcounterparty.

Pay- asyou- go systems are conceptually appealing for manytrading organisations, but the success of the implementation of thecredit risk charge system is often dependent on the details. Forexample, if traders are charged on the standalone credit risk of eachdeal, they are likely to be overcharged on an overall basis and there-fore be at a competitive disadvantage over other firms.

The best systems strike the right balance in terms of taking intoaccount diversification benefits at the counterparty portfolio leveland also impact the behaviour of the risk takers directly. Although pay- as- you- go systems apply daily charges, the credit risk managerscan provide traders with the distribution of their maximum potentialfuture credit charges before they conduct a new deal group using forward- looking probability- based credit metrics such as PFE.


405

Table 15.9 Sample trader- level allocation of pay- as- you- go charges for a singlecounterparty

Counterparty Current Credit spread Credit Dailyexposure (bp) spread charge

Credit Suisse 2,091,546 245 2.45% US$140.39

Trader MtM Charge Daily allocation charge

M. Smith US$1,235,000 36.5% US$51.31 J. Arnold US$(212,456) 0.0% US$–C. White US$324,500 9.6% US$13.48 M. Ford US$750,700 22.2% US$31.19 S. Chance US$1,069,002 31.6% US$44.41 Totals US$3,379,202 100.0% US$140.39

Source: NQuantX LLC


Active risk management with CVA desksAs a response to the increased magnitude and complexity of thecredit risk dimension in derivatives trading, many firms havecreated internal groups, known as CVA desks, with a mandate tomeasure, price and manage counterparty risks. CVA desks act asspecialised units that manage the credit risk of the consolidatedexposures with each major counterparty.

Some CVA desks are in charge of implementing the system ofcredit charges and reserves for the firm. A sample decision flowdiagram is shown in Figure 15.7. When a trader wants to conduct anew deal, the CVA desk determines the cost to protect the credit riskof that new deal and communicates it to the trader. The credit chargecan be calculated based on the size, liquidity and duration of theexposure, the credit risk mitigation tools in place (netting, collateral,guarantees, etc), as well as the creditworthiness of the counterparty.If the trader decides to go ahead with the transaction, it may pass thefees to the counterparties by pricing the deal taking into account thecost of protection.

The CVA desk can also play an active role by encouraging tradersto use collateral to minimise credit exposures, and also to trade withcertain counterparties that reduce overall portfolio exposures. Inorder to do so, they need to have the ability to calculate credit risk on


406

Figure 15.7 Active credit risk management with CVA desks

Source: NQuantX LLC

Trader approaches CVA desk before conducting new deal

CVA desk calculates fee for credit risk protection

Trader pays CVA premium

CVA collects fee andprotects credit

exposureCVA purchasesprotection for

unsecured exposuresfrom trading position

TraderTrader

CreditmarketsCredit

markets

CVAdeskCVAdesk


an aggregate portfolio from multiple potential trades, as well as todetermine credit charges, pricing, hedging and reserves from thosehypothetical trades. In certain instances, the CVA desk could poten-tially allow certain deals to be priced at a discount if they reduced thecounterparty risk from a portfolio perspective.

In some financial institutions, the CVA desk is structured as aprofit centre whose traders attempt to take active credit bets. Formost energy and commodity firms, however, the starting goal of theCVA desk is likely to be the active management of credit exposureswith an emphasis on credit risk hedging and loss minimisation.

CONCLUSIONCounterparty and liquidity risk management in energy andcommodity portfolios is undergoing a revolution driven by efforts toprice and hedge those risks. CVA is gradually becoming an integralpart of the risk management process of energy and commoditytrading firms by helping to adjust valuations, and also in settingcredit and liquidity risk charges.

There are various methods to calculate CVA, from relativelysimple discounting of cashflows using credit risk- adjusted curves tothe more complex simulation- based potential exposures coupledwith default and recovery rates.

Valuation and risk measurement models and systems that fail toincorporate counterparty and liquidity risk adjustments are ignoringa material risk that could impact the accuracy of fair value and P&Lcalculations, as well as the validity of risk metrics used throughoutthe firm.

The implementation of CVA at the valuation and risk measure-ment level introduces other risks, such as potentially higher P&Lvolatility and increased model risk arising from potential CVA esti-mation errors, but the benefits often outweigh the risks. Firms withthe ability to price and manage credit risk proactively will have acompetitive advantage over those that continue to manage thoserisks in a passive and reactive fashion.

1 For a more detailed explanation of the different methods, we recommend:PricewaterhouseCoopers, 2008, “Consideration of Credit Risk in Fair Value Measurements:An Addendum to PwC’s Guide to Fair Value Measurements”.

2 The first part of this chapter covers the main CVA calculation methodologies.3 For more details, see Humphreys and Shimko (2005).


407



408

REFERENCES

Blanco, C., 2010, “Collateral, Cash Flow & Earnings at Risk: Time to update your riskmetrics and policies?”, Commodities Now, July.

Blanco, C., 2010, “Credit Valuation Adjustment for Commodity Derivatives”, CommoditiesNow, December.

Blanco, C., K. Dowd, R. Mark and W. Murdoch, 2006, “Credit Risk Measurement andManagement for Energy Firms”, in P. C. Fusaro (Ed), Professional Risk Managers’ Guide toEnergy and Environmental Markets (New York, NY: McGraw- Hill): pp 69–82.

Humphreys, B. and D. Shimko, 2005, “Pay As You Go”, Energy Risk, January.

Stein, H., 2012, “Counterparty Risk, CVA, and Basel III”, Columbia University FinancialEngineering Practitioners Seminar, March.


This chapter will review the development of China’s futures marketsince the early 1990s, with corresponding analysis of futures tradingvolume. It will discuss the two phases of clean- up and rectificationnecessitated by the development of China’s futures markets and theestablishment of its regulatory regime in the 1990s. In this context, itanalyses the trading volumes of China’s futures market between2000 and 2011, particularly trading characteristics in 2011. Thisanalysis will help to explain why the trading volume of futures andoptions rose worldwide but dropped in China during the sameperiod, as well as the reason why China’s futures market has latterlybeen experiencing greater success. The last part of this chapter willoffer some predictions based on the huge potential for furthering thefutures market in China. These predictions include a trend towardsloosening product control, incorporating exchanges and the furtheropening up of the futures market in China. Finally, this chapter willconclude that a futures market with a stable trading volume willconform to the principle of “making progress while ensuringstability”. Such progress will be aided by research on the insurancefunction of futures options, grasping the developing direction ofoptions early on and accelerating the innovation of futures productsand new futures business.

409

16

The Past, Present and Future ofChina’s Futures Market: Trading

Volume AnalysisWang Xueqin

Zhengzhou Commodity Exchange


THE PASTIn 1990, with the approval of the State Council, the Zhengzhou GrainWholesale Market1 was founded as the first futures pilot programmein China. Since then, China’s futures market has continued todevelop, moving from “chaos” to “governance” – including twophases of clean- up and rectifications in the 1990s, as well as stan-dardisation development in the 2000s. The futures market has grownsteadily in scale, with laws and regulations increasingly imple-mented, market functions well- performed and internationalinfluence becoming even more significant. This has been especiallytrue since 2004, with more futures contracts being listed and traded,forming an almost complete commodity futures market systemconsisting of agricultural, metal, energy and chemical products.

The first 10 years (1990–2000)In the 1990s, along with the establishment of a market- basedeconomic policy, China gradually opened its free commoditymarket, resulting in frequent fluctuations in commodity prices. Thegovernment began to develop a wholesale market of food, metals,materials and other commodities, and futures trading of these prod-ucts was gradually promoted. For a time, the commodity wholesalemarkets and futures exchanges flourished everywhere in China.From 1990 to 1993, the number of futures exchanges increasedrapidly, while futures contracts became multiply listed and themarkets were over- speculated. There were then more than 50 futuresexchanges in China, leading to vicious competition between theexchanges. Disorderly market trading, market manipulation andinsider trading also occurred frequently during that time.2 Over athousand futures commission companies were founded, althoughtheir operation and management verged on the chaotic. Customerswere often cheated and the interests of investors violated. Thebrokerage services for overseas futures trading developed too fast tobe brought under regulation in a timely manner. The futures marketentered a stage of blind development.

The first phase of clean- up and rectificationOn November 4, 1993, the State Council of the People’s Republic ofChina issued the “Notice to Resolutely Stop the Blind Developmentof the Futures Markets”, and started the first clean- up and rectifica-


410


THEPA

ST, PRESEN

TAND

FUTU

REOFCHIN

A’SFU

TURES

MARKET: TR

ADIN

GVOLU

MEANALY

SIS

411

Figure 16.1 Estimated Chinese trading volume (US$ trillion US$ equivalents) and number of listed futures

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1993 1994

1995 1996

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Num

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futu

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Trading turnover

Listed Contracts (right axis)

16 Chapter C

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age 411

tion in the futures market. The key measures adopted includedreducing the number of futures exchanges and futures products, aswell as suspending bulk trading for several commodities that wereclosely related to the people’s livelihood but the price of which werestill subject to policy control – such as steel, sugar, coal, petroleumand vegetable oil. On May 18, 1995, the Treasury bond futurescontract was also suspended. By the end of the first clean- up andrectification, only 14 exchanges were approved to remain in busi-ness, while just 35 futures contracts were approved, divided intoformally listed contracts and trial- based contracts. Figure 16.1 showsthe number of listed futures and total futures trading notionalturnover by year.Therefore, the main measures taken in the first clean- up and recti-

fication were:

reduce the amount of futures exchanges and futures contracts;�

suspend bulk trading for several commodities that were closely�

related to the people’s livelihood and the prices and were stillsubject to policy control, such as steel, sugar, coal, petroleum andvegetable oil;suspend trading for Treasury futures on May 18, 1995;�

suspend approval for futures companies, review futures compa-�

nies and implement a licence system;strictly control overseas futures trading;�

strictly control the participation of state- owned enterprises and�

institutions, and financial institutions in futures trading; andestablish a centralised and unified regulatory regime.�

The second phase of clean- up and rectificationAfter the first phase of clean- up and rectification, there were still toomany futures exchanges and futures brokerage firms with problem-atic operations, and a few institutions and individuals weremanipulating the market to make exorbitant profits. There werenumerous incidences of illegal futures trading in overseas markets.The supervisory administration had weak supervision powers andretrograde supervisory methods.In August 1998, the State Council issued the “Circular on Further

Rectification and Regulation of Futures Market”, in which the princi-ples of continuing the pilot project, strengthening regulation,


412


standardising according to law and preventing risk were determinedfor the second clean- up and rectification in China’s futures market.The main measures included the further rectification, cancellationand consolidation of futures exchanges. Three futures exchanges

THE PAST, PRESENT AND FUTURE OF CHINA’S FUTURES MARKET: TRADING VOLUME ANALYSIS

413

Table 16.1 Exchanges, brokerage firms, listed commodities and turnover in Chinesefutures

Year Futures Brokerage Listed Trading Trading exchanges firms commodities turnover volume

contracts (in US$100 million)* (contracts)

1990 1 - - - -1991 2+ - - - -1992 5+ - - - -1993 32+ ≈1000** 52+** 443.18 4,453,4501994 14 144 35 2536.23 60,553,6001995 14 330 35 8071.05 318,060,3501996 14 329 35 6751.14 171,283,8501997 14 294 35 4909.36 79,381,6001998 14 278 35 2966.87 52,227,8501999 3 213 12 1793.18 36,819,5502000 3 178 12 1290.71 27,305,3502001 3 184**** 12 2419.34 60,231,7502002 3 187 11*** 3169.35 69,716,3162003 3 176 11 8698.96 139,932,1112004 3 172 11 11792.56 152,848,8002005 3 166 11 10790.40 161,423,7612006 3 164 14 16857.65 224,737,0512007 3 165 18 32883.02 364,213,39708 3 163 19 57716.05 681,943,55109 3 164 23 104743.76 1,078,714,90910 4 163 24 248087.05 1,566,764,67211 4 161 27 220727.81 1,054,088,66412 4 161 31 274675.97 1,450,462,383

Source: Compiled from data in CSRC, 2001, China Securities and Futures Statistical Book,apart from the number of exchanges in 1993 being taken from 100 Questions and Answers ofFutures Operation (China Material Publisher): p.138 (while the number of exchanges is put at32 in this report, it was generally recognised that there were over 40 exchanges operating in1993; the number of exchanges in 1990, 1991 and 1992, and the number of brokerage firmsin 1993, are the author’s estimates).*Trading turnover in Chinese yuan (Yn) was converted to US dollars at an exchange rate of6.23 Yn/US$.**The 1993–2001 listed commodities contracts data and the number of 1993–2000 brokeragefirms was taken from Xueqin and Gorham (2002).***2002–12 listed commodities contracts dates came from:http://www.cfachina.org/news.php?classid=108.****The 2001 brokerage firms number was taken from:http://www.yafco.com/show.php?contentid=40670, while the 2002–12 brokerage firmsnumber came from: “China Futures Industry Development Report” and the CSRC.


were retained, in Shanghai (Shanghai Futures Exchange, SHFE),Zhengzhou (Zhengzhou Commodity Exchange, ZCE) and Dalian(Dalian Commodity Exchange, DCE), and the exchange manage-ment system was upgraded. In order to fully perform the functionsof price discovery and hedging for futures markets and furthercontain excessive speculation, the listed futures contracts werereduced from 35 to 12, with 23 futures contracts delisted. The 12commodities remaining were copper, aluminium, soybeans, wheat,soybean meal, mung bean, natural rubber, plywood, long- shapedrice, beer barley, red bean and peanuts.The measures for the second phase also included the level of

minimum registered capital for futures brokerage firms beingincreased to US$4.82 million. In 1998, after the increase of registeredcapital, there were about 180 futures brokerage firms remaining innormal operation. In addition, the China Securities RegulatoryCommittee (CSRC) was created and “Interim Regulations onAdministration of Futures Trading” implemented.3

Determine the regulatory regimeAfter the clean- up and rectification, CSRC was appointed as thecentralised and unified regulatory commission for China’s futuresmarket, a regulatory change that created steady and well- functioning futures markets. In addition, China’s futures marketwithstood the impact of the financial crisis of 2007–08; due to theinfluence of the crisis, price volatility increased, especially incommodity futures. The global financial crisis also impacted uponthe growth rate on China’s futures market. In 2009, this plunged to20.72% from 87.24% in 2008, down 66.52%, showing that thecentralised and unified regulation had been successful and deservedhigh credit, and also that it provides useful experience for the devel-opment and supervision of other commodity futures and financialderivatives in China.On November 24, 1998, the CSRC approved six modified contracts

for soybean, wheat, mung bean,4 copper, aluminium and naturalrubber, and on November 27, it approved modified contracts forwheat and mung bean, to be re- listed and traded again. Theseapprovals marked a turning point in China’s futures markets in thelatter stages of its first decade. The second significant event was theimplementation of “Interim Regulations on Administration of


414


Futures Trading”,5 and the third was the founding of the ChinaFutures Association on December 28, 2000.6

The second 10 years (2000–10)In the first ten years of 21st century, China’s futures market devel-oped rapidly. The most significant events included new futurescontracts listed for trading, futures companies gradually opening upand financial futures being launched successfully.


415

Figure 16.2 Regulations on administration of futures trading

Shanghai FuturesExchange

Dalian CommodityExchange

ZhengzhouCommodity Exchange

China FinancialFutures Exchange

China Futures Associationwas founded in 2000

CSRC

36 CSRC were distributed to various places in 1998

China Futures MarginMonitoring Centre in 2008

Figure 16.3 Chinese yearly futures trading volumes (millions of contracts)

0

200

400

600

800

1000

1200

1400

1600

1993 1995 1997 1999 2001 2003 2005 2007 2009 2011

Trading volume (m

illions of contracts)


New futures contracts promotedBy the end of 2011, there were 31 futures contracts listed in fourfutures exchanges in China, covering agricultural, industrial, energyand financial products. Futures contracts for Treasury bonds, crudeoil, coking coal and even potato and egg were also being considered.


416

Table 16.2 Futures contracts list for trading on China futures exchanges

Exchanges Contracts Time Trading volume Trading volumein (2011) (2012)

Shanghai Aluminium 1992 9,953,918 3,942,680Futures Copper March 1993 48,961,130 57,284,835Exchange Natural rubber November 1993 104,286,39 75,176,266

Fuel oil August 25, 2004 1,971,141 9,132Zinc March 26, 2007 53,663,483 21,100,924Gold January 9, 2008 7,221,758 5,916,745Deformed steel bar March 27, 2009 81,884,789 180,562,480Wired rod March 27, 2009 3,242 2,717Lead March 24, 2011 293,280 68,646Silver May 10, 2012 – 21,264,954Volume 308,239,140 365,329,379

Zhengzhou Excellent strong glutenCommodity wheat March 28, 2003 7,909,755 25,802,102Exchange No. 1 cotton June 1, 2004 139,044,152 21,016,438

Sugar January 6, 2006 128,193,356 148,278,025PTA (pure terephthalic acid) December 18, 2006 120,528,824 121,245,610Rape oil June 8, 2007 4,320,115 6,248,568Regular white wheat March 24, 2008 152,901 6,262

(hardwhitewheat)

Early long- grain nonglutinous rice April 20, 2009 5,925,454 3,838,320

Methanol October 28, 2011 316,107 3,797,412Glass December 3, 2012 - 16,136,920Rape seed December 28, 2012 - 137,084Rape meal December 28, 2012 - 421,207Volume 406,390,664 347,028,203

Dalian Commodity Soybean meal July 17, 2000 50,170,334 325,876,653Exchange No. 1 yellow soybean March 15, 2002 25,239,532 45,475,425

Corn September 22, 2004 26,849,738 37,824,356No. 2 yellow soybean December 22, 2004 10,662 10,400Soybean oil January 9, 2006 58,012,550 68,858,554LLDPE (linear low- density

polyethylene) July 31, 2007 95,219,058 71,871,537Palm oil October 29, 2007 22,593,961 43,310,013PVC (polyvinylchloride) May 25, 2009 9,438,431 6,900,153Coking coal April 15, 2011 1,512,734 32,915,885Volume 289,047,000 633,042,976

China Financial Shanghai–Shenzhen 300Futures Exchange stock index Apr. 16, 2010 50,411,860 105,061,825

Volume 50,411,860 105,061,825

Futures tradingvolume in China Total 1,054,088,664 1,450,462,383


In addition, steam coal futures contracts at the ZhengzhouCommodity Exchange, coking coal and iron ore futures contracts atthe Dalian Commodity Exchange, crude oil futures contracts at theShanghai Futures Exchange and debt futures contracts at the ChinaFinancial Futures Exchange were prepared and subject to regulatoryapproval (see Table 16.2).

Futures companies gradually opened upChinese brokerage companies also became qualified for overseasfutures business. At the end of June 2001, the CSRC officiallypublished “Administrative Method for Overseas Futures HedgingBusiness of State- owned Enterprises”, a list of state- owned enter-prises that were allowed to participate in overseas futures hedgingbusiness. By end-2005, 31 enterprises obtained the qualification ofoverseas futures business.

Foreign capital participated shares into Chinese futures companiesBy 2013, there were only three Sino–foreign joint venture futurescompanies in China: Galaxy Futures, CITIC Newedge and JPMorganFutures. On December 2, 2005, ABN of Royal Bank of Scotland wasapproved to set up the first joint venture futures company withGalaxy Futures in China. The foreign party, ABN Financial FuturesAsia Co Ltd, held 40% of the shares, while on the Chinese side,Galaxy Securities held the remaining 60% of shares. CITIC Newedgewas formerly known as CITIC Futures – in January 2007, overseasstrategic partner Newedge Group was launched, founded by SociétéGénérale and Banque de L’lndochine with a 50–50% ownership ratio.The futures business of the group was carried out by Banque deL’lndochine and the Pegasus Group. On September 26, 2007,Guangzhou Zhongshan Futures was renamed as JPMorgan Futures,with JPMorgan holding about 49% of shares indirectly.7

Chinese FCMs started to set up branches in Hong KongThe China Securities Regulatory Commission approved six Chinesefuture commission merchants (FCMs) to set up branches in HongKong in March 2006, namely China International Futures, Yong’anFutures, GF Futures, Nanhua Futures, Jinrui Futures and GreenFutures.


417


Financial futures successfully launchedChina Financial Futures Exchange (CFFEX), approved by the StateCouncil and the CSRC, was set up by five shareholders: ShanghaiFutures Exchange, Zhengzhou Commodity Exchange, DalianCommodity Exchange, Shanghai Securities Exchange and ShenzhenSecurities Exchange, with each of the shareholders contributingUS$16.05 million. The CFFEX was founded on September 8, 2006, inShanghai. On April 16, 2010, CSI300 stock index futures started to belisted and traded on CFFEX.

Trading volume of China’s futures market (2000–10)Since the early 2000s, the trading volume of Chinese futures hasincreased exponentially. The annual average growth rate of China’sfutures market between 2000 and 2010 was 43.16%.In 2001, the trading volume of Chinese futures stood at 60.23


418

Figure 16.4 Growth in trading (log scale) for China’s futures market

0.01

0.1

1

10

100

1993 1994

1995 1996

1997 1998

1999 2000

2001 2002

2003 2004

2005 2006

2007 2008

2009 2010

2011 2012

Trad

e vo

lum

e (U

S$ tr

illio

n, U

S$ e

quiv

alen

t)

Table 16.3 Latest trading volume of stock index futures in China financialfutures exchange

Year 2010 2011 2012 Total

CSI 300 91,746,590 50,411,860 105,061,825 247,220,275


million contracts. The trading volume of global futures and optionswas 4.4 billion contracts, and trading volume of global futures wasabout 1.8 billion. The trading volume of global commodity futuresand options was about 400 million, and the volume of globalcommodity futures was about 380 million contracts.A decade later, the trading volume of Chinese futures had grown

to 1.52 billion lots and global futures was about 11.2 billion. Thegrowth in size of China’s markets as a percentage of the total globalmarket for all futures and for commodity futures is shown in Figure16.5.Global futures volumes in China’s futures markets increased from

3.34% in 2001 to 13.6% in 2010. For the first half of that period, themarket share of China was no more than 5%. Over the period 2001–10, the ratio for futures and options increased from 1.37% to 6.82%,illustrating the slower growth of options trading relative to futurestrading in China. During the 11th five- year plan, the market share ofChina’s futures market to global futures and options marketincreased exponentially.For commodity futures, the ratio of China’s increased from 15.99%

of the global volume to 53.65% in 2010. When including optionsvolumes, China’s market increased from 14.61% to 50.95% of globalfutures and options volume in 2010.To summarise, between 2001 and 2010 the rapid increase in

trading volume made China’s futures market the largest commodity


419

Figure 16.5 China markets as a percentage of global trading volume

0

10

20

30

40

50

60

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Commodity futures

Commodity futures and options

Futures

Futures and options


market in the world. On the other hand, the average annual share ofthe Chinese futures market share to global futures and optionsremained low, reflecting that the trading volume of China’s futuresmarket in commodity options, stock futures and stock options arenot yet fully developed, with some products being totally absentfrom China’s derivatives market. In 2010, China’s futures marketwas one of the largest commodity futures market in the world,accounting for a trading volume of 53.65% of the global commodityfutures market.

THE PRESENTThe micro characteristics of the market operationThe healthy development of the Chinese futures market has beenmainly based on the powerful spot market and an upward macro-economic environment. China’s consumption volume of nearly allthe commodities underlying the futures contracts ranks among theglobal top three consuming nations (see Table 16.4).

Open interestAs research shows,8 legal persons9 hold 40–60% of the 10 futuresvarieties’ open interest,10 offering a reasonable market structure in


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Figure 16.6 The historical ranking of Chinese futures exchanges among all global exchanges

0

5

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15

20

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35 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

ZCE DCE SFE CFFEX


open interest.11 The percentages for aluminium, soybean No. 1, palmoil, hard wheat and rapeseed oil are high above 60%, which is over- high. The percentages for gold, rebar, rubber, LLDPE, soybean No. 2,coke, sugar, early long- grain nonglutinous rice and stock indexfuture are below 40%, which is relatively low.Individuals are the main trading participants, usually more than

half of total trading volumes. The volume percentage12 of individ-uals for soybean No. 2 and rubber is over 80%; for gold, rebar,LLDPE, coke, sugar, early long- grain nonglutinous rice, stock index,zinc, PVC, strong gluten wheat, cotton and PTA is 50–80%; foraluminium, lead, fuel oil, copper, soybean N0.1, soybean meal,soybean oil, corn, palm oil, hard wheat and rapeseed oil is below50%.

Price volatility between futures and spotThe assessment shows that the price volatility between futures andspot is almost at the same level, except for the price of strong glutenwheat, hard white wheat and early long- grain nonglutinous ricecontracts, whose volatilities are slightly higher than the actual pricevolatility. Research data reveal that price correlation between futuresand spot on the majority of products in China’s futures markets arestable and functional.


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Table 16.4 China’s international consumption status of its futures underlyingcommodities

China positioned Futures underlying commodities Numberin the world

The biggest consumer Copper, aluminium, rebar, wire, rubber, PTA,PVC, cotton, soybean N0.1, soybean meal,soybean oil, strong gluten wheat, hard wheat,rapeseed oil, palm oil, early long- grain nonglutinous rice, lead, coke, methanol 20

The second biggest Gold, silver, LLDPE, sugar, corn 5consumer

The third biggest Fuel oil, soybean No. 2 2consumer

The second biggest Stock index futures 1stock market


Declared hedging positionsResearch shows that commercial enterprises mainly took short posi-tions to hedge, based on the application for hedging quota, the use ofthat hedging quota and the hedging of open interest. Based on theactual situation in China, upstream enterprises often have a largeproduction capacity and therefore exhibit great enthusiasm forfutures market participation, while the downstream enterprises aremainly small businesses, usually lacking hedging abilities and will-ingness. This results in a large amount of short hedging. In thefutures market, the manufacturers naturally hold short positions,which creates a downward pressure on futures price. As the counter-party, speculators generally tend to hold long positions, hoping toprofit when the futures prices increase. Manufacturers pay apremium to manage the risks coming from price fluctuations.

Price changes and price hit limitsGenerally, the futures market prices ran smoothly in 2011, with thehitting of the upper or lower price limits three times in a rowdecreasing year- on- year.13 However, the futures prices of copper,rubber, LLDPE, PVC, coke and palm oil experienced rather frequentfluctuations, with each hitting the price limit more than 25 times.LLDPE hit the price limit three times in a row on 16 occasions.

Short- term trading activity in the marketThe short- term trading of futures was active, with intraday tradinggenerally over 50% of total trading volume. In 2011, the three vari-eties that generated the largest proportion of day trading volumewere rubber, cotton and stock index futures. The intraday tradingactivity for these contracts accounted for over 80% of the total. Thethree varieties with the lowest proportion of intraday trading weresoybean No. 2, corn and hard wheat, all of which had an intra- daytrading proportion below 50%.

ANALYSIS OF TRADING CHARACTERISTICS IN 2011The trading volume of futures and options rose worldwide butdropped in ChinaGlobal futures and options market trading volume increasedStatistics show14 that the global futures trading volume increased by7.4% from 12.049 billion transactions in 2010 to 12.945 billion transac-


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tions in 2011, and that global options trading volume increased by15.9% from 10.375 billion transactions in 2010 to 12.027 billion trans-actions in 2011. In 2011, the global futures and options tradingvolume was 24.972 billion contracts, an increase of 11.4% from 22.425billion transactions in 2010.

Futures trading volume in China decreasedAccording to the overall analysis from CFA data, in 2011 the tradingvolume of the China’s commodity and financial futures was 1.054billion transactions, a decrease of 32.72% from 1.567 billion transac-tions in 2010. China’s proportion of commodity and financial futurestrading volume in the world decreased from 6.82% in 2010 to 4.22%in 2011. China’s commodity futures exchanges accounted for 53.65%and 38.03% of the world’s futures trading volume for 2010 and 2011,respectively.It is worth noting that, despite an 11.36% growth in the global

futures and options markets trading volume in 2011, the globalcommodity futures and options trading volume fell by 6%.Agricultural commodity futures trading volume dropped by 26%and non- precious metal futures fell by 33%. Although the nationalfutures trading volume declined by more than 30%, China’s financialfutures trading volume grew in 2011. CFFEX’s trading volume was50.41 million transactions in 2011, an increase of nearly 10 % over2010.

China’s commodity markets still world’s largestIn FIA’s “Annual Volume Survey”, ZCE ranked 11th in 2011 (12thfor 2010), SHFE ranked 14th in 2011 (11th for 2010) and DCE ranked15th (13th for 2010). In 2011, the trading volume of ZCE decreasedonly by 18%, while DCE’s decreased by 28% and SHFE by 50%.Among the 32 countries for which FIA collected data, there are

commodity futures markets in 25 of those countries/regions. Thereare 45 commodity derivatives exchanges, with total trading volumereaching 2.64 billion transactions. Despite a decline of 34.01%between 2010 and 2011, the trading volume of China’s commodityfutures in 2011 amounted to 1.003 billion contracts, accounting for38.03% of the world volume and ranking first place globally. Figure16.7 shows the proportion of total commodity future trading volumeby country.


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Of the global top 20 agricultural contracts by volume in 2011, ninewere in China. ZCE cotton futures ranked first and sugar rankedsecond. The other seven contracts were from DCE (soybean oilranked fifth, soybean meal ranked sixth, corn ranked ninth, soybeanN0.1 ranked 10th and palm oil ranked 14th), SHFE (rubber rankedthird) and ZCE (strong gluten wheat ranked 20th). Moreover,SHFE’s rebar, zinc and copper futures ranked first, fifth and seventh,respectively, among global top listing on metal derivatives.

Why the trading volume of futures and options rose worldwidebut dropped in ChinaThe increase of the trading volume of futures and options globallyBelow are some of the reasons behind the increase in tradingvolumes.

Most of global futures exchanges experienced an increasing�

trading volume: among the 77 exchanges around the world, thetrading volume of 59 exchanges grew (accounting for 77%), 14exchanges grew by 50% (accounting for 18%) and 11 exchangesgrew by 100% (accounting for 14%).Several exchanges experienced a rapid or steady increase in�

trading volume: in 2011, the Singapore Mercantile Exchange, C2Options Exchange in the US, BATS Exchange and AustralianStock Exchange enjoyed the highest growth rate, India Joint


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Figure 16.7 Proportion of commodity futures (2011)

Source: Based on FIA data

Proportion of commodity futures (2011)

d on FIA data

China 38%

US 28%

UK 15%

India 15%

Japan, Russia,others 1%

respectively


Stock Exchange achieved a growth rate of 181%, ThailandFutures Exchange saw a growth rate of 136% andIntercontinentalExchange (ICE) futures trading volume hit arecord high.Precious metals futures contracts enjoyed a significant increase�

in trading volume globally.

In 2011, the gold futures industry trades amounted to 0.179 billioncontracts globally, increasing by 65.97% from 2010. Worldwide, threederivatives exchanges introduced four new gold futures contracts.The silver futures contracts traded nearly 144 million globally, anincrease of 172.57% from 2010. Four derivatives exchanges launchedfour new silver futures contracts around the world. 15

The decline in China’s futures trading volumeIn 2011, although China’s futures market made significant achieve-ment in improving the market structure, market function and futuresproduct innovation, the trading volume of the market suffered amarked decline after five years’ rapid growth.16 There were threereasons for this. First, the turmoil of the global economy, the struc-tural transformation the domestic economy was facing and theslowdown of the economic growth rate adversely impacted thefutures market. Second, factors such as the global loose-monetaryenvironment and the speculation of some commodities boosted theprice and volume of the futures market. Third, since the fourthquarter of 2010 many measures have been taken to curb excessivespeculation, such as suspending the trading fee preferential system,restricting the size of opening positions and raising the marginrequirement.

FUTURES COMPANIES AND TRADING VOLUMESFutures companiesAt the end of 2011, the margin held by the futures industry wasnearly US$25.59 billion. The various reserves that can be used to dealwith market risks was US$1.74 billion, out of which the risk reserveof futures brokerage companies was US$304.98 million, the riskreserve of future exchanges was US$1.09 billion and investor protec-tion funds was US$345.10 million, which enhanced the industry’sability to control risk. However, compared to other financial


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institutions, futures brokerage companies’ overall strength was stillquite low. At end-2011, there were 161 futures brokerage companiesin China, and their total net capital was US$5.28 billion, onlyUS$32.10 million for each company on average. The net capital of thetop 10 futures brokerage companies only accounted for 28% of theindustry total, 32% for clients’ margin and 28% for fee income.Futures brokerage companies’ income source was mainly brokeragefee income and interest income from customers’ margin deposits. In2011, the revenue of 161 futures brokerage companies was US$2.46billion, out of which brokerage fees were US$1.60 billion and interestincome from customers’ margin was US$513.64 million. The annualnet profit was US$369.18 million, and some futures companies evensuffered a loss (data from CSRC).17

2012 trading volumesIn 2012, the total volume of futures market in China was 1.45 billioncontracts, with a notional value of approximately US$27.47 trillion,increasing by 37.60% and 24.44% from 2011, respectively.The trading volume of Shanghai Futures Exchange was 0.365

billion contracts, with a notional value of approximately US$7.12 tril-lion, increasing by 18.52% and 2.63% from 2011, respectively, andwith a market share of 25.19% and 26.06%, respectively.The trading volume of Zhengzhou Commodity Exchange was

0.347 billion contracts, with a notional value of approximatelyUS$2.79 trillion, decreasing by 14.61% and 48.04% from 2011, respec-tively, and with a market share of 23.93% and 10.15%, respectively.The trading volume of Dalian Commodity Exchange was 0.633

billion contracts, with a notional value of approximately US$5.35 tril-lion, increasing by 119.01% and 97.45% from 2011, respectively, andwith a market share of 43.64% and 19.47%, respectively.The tradingvolumeofChinaFinancial FuturesExchangewas0.105

billioncontracts,withanotionalvalueofapproximatelyUS$12.17 tril-lion, increasing by 108.41% and 73.29% from 2011, respectively, andwith amarket share of 7.24%and 44.32%, respectively.

THE FUTUREThe development of China’s futures marketThe year 2012 witnessed the 22nd anniversary of the establishmentand development of China’s futures market. Since the early 1990s,


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China’s futures market has grown to become one of largestcommodity futures exchanges in the world on the basis of tradingvolume. In 2009, the total trading volume of China’s commodityaccounted for 43% of global volume, the biggest commodity futuresmarket globally.There are three reasons for the prosperity of China’s futures

market. First, China’s futures market enjoys great support fromgovernmental policy. Since 1988, the establishment of a sound andsteady futures market has been included and referred to in thegovernment’s working report six times. Since 2004, requirementshave been raised in the No. 1 central document18 on the futuresmarket that futures derivatives for commodities should be trans-formed to a risk management instrument used by the real economy.Second, the regulatory agency has strengthened the supervision ofthe futures market to control risk. The regulatory agency alwaysgives top priority to strengthening the infrastructure of the futuresmarket. In addition, a good environment has been created for thedevelopment of the futures market through continuously reinforcingthe basic regulations and institutions, steadily promoting new prod-ucts, enhancing frontline supervision and strictly preventing marketrisk.Third, China’s futures market is generally managed well, not only

attracting a number of commercial enterprises to hedge, but also


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Figure 16.8 China’s futures transactions and GDP

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Contracts in m

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increasing the integration of the futures market, cash market andindustrial entities. Also, the key reason is that the futures marketkeeps up with China’s rapid economic development and soaringcommodity cash market.

Huge development space of China’s futures marketAlthough the trading volume of China’s futures market tops anyother, some development- restricting factors cannot be ignored. Forexample, the futures market is faced with an environment of exces-sive regulatory restrictions, community monopoly,19 OTC disguisedfutures,20 over- speculation and the problem of a lack of creativity indevelopment, competitiveness in exchange and low level of diversityof futures varieties. While the domestic futures market is active, theinternational pricing power is still out of China’s control.Additionally, the monotonous operating system, improper regula-tory system and single futures agent business21 have held back thedevelopment of the futures market. It should be admitted thatChina’s futures market has a long way to go. For instance, cash settle-ment of commodities has not been implemented and cross- productarbitrage trading, transactions after closing (post- market sessiontrading), margin netting, SPAN, exchanges of futures for swaps andoptions trading have not been instigated.It is not yet possible to determine the steps necessary to achieve

these innovative projects or how long it will take and how difficult itwill be. To improve the international status of China’s financialsector, a strong futures market is needed for support. To improveChina’s international competitiveness, the price discovery functionof futures markets should be given full play; to cope with the impactfrom the international financial crisis, the hedging function of thefutures market should be further promoted; to improve the nationaleconomy growth, service level of the futures markets should beimproved. Therefore, China enjoys a huge development opportunityin the future.

Possible changes to China’s futures marketIn spite of the high trading volume, the operating quality of China’sfutures market is not completely satisfactory. Since the financialcrisis of 2007–08, the global financial environment has changedconstantly, and the futures market has been confronted by great


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uncertainty, offering both risks and opportunities. However, sincethe early 1990s, China’s futures market has gone through stages ofblind development, rectification and regulatory change, and come toa critical period of development, namely a development processfrom quantitative change to qualitative change.Undoubtedly, through this process some essential changes will

occur in China’s futures market. The first is that the futures marketwill operate within the rule of the law, although there is still nonational futures law in China. Second, with the increasing number offutures varieties, the coverage of futures products will be wider, andfutures markets should tightly track the actual underlying markets.In addition, a speculative component will be more suitable for theoperation of the market. Third, there will be an increasing number ofinnovations in the market- operating mechanism. The investor struc-ture will be optimised and the proportion of institutional investorswill be raised. Fourth, with the improvement of internationalcompetitiveness, the pricing power of the futures market will bestrengthened. Finally, new ways should be constantly opened up toprevent market risks.

THREE MAJOR DEVELOPING TRENDS OF CHINA’S FUTURESMARKETThe trend to loosen product- listing controlIn order to curb the excessive amount of different types of futuresthat were listed by the exchanges in the early days of the futuresmarket in the 1990s, an approval mechanism for the listed futuresvarieties has been implemented. This prevents premature futuresvarieties from listing, prevents the futures market from deviatingfrom the real economy, ensures the consistence of the futures andcash market, controls market risk and standardises the market order.However, under the approval mechanism, despite strong efforts forpromoting new products by the regulators and exchanges, a newproduct often has a slow introduction process and may miss the besttime to market owing due to cumbersome procedures.22 This indi-cates that the inefficient administrative approval mechanism fails tokeep pace with the needs of the market and may hinder the pricingfunction of the futures market. Thus, futures markets do not alwaysprovide the risk management tools needed by all enterprises. Inaddition, in the majority of futures exchanges around the world,


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nearly all varieties of agricultural futures have correspondingfutures options trading to allow flexible hedging of the agriculturalmarket risks. However, options trading has not appeared uniformlyin China’s futures markets, preventing domestic investors fromtaking a more flexible approach to avoid market risks and alsorestricting enterprises from participating in hedging. Therefore, inthe future, China’s futures market should relax product control andgradually allow more listing of new products.

Corporatisation of the exchangesChina’s futures exchanges are structured uniquely, and their owner-ship is ambiguous and confusing, even to professionals in China.23

Although, in economic terms, according to the “Regulations onAdministration of Futures Trading”, 24 exchanges are seen as corpo-rate legal persons or other economic organisations, in reality, interms of management, the institutional arrangements stipulated bythe “Futures Exchange Management Regulations”25 implies verystrong administrative factors. Therefore, the exchanges that act asadministrative organisations should be institutional legal persons.On the other hand, organisationally, judging by the source of theircapital and their stated aims,26 exchanges that have features of socialcommunity should be seen as social group legal persons (legalpersons are classified under several different categories in China).Therefore, exchange systems should come under a special organisa-tional model.27 In the author’s opinion, since the futures market is market- oriented, complex and has high risk, it should be dynami-cally regulated for specialisation and legalisation, and even theregulatory system itself should be market- oriented. The trend of thecorporatisation28 of exchanges around the world proves that corpo-ratisation does not contradict the unified regulation of the futuresmarket by the government. Therefore, with the further opening- upof China’s futures market as a part of its regulatory system,exchanges should be equipped with a more advanced independentmarket development force and much stronger competitiveness. As aresult, corporatised exchanges will become a development trend ofChina’s futures market.


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The further opening up of the futures marketSince 2008 (and especially since 2010), the internationalisation of theChina’s futures market has been a hot topic in the domestic futuresindustry. A consensus has been reached, that it is the basic strategy ofChina’s economic development to insist in opening up to allowactive participation in the international market. Since the futuresmarket directly serves the national economy, its internationalisationis both an internal need for the development of China’s economy andan irresistible development trend. There are two reasons toexplaining this. On one hand, our enterprises’ competing for thepricing power of commodities requires the international futurescompanies to provide intermediary services, and the prices of inter-national commodities are based on the prices of internationalcommodity futures prices. Only by actively participating in the samemarkets with the leading international futures companies and grad-ually expanding its influence, can China’s enterprises overcome thedifficulty of gaining pricing power. On the other hand, international-isation can enhance the competitiveness of the futures companies. Inthe process of internationalisation, in addition to domestic brokeragebusiness, futures companies can expand their businesses to foreignbusiness, as well as business that combines domestic and foreignoperations, which can improve operations and competitiveness. Aspredicted, a series of opening- up measures, including product cross- listing, stock holding of foreign investors and international productlistings will become one of the main developing trends of China’sfutures market.

SUGGESTIONSStable trading allows for progress while ensuring stabilityInfluenced by factors such as state macro- control and complicatedmarket conditions, there has been a large fluctuation in the tradingvolume of China’s futures market, especially in 2008. China’s futurestrading volume increased to 87.24% as a result of the internationalfinancial crisis, before the rate decreased again by 20.72% in 2009. In2010, it rocketed up again to 84.74%, but then sharply declined to30.69% negative growth in 2011. On the contrary, since the early2000s the proportion of US futures and options trading volume in theworld increased by more than 30%. Since the financial crisis, thetrading volume of the US market has gone up steadily. In 2011, its


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futures and options trading volume was 8.119 billion contracts,14.01% more than the 7.121 billion contracts of 2012, occupying32.51% of the total world shares, a small increase on the 31.94% of2010. Thus, compared with the futures market of the US and otherdeveloped countries, the stability of China’s futures trading volumeshould further improve. Therefore, in a situation where futurestrading volume might decline, the relation between trading volumeand upgrading the quality of the markets should be handledcorrectly and the scale of the futures market should be kept relativelystable, both of which are not inconsistent with the principle of“making progress while ensuring stability” of the Central EconomicWorking Conference.29 That is to say, it would promote the transfor-mation of China’s futures market from quantity expansion to qualityexpansion.

The insurance function and development of optionsInternational experience shows that, since the early 2000s, the globaloptions market has developed quickly. Statistics show that thetrading volume of options outpaced futures, apart from in 2010 and2011. However, as the data of different classes of futures shows, thetrading volume of stock index option was several times that of stockindex futures, but commodity options trading volume was only one- tenth of the futures volume.30


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Figure 16.9 Global options volume as percentage of futures

Source: http://www.futuresindustry.org

0%

2%

4%

6%

8%

10%

12%

Global commodities Global energy Global agricultural Global metals


As shown in Figure 16.9, in 2011 global energy options transac-tions accounted for 11% of its futures trading volume, globalagricultural options trading was 7.88% of the agricultural futuresand global non- precious metals and precious metals options tradingvolume accounted for only 2.8% of the futures trading volume.It can be seen that the original intention and purpose of listing

commodity options was mainly to serve as an insurance function forfutures and to enhance the quality of the futures market, not toincrease trading volume. For this reason, it is recommended that afurther study on the insurance function of options for futures and aproject on options training including simulated options trading isneeded.

CONCLUSIONSFor the optimal development of China’s futures market, thefollowing should be carried out:

further research and development of the crude oil futures;�

optimise the design of contract and rules of Treasury bond�

futures, upgrade the technological system and carry out simula-tion trading and training on market for the investors;actively promote the listing of coking coal, iron ore, steam coal�

and asphalt, as well as egg, potato lumber and Japonica ricefutures, etc;increase the research and development of new trading instru-�

ments and strategic futures, such as stock index options,commodity futures options, commodity index, iron ore andcarbon emissions;further the pilot project of the overseas futures brokerage busi-�

ness of futures companies;support the market- oriented M&A of futures companies and�

exchanges; andmotivate futures companies to carry out new business�

innovation.

After 30 years of reform and opening up, China’s economic scale inGDP has reached a rank of second in the world, while thecommodity futures trading ranked first in the world after more than20 years development. As expected, China’s economy will continuealong the path of macro- control development, and maintaining


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overall operations, despite the growth rate having slowed. Underthese conditions, the development of the real economy needs a rapiddevelopment of China’s futures market through greater specialisa-tion, diversification and internationalisation. It offers the futuresmarkets a historic opportunity, but also some serious challenges.China’s futures market needs not only a stable and functioningmarket operation, but also much more innovation and opening up tothe world. This is the common aspiration for people in the industryand is also the objective requirement for economic development inChina. The Chinese market has a great deal of room to develop, withmore new products to be listed, more market innovation and to bemore involved in the world’s derivatives business.Finally, the question remains whether it is possible that these

markets will open to foreign traders. There is no schedule for foreigninvestors to start trading inChina’s commoditymarkets, but that timeis getting closer every day. Regulators are takingmuchmore interestin allowing foreign investors to trade in domestic markets, asresearched and provided by futures exchanges. Regulators are evenguiding exchanges to research some of the programmes aimed atattracting foreign investors. In 2012, China’s regulators implementeda series ofmeasures to speed up the opening of the capital markets tothe outside world, including the qualified foreign institutionalinvestor (QFII) and renminbi qualified foreign institutional investor(RQFII) systems. In 2012, 72 QFIIs were approved. At the time ofwriting in 2013, a total of 213 qualifiedQFIIs had gained an approvedinvestment quota amounting to US$39.985 billion. At the same time,inorder to further expandopening to the capitalmarket, inSeptember2012 theShanghai andShenzhenstockexchanges, aswell as theChinafinancial futures exchange, held severalQFII promotional activities intheUS,Canada, Europe,MiddleEast, Japan andSouthKorea to intro-duce foreign investors to China’s capital market. Furthermore, theShanghai futuresexchange’s crudeoil futuresareexpected tobe intro-duced in 2013, with the aim of attractingmore international investorsto participate in the financial and capitalmarket inChina.

1 Zhengzhou Grain Wholesale Market is the predecessor of Zhengzhou CommodityExchange. It is located on the south bank of the Yellow River, and is the first experimentalfutures market in mainland China. Henan is a large agricultural province, whose output ofgrain, cotton and oil rank among the top in China. It is an important base of quality agricul-tural products.


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2 Natural rubber R708, China Commodity Futures Exchange, Inc of Hainan (CCFE) in 1997;Coffee F605, F607, F609, F703, China Commodity Futures Exchange, Inc of Hainan (CCFE)in 1996-1997; Jun Plywood 9607, Shanghai Futures Exchange (SHFE) in 1996; Red bean,Suzhou Commodity Exchange in Jan-Mar 1996; Soybean meal 9601, 9607, 9708 GuangdongUnited Futures Exchange (GUFE) in Oct-Nov 1995; Sticky rice 9511, Guangdong UnitedFutures Exchange (GUFE) in Oct 1995; Red bean 507, Tianjin Commodity Exchange in May-Jun 1995; 3-year T-bond 314, 327, Shanghai Securities Exchange (SHSE) in 1995; Palm OilM506, China Commodity Futures Exchange, Inc of Hainan (CCFE) Mar 1995; Corn C511,Dalian Commodity Exchange (DCE)Futures in 1995; Steel wire, Suzhou CommodityExchange in 1994-1995; Japonica rice, Shanghai Food and Oil Exchange in Jul-Oct 1994.

3 Including a serial of documents, such as “Provisional Regulations on Futures Trading”,“Futures Exchange Management Regulations”, ”Futures Brokerage Corporate ManagementApproach”, “Futures Brokerage Company’s Senior Management Personnel QualificationsManagement Approach” and “Futures Industry Practitioners Qualified ManagementMethods”.

4 In the early 1990s, the Zhengzhou Commodity Exchange listed mung bean futures, the firstbatch of domestic listed contracts. In 1998 and 1999, for two consecutive years it traded at apeak of more than half of the national futures trading volume. In late 1999, after the marginincreased from 5% to 15–20%, the mung bean futures had no trades. In May 2009, after theCSRC approved the “Request for Suspension of Mung Bean Futures Trading”, which wasdelivered by ZCE in 2008, the mung bean contract was delisted.

5 On June 2, 1999, the State Council issued the “Interim Regulations on Administration ofFutures Trading”. After it was revised, the new “Regulations on Administration of FuturesTrading” took effect on April 15, 2007. The newest “Regulations on Administration ofFutures Trading” were approval by the State Council on September 12, 2012, taking effect onDecember 1, 2012.

6 China Futures Association consists of group members, including members of the futuresbrokerage firms, special members of futures exchanges and individual members in thefutures industry. It accepts business guidance and management from the China SecuritiesRegulatory Commission and the Organization of National Social Group RegistrationAdministration.

7 http://futures.hexun.com/2012–05–15/141434876.html.8 According to the internal research materials of regulators (and the below is the same).9 Refers to an individual or group that is allowed by law to take legal action as plaintiff ordefendant. It may include natural as well as fictitious persons (such as corporations).

10 Includes lead, fuel, copper, zinc, PVC, soybean meal, corn, strong gluten wheat, cotton,PTA.

11 Refers to the ratio of the position of legal person to the total position in the related contractmarket.

12 Refers to the ratio of individuals trading volume to total trading volume in the relatedcontract.

13 For example, if the accumulative price increase (decrease) based on settlement price in threesuccessive trade days (D1, D2, D3) of a futures contract reaches three times of the stipulatedincrease (decrease) price limit in the contract, the exchange has the right to increase themargin rate with the scale no more than three times of the trading margin standard in thecontract applicable at that time. Under circumstances of significant increased market riskcaused by the special situation of some futures contract, the exchange may take thefollowing measures according to the market risk of some futures contract, such as puttinglimitations on margin movements, putting limitations on opening position and closing posi-tion, adjusting the trading margin standard of this futures contract and adjusting the rangeof price limits of the contract.

14 Analysis and calculation based on data from the Futures Industry Association.


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15 http://www.tygedu.com/newsviews.php?newsid=4234.16 Annual growth rate from 2006 to 2010 is, respectively, 32.22%, 62.06%, 87.24%, 20.27% and

84.74%.17 http://www.pbc.gov.cn/image_public/UserFiles/goutongjiaoliu/upload/File/%E4%

B8%AD%E5%9B%BD%E9%87%91%E8%9E%8D%E7%A8%B3%E5%AE%9A%E6%8A%A5%E5%91%8A%EF%BC%882012%EF%BC%89.pdf.

18 “No. 1 central file” refers to the first document issued by the Central Committee ofCommunist Party of China every year. It has become the proper noun for showing thatthe Central Committee of Communist Party of China attaches great importance to ruralproblems.

19 The monopoly behaviour of the futures industry in China is mainly reflected in the admin-istrative monopoly, futures exchanges monopoly and futures brokerage industrymonopoly. For example, the regulatory department dominates the approval of varieties offutures and products, listing locations as well as restricting every product to be listed on oneexchange without saying that listed procedures are cumbersome. In addition, in relation toa futures company, an exchange has a much stronger voice and negotiation ability, etc.

20 In China, outside of futures exchanges, there are more than 200 electronic commoditytrading markets that trade almost the same contracts and use trading mechanisms and rulessimilar to futures exchanges. This is called “disguised futures” or ‘quasi futures”. Themarkets lack strict regulation and market rules, and their operation and implementation arein chaos. To some extent, they inhibit the healthy development of China’s futures markets.

21 Since the early days, risk events have occurred with futures companies misappropriatingcustomer funds and allowing customers to carry out overdraft trading. However, futuresbrokers firms are not allowed to trade their own accounts and can only trade for customeraccounts, promulgated and provisioned by the regulations on futures trading of September1999. The main business of futures companies in China is to trade on behalf of theircustomers, so the firm’s income mainly comes from commission.

22 The listing mechanism for new futures contracts in China adopts a non- market- approvalsystem. In order to list a new variety of futures contract, it must first undergo a repeatedresearch and review process by the futures exchange and then report to the CSRC accord-ingly. After examination and verification, CSRC then submits a report to the State Council,which needs to consult the relevant state ministries and commissions, related industryadministration departments and even relevant provinces and cities. After getting all feed-back, the State Council might make written instructions. A new variety of futures contractwill finally be launched.

23 China’s three commodity futures exchanges are institutional units implementing enterprisemanagement, while China Financial Futures Exchange is a corporate system, an enterpriseis a legal person. In fact, they are all to different extents the subsidiary of the regulatorydepartment.

24 Article 7: non- profit futures exchanges shall carry out self- regulation according to theirconstitutions, and assume the civil liabilities with all their properties. A futures exchangebears civil liabilities to the extent of all of its property. The persons in charge of futuresexchanges shall be appointed and dismissed by the futures supervision and administrationauthority of the State Council. Article 18: The revenues obtained by a futures exchange shallbe managed and used in accordance with the relevant state regulations and may not bedistributed to members or diverted for other uses.

25 A futures exchange shall establish a board of directors. The chairman and the vice- chairmanshall be nominated by the CSRC and elected by the board.

26 Article 4: Registered capital of the membership futures exchange is divided into equalshares, which is subscribed to by its members.

27 This is not only difficult to explain to most Westerners, but also to some Chinese.28 The meaning is similar to “demutualisation” – ie, the move from a member- owned organi-

sation to a publicly listed organisation.


436


29 The Central Economic Working Conference of the Central Committee of the CommunistParty and the State Council is the highest economic conference in the nation. Its mission is tocarry out economic achievements, deal with international and domestic economic changes,formulate macroeconomic development planning and deploy the following year’s economicwork.

30 Data source from FIA statistics and calculated by author (the below is the same).


437

REFERENCES

Falloon, William D., 1998, Market Maker: A Sesquicentennial Look at the Chicago Board ofTrade (Chicago, Ill: CBOT).

Gorham, Michael and Nidhi Singh, 2009, Electronic Exchanges: The Global Transformationfrom Pits to Bits (Burlington, MA: Elsevier).

Hieronymus, Thomas A., 1996, “A Revisionist Chronology of Papers”.

Hieronymus, Thomas A., 1997, “Economics of Futures Trading: For Commercial andPersonal Profit”, Commodity Research Bureau.

Melamed, Leo, 1993, Leo Melamed on the Markets: Twenty Years of Financial History as Seen bythe Man Who Reolutionized the Markets (New York, NY: Wiley).

Ronalds, Nick and Wang Xueqin, 2006, “The Dawning of Financial Futures in China”,Futures Industry, November/December.

Ronalds, Nick and Wang Xueqin, 2007, “China: Futures Take Dragon Steps”, FuturesIndustry, September/October.

Ronalds, Nick and Wang Xueqin, 2007, “Chinese Commodity Markets: History,Development and Prospects”, in Hilary Till and Joseph Eagleeye (Eds), IntelligentCommodity Investing: New Strategies and Practical Insights for Informed Decision Making(London: Risk Books).

Xueqin, Wang, 2008, Research on Options on Commodity Futures (China Financial &Economic Publishing House).

Xueqin, Wang and Michael Gorham, 2002, “The Short, Dramatic History of FuturesMarkets in China”, Journal of Global Financial Markets, Spring, pp 20–28.

Xueqin, Wang and Nick Ronalds, 2005, “The Fall and Rise of Chinese Futures 1990–2005”,Futures Industry,May/June.


A

agriculture:and farmland as investment, see

farmlandand impact of non- fundamental

information on commoditymarkets 7–9, 8, 9

market participants 249–60commercial traders 249–55,251, 254

commodity index and swaptraders 260

non- commercial traders255–6

proprietary traders: individualand trading groups256–8

systematic and technicaltraders 258–9

market strategies in 279–92calendar spreads 283–7, 284–5,288

crush spreads 289–92, 291directional 282–3geographical spread arbitrage287–9, 289

options 293

and options volatility 292markets, trading in 261–79and correlation benefit 265–8,267, 269, 271, 272

fundamental data points273–8

and investment flows,seasonality and weather268–73

market environments andvolatility 261–2

and strategy selection 262–5,266

technical inputs 278–9and mollisols 230trading in 249–94, 251, 252, 254,

255, 257, 263, 264, 266, 267,269, 271, 272, 274, 275, 276,277, 280, 281, 284–5, 286,288, 289, 290, 291, 293

and weather 66, 72–3see also farmland

American Petroleum Institute103

Anderson, Dwight 385APX- ENDEX 119Australian Stock Exchange 424

439

Index(page numbers in italic type refer to figures and tables)

17 Index CIT_Commodity Investing and Trading 30/09/2013 14:41 Page 439

B

Barnett Shale 36–8base metals:exchanges that list 144–5, 144price movement in 6

BATS Exchange 424BHP Billiton 373BP 91Brazil:and coffee prices 74growing economic prominence

of 86as major meat exporter 177see also BRIC economies

Brent 4–5, 5, 14, 93–5, 94, 97–9Dated 94, 97–8

BRIC economies 105see also oil and petroleum

products: historical priceperspective on

Brookings Institution 245

C

C2 Options Exchange 424capacity allocation and congestion

management (CACM) 119CBOT, see Chicago Board of TradeCFFEX, see China Financial Futures

ExchangeChávez , Hugo 106Chevron 83Chicago Board of Trade (CBOT)

166, 184Chicago Mercantile Exchange

(CME) 27, 89, 101, 214–15,250, 260

China:and farmland 242–4, 243futures market of 409–37, 411,

413, 415, 416, 418, 419, 420,421, 422, 424, 427, 432

clean- up and rectification, firstphase of 410–12

clean- up and rectification,second phase of 412–14

companies 425–6and corporatisation ofexchanges 430

development of 426–7and exchanges,corporatisation of 430

and financial futures,successful launch of 418

first ten years (1990–2000) 410and foreign capital 417further opening up of 431and futures and spot, andprice volatility between 421

and hedging positions,declaring 422

and Hong Kong branches offuture commissionmerchants (FCMs) 417

huge development space of428

and insurance function anddevelopment of options432–3

major developing trends429–31

market operation, micro- characteristics of 420

and open interest 420–1and opening of futurescompanies 417

possible changes to 428–9and price changes and pricehit limits 422


440


and product- listing control,loosening 429–30

and promotion of new futurescontracts 416–17

regulatory regime,determining 414–15, 415

second ten years (2000–10)415–18

and short- term trading activity422

and stable trading andstability 431–2

trading characteristics 2011,analysis of 422–5

trading volumes (2000–10)418–20

trading volumes (2012) 426increased number of refineries in

99as key driver in metals 137and LNG imports 60; see also

BRIC economiesMMT growth of 56net oil consumption of 107

China Financial Futures Exchange(CFFEX) 416, 418, 426

coal 207–25, 209, 216, 217, 218, 219,223

characteristics of 208–13, 209metallurgical 210–11thermal 210

conversation statistics andterminology 223–5

financial markets for 214–15CME product slate 215

and fuel- to- power spreads 220–1market structure for 213–14overview 207–8and supply, demand and

regulation 215–20consumption 217–20regulation 215–16supply and trade 216

terminology and conversionstatistics 223–5

and transportation 211–13Comex Copper Exchange 144commodities index investing

339–41Commodity Futures Trading

Commission (CFTC) 249commodity markets:coal 207–25, 209, 216, 217, 218,

219, 223characteristics of 208–13, 209,210, 210–11

conversation statistics andterminology 223–5

financial markets for 214–15,215

and fuel- to- power spreads220–1

market structure for 213–14overview 207–8and supply, demand andregulation 215–16, 215–20,216, 217–20

terminology and conversionstatistics 223–5

and transportation 211–13and energy and commodity

physical and financialportfolios, enterprise riskmanagement for, see mainentry

excess capacity enjoyed by 3farmland 229–47; see also

farmland

INDEX

441


grains and oilseeds 165–206, 169,170, 172, 175, 180, 182, 191,194, 195, 202

and agribusiness investors,listed 206

feed, food and vegetableproteins 166–201; see alsounder grains and oilseeds

and genetic modification 166,240

impact of non- fundamentalinformation on 3–24, 6, 7, 8,9, 10, 11, 12, 13, 14, 15, 16,17, 18, 19

and agriculture 7–9, 8, 9; seealso farmland

and base metals 6and Dow Jones- UBS (DJUBS)10–13, 20–2

and energy 4–5, 5increased influence of 4and precious metals 6and sentiment 9–20, 10, 11, 12,13, 14, 15, 16, 17, 18, 19

and SG sentiment indicator9–11

inmetals 133–64,135,136,137,138,139,140,141,142,143,144,149–50,151–2,153–4,155–6,157,158,159–60,161–2,163,164; see alsometals

aluminium 149–50base 144–5bulk 145–6, 158copper 151–2demand 135–6gold 159–60inventory 134–5, 135iron ore 157

nickel 153–4palladium 164physical factors drivingmarket in (listed) 148

platinum 163precious 145–7prices 139–44, 139, 140, 141,142, 143

scrap 138silver 161–2supply 136–8zinc 155–6

North American natural gas25–64, 26, 28, 29, 31, 32, 34,35, 37, 38, 39, 40, 45, 48, 50,51, 55; see also natural gas

and coming decade, key issuesfor 53

and demand- side dynamics28–36

and futures, price dynamics in49–53

geography of production anddemand 47

measures and conversions,common units of 26

overview 25–6and regasification 58storage 43–6, 45, 47, 48

oil and petroleum, history andfundamentals 75–110; seealso oil and petroleumproducts

putting momentum into 325–35and better strategy, building330–3

and commodity beta 326and downside protection334–5


442


and long and short 327, 335and roll yield and excessreturn 330, 330

and sources of excess return327–9

and sentiment 9–20, 10, 11, 12,13, 14, 15, 16, 17, 18, 19

in “riskoff” versus “riskon”environments 19–20, 19, 21,22, 23

structural shift in 12–18and SG sentiment indicator 9–11and structural alpha strategies,

see main entry

and weather 65–74data basics 66–7“day in the life” 67–70impacts: on agriculture 72–3impacts: on livestock 73–4impacts: on softs 74impacts: on transport 73tropical conditions 70–2

in wholesale power 113–31; seealsowholesale powermarkets

and coming decade, key issuesfor 128–30

electricity 114–18and hedging strategies andprice formation 121–2

and historical priceperspective 122–8

sources of informationconcerning 130–1

trading of, European markets118–21

see also specific commodities

commodity prices:in metals 139–44

principal component analysisconducted on 4

strong gains in xviicommodity risk premiums

295–305, 298, 300, 301, 303active strategies 296–7benchmarks and overview 296–7convergent and divergent

strategies 297–304, 298, 303active example 301–4

Conservation Reserve Program168, 241

credit valuation adjustment (CVA)391, 392, 393, 395, 396, 397,398, 401, 402, 403, 404, 405,406

and active risk managementwith CVA desks 406–7, 406

allocation approaches 400–3and credit- adjusted rate curves

391and credit limits, charges and

CVA desks 403–5CVA methods 392–5and debt valuation adjustment

390for energy and commodity

derivatives 389–407explained 389–90from portfolio CVA to deal CVA

398–400crude grades and locations 79–83see also oil and petroleum

productsCVR Energy 91

D

Dalian Commodity Exchange 166,414, 416, 417, 418, 426

INDEX

443


Dated Brent 94, 97–8day- ahead and intraday markets

119–20Department of Agriculture (US)

174, 231Dodd–Frank Act 130, 389Dow Jones- UBS (DJUBS) 10, 10–13,

10, 11, 12, 23simple overlay example 20–2

Dubai Mercantile Exchange (DME)27, 99, 100, 146

E

electricity 114–18market design 117–18unique characteristics of 114–17dispatch arrangements 114–16locational issues concerning116–17

and quality 117and scientific laws 114

see also commodity markets;wholesale power markets

energy and commodityderivatives:

credit valuation adjustment(CVA):

and active risk managementwith CVA desks 406–7, 406

allocation approaches 400–3and credit limits, charges andCVA desks 403–5

credit valuation adjustment(CVA) for 389–407, 391, 392,393, 395, 396, 397, 398, 401,402, 403, 404, 405, 406

and credit- adjusted ratecurves 391

CVA methods 392–5

and debt valuation adjustment390

explained 389–90from portfolio CVA to dealCVA 398–400

energy and commodity physicaland financial portfolios:

enterprise risk management for363, 375, 376, 378, 379, 386

backtesting 380–2infrastructure 383–5and Ospraie ManagementLLC, risk managementlessons from 385

policies and governance 371–4risk- adjusted performancemeasurement 383

and risk management systemsand data 384–5

stress tests 377–80valuation models for physicalassets, contracts andfinancial derivatives 382–3

and valuation and riskmethodologies and metrics374–83

“at-risk” metrics: cashflow atrisk, VaR and earnings atrisk 375–7, 375, 376

energy indexes:tracking 337–64, 350, 352–3,

356–7, 362, 363benchmark energy index, spotand energy data 346–9

differential evolutionalgorithm 361

evolutionary solutiontechniques 344–6

genetic algorithm 361–3


444


industry classificationbenchmark 363–4

innovative approach 341–6and problem formulation342–4

and SEI performance 349–59and selective portfolios,statistical properties of355–9, 356–7

Energy InformationAdministration (EIA) 78

Energy Market Observatory 130EPEX Spot 119Essar Oils 99European Network of

Transmission SystemOperators (ENTSO) 130

exchanges:Australian Stock 424BATS 424C2 Options 424Chicago Mercantile (CME) 27,

89, 101, 214–15, 250, 260China Financial Futures (CFFEX)

416, 418, 426Comex Copper 144Dalian Commodity 166, 414, 416,

417, 418, 426Dubai Mercantile (DME) 27, 99,

100, 146India Joint Stock 424–5Intercontinental (ICE) 27, 94, 101,

214, 215, 425London Metal (LME) 144, 145Minneapolis Grain (MGE) 184Multi Commodity (MCX) 146NewYorkMercantile (Nymex)

27, 68, 69, 70, 71, 146, 299, 346New York Stock 364

Shanghai Futures (SHFE) 144,146, 414, 416, 417, 418, 423,424, 426

Shanghai Securities 418Shenzhen Securities 418Singapore Mercantile 424Thailand Futures 425Zhengzhou Commodity (ZCE)

414, 416, 417, 418, 423, 424,426

Exxon 105

F

farmland:biofuels from 231cash returns from 232and debt levels 231and declining worldwide

inventories 231defined 229and food demand 231versus population growth 245,245

and global farming 241–5, 242China 242–4, 243demand for crops andcommodities 244–5

and inflation hedge 231as investment 229–47, 233, 235,

236, 239, 242, 243, 245and mollisols 230and production, limitations of

240–1and conservation programmes241

and renewables, impact of 236–40agricultural products 238–40;see also agriculture

fuels 236–8

INDEX

445


and resource conservation 231scarcity of 231as sustainable asset 232and US infrastructure 231and value creation and

investment 232–6and farmland as leaseproperty 232–3

and risk–return profile 233–4,233

see also agricultureforward curves and market value

of storage 309–11, 310forward markets:physical versus financial 120; see

alsowholesale powermarkets

products 120–1

G

gas futures:price dynamics in 49–53; see also

natural gassee also commodity markets

Germany:and wholesale power markets,

historical price perspective122–4, 123

grains and oilseeds 165–206, 169,170, 172, 175, 180, 182, 191,194, 195, 202

feed, food and vegetableproteins 166–201

feed 170–83food 183–95vegetable proteins (oilseeds)196–201

and genetic modification 166, 240and rotation 201–4

storage 198trends and swing factors for

future 204–5Great Britain:and wholesale power markets,

historical price perspective124–6, 125

Gulf War, first 104, 107see also oil and petroleum


H

Henry Hub 27, 62, 346high- fructose corn syrup (HFCS)

179Hubbert, M. King 103, 105, 108, 110Hurricane Ike 92Hurricane Katrina 42Hurricane Rita 42Hurricane Sandy 76

I

ICE, see IntercontinentalExchangeIndia:Jamnagar complex in 99and LNG imports 60; see also

BRIC economiesmeat consumption in 177–8MMT growth of 56

India Joint Stock Exchange 424–5IntercontinentalExchange (ICE) 27,

94, 101, 214, 215, 425International Financial Reporting

Standards (IFRS) 121Iran–Iraq war 103, 105see also oil and petroleum



446


Iranian revolution 103see also oil and petroleum


Iraq, Kuwait invaded by 104; seealso oil and petroleumproducts: historical priceperspective on

J

Jamnagar 99Japan, MMT growth of 56JBS 199–200

K

Kuwait, Iraq invades 104; see alsooil and petroleum products:historical price perspectiveon

L

Lehman Brothers 3, 4liquefied natural gas (LNG) 54–9,

59, 60, 61exports of 54–8future flow considerations for 63global 54–64imports of 59, 60imports of, and import growth

58–61supply chain 62–3

London Metal Exchange (LME)144, 145

M

market information, sources of130–1

market strategies in agriculture279–92

calendar spreads 283–7, 284–5,288

crush spreads 289–92, 291directional 282–3geographical spread arbitrage

287–9, 289options 293and options volatility 292

Markets in Financial InstrumentsDirective (MiFID) 129–30

metals 133–64, 135, 136, 137, 138,139, 140, 141, 142, 143, 144,149–50, 151–2, 153–4, 155–6,157, 158, 159–60, 161–2, 163,164

aluminium 149–50base:exchanges that list 144–5,144

price movement in 6bulk 145–6, 158copper 151–2demand 135–6gold 159–60inventory 134–5, 135iron ore 157nickel 153–4palladium 164physical factors driving market

in (listed) 148platinum 163precious 145–7exchanges that list 146gold among most activelytraded 146

gold market an outlier among6

many trading centres for 146most actively traded 146

INDEX

447


prices 139–44, 139, 140, 141, 142,143

scrap 138silver 161–2supply 136–8zinc 155–6

meteorology, seeweatherMinneapolis Grain Exchange

(MGE) 184Mobil 105mollisols 230Multi Commodity Exchange

(MCX) 146

N

National Hurricane Center 70National Weather Service, US 66,

67natural gas:demand- side dynamics for

28–36exports 34–6, 46industrial use 28–30, 28, 29power generation 30–4, 31, 32,34

residential and commercialdemand 34, 35, 37

futures, price dynamics in, andfutures, price dynamics in49–53

liquefied (LNG) 25, 34, 36global 16, 55, 57

North American market in25–64, 26, 28, 29, 31, 32, 34,35, 37, 38, 39, 40, 45, 48, 50,51, 55


and demand- side dynamics 28

and futures, price dynamics in49–53

geography of production anddemand 47

measures and conversions,common units of 26

overview 25–6and regasification 58storage 43–6, 45, 47, 48, 49, 52

peak in (2011) xviisupply- side considerations of

36–43and ethane rejection 42–3shale 36–42, 38weather impacts on 42

Natural Resource ConservationService (NRCS) 230

New York Mercantile Exchange(Nymex) 27, 68, 69, 70, 71,146, 299, 346

New York Stock Exchange 364New York Times 72, 74, 104, 207non- fundamental information:impact of, on commodity

markets 3–24, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18,19; see also commoditymarkets

and agriculture 7–9, 8, 9and base metals 6and Dow Jones- UBS (DJUBS)10–13, 20–2

and energy 4–5, 5increased influence of 4and precious metals 6and sentiment 9–20and SG sentiment indicator9–11, 10, 11, 12, 13, 14, 15,16, 17, 18, 19


448


North American market in naturalgas 25–64, 26, 28, 29, 31, 32,34, 35, 37, 38, 39, 40, 45, 48,50, 51, 55


and demand- side dynamics 28and futures, price dynamics in

49–53geography of production and

demand 47measures and conversions,

common units of 26overview 25–6and regasification 58storage 43–6, 45, 47

“Notice to Resolutely Stop theBlind Development of theFutures Markets” (China)410

O

oil:peak in (2011) xviiand petroleum, history and

fundamentals 75–110; seealso oil and petroleumproducts

oil and petroleum products 75–110,77, 78, 80, 81, 82, 84, 85, 86,87, 88, 90, 91, 92, 93, 94, 96,97, 100, 102, 104, 107, 108,109

and critical fuel and elasticity75–8

and crude grades and locations79–83

and crude markets and trading97–101

and crude pricing and trading89–97

and crude production trendssince 1960 104

and crude transport and chokepoints 87–9, 87, 88

historical price perspective on101–10

and OPEC 101–6 passimmajor supply, locations of 83–7;

see also oil and petroleumproducts: and crude gradesand locations

reason for oil 75–101and seasonality 78–9, 80storage 89–90

oilseeds and grains 165–206, 169,170, 172, 175, 180, 182, 191,194, 195, 202

feed, food and vegetableproteins 166–201

feed 170–83food 183–95vegetable proteins (oilseeds)196–201

and genetic modification 166,240

grains and oilseeds 206and rotation 201–4storage 198trends and swing factors for

future 204–5Organization of Petroleum

Exporting Countries (OPEC)101–6 passim, 102

modern oil pricing begins withbirth of 101

Ospraie Management LLC 385

INDEX

449


P

PCA, see principal componentanalysis

petroleum and oil products, see oiland petroleum products

PetroPlus 84, 100Ponca 91precious metals 145–7exchanges that list 146gold among most actively traded

146gold market an outlier among 6many trading centres for 146most actively traded 146

principal component analysis(PCA), explained 4

PurchasingManager’sIndex(PMI)4

R

regasification 54, 56, 58, 60–3passim, 61

Regulation on Energy MarketIntegrity and Transparency(REMIT) 130–1

Renewable Fuel Standard (RFS)(US) 253

rice, as percentage of world’sdietary energy 192

“riskoff” versus “riskon”environments 21, 22, 23

Russia:becomes largest producer of

crude 101growing economic prominence

of 86and LNG capacity 63and naturalgas pipeline exports,

decline in 63see also BRIC economies

S

Saddam Hussein 104Saudi Arabia, as swing oil

producer 105scrap metals 138see alsometals

sentiment:and commodities 9–20, 10, 11, 12,

13, 14, 15, 16, 17, 18, 19; seealso commodity markets

see also SG sentiment indicatorSG sentiment indicator, and

commodities 9–11shale 36–42, 38Shanghai Futures Exchange

(SHFE) 144, 146, 414, 416,417, 418, 423, 424, 426

Shanghai Securities Exchange 418Shenzhen Securities Exchange 418SHFE, see Shanghai Futures

ExchangeShuanghui International 205SingaporeMercantile Exchange 424Smithfield Foods:JBS acquires beef business of 200Shuanghui International

acquires 205South Korea, MMT growth of 56Soviet era, and assertion for energy

dominance 103; see also oiland petroleum products:historical price perspectiveon

spark and dark spreads 121Spot Energy Index 337–64see also energy indexes: tracking

storage:forward curves and market

value of 309–11, 310


450


of grains and oilseeds 198of natural gas 43–6, 45, 47, 49, 52of oil and petroleum 89–90

structural alpha strategies 305–35,310, 311, 314, 316, 319, 320,323, 324

and backtesting bias 321–2curve placement 308, 309–15,

311, 314market segmentation acrosscommodity forward curves311–12

risks of 313–15seasonality in 312–13

momentum 308–9, 315–17and commodities 325–35and commodity beta 326and downside protection334–5

and long and short 327, 335and roll yield and excessreturn 330, 330

and sources of excess return327–9

volatility 309, 318–20weaving an alpha basket 322–4

T

tail- riskmanagement principles 381Taiwan, MMT growth of 56Texas Railroad Commission 101,

103Thailand Futures Exchange 425“Three Cheers for US$5 Oil” 104trading volume analysis:and China’s futures market

409–37, 411, 413, 415, 416,418, 419, 420, 421, 422, 424,427, 432

clean- up and rectification, firstphase of 410–12

clean- up and rectification,second phase of 412–14

companies 425–6and corporatisation ofexchanges 430

development of 426–7and exchanges,corporatisation of 430

and financial futures,successful launch of 418

first ten years (1990–2000)410

and foreign capital 417further opening up of 431and futures and spot, andprice volatility between421

and hedging positions,declaring 422

and Hong Kong branches offuture commissionmerchants (FCMs) 417

huge development space of428

and insurance function anddevelopment of options432–3

major developing trends429–31

market operation, micro- characteristics of 420

and open interest 420–1and opening of futurescompanies 417

possible changes to 428–9and price changes and pricehit limits 422

INDEX

451


and product- listing control,loosening 429–30

and promotion of new futurescontracts 416–17

regulatory regime,determining 414–15, 415

second ten years (2000–10)415–18

and short- term trading activity422

and stable trading andstability 431–2

trading characteristics 2011,analysis of 422–5

trading volumes (2000–10)418–20

trading volumes (2012) 426

U

United Nations Food andAgriculture Organization229

US National Weather Service 66, 67

W

weather:and agriculture markets, trading

in 268–73; see alsoagriculture

and commodities 65–74data basics 66–7“day in the life” 67–70impacts: on agriculture 72–3impacts: on livestock 73–4impacts: on softs 74impacts: on transport 73tropical conditions 70–2

and North American gas market42

West Texas Intermediate (WTI) 20,76

wholesale power markets 113–31,123, 125

and coming decade, key issuesfor 128–30

changing consumerrequirements 130

evolving market design 128–9financial market regulation129–30

electricity 114–18dispatch arrangements114–18

and functioning wholesalemarkets, location of 118

locational issues concerning116–17

market design 117–18and quality 117and scientific laws 114–18

and hedging strategies and priceformation 121–2

and historical price perspective122–8

and European markets, widerrelationship between 126–7

Germany 122–4, 123Great Britain 124–6, 125new developments 127–8

sources of informationconcerning 130–1

trading of, European markets118–21

design principles, andimportance of balancingregime 118–19

forward markets 120–1Wood River Refinery 91


452


Z

ZCE, see Zhengzhou CommodityExchange

Zhengzhou Commodity Exchange(ZCE) 414, 416, 417, 418, 423,424, 426

INDEX

453
