volatility paradigm and paradox

Christopher Cole, CFA Artemis Capital Management LLC Artemis Vega Fund LP 520 Broadway, Suite 350

Santa Monica, CA 90401

(310) 496-4526 phone

(310) 496-4527 fax

[email protected]

VOLATILITY PARADIGM AND PARADOX C B O E R I S K M A N A G E M E N T C O N F E R E N C E - M A R C H 3 , 2 0 1 3

For Investment Professional Use. Not for Distribution

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Volatility at World’s End Deflation Imagine the world economy as an armada of ships passing through a narrow and

dangerous strait between the waterfall of deflation and hellfire of inflation

Our resolution to avoid one fate may damn us to the other

Like Odysseus in the epic poem the global economy is trapped between the monsters of Scylla (fire of inflation) and Charybdis (the waterfall of deflation)


Illustration by Brendan Wuiff based on concept by Christopher Cole

Volatility at World’s End Deflation Imagine the world economy as an armada of ships passing through a

narrow and dangerous strait leading to the sea of prosperity. Navigating the channel is treacherous for to err too far to one side and your ship plunges off the waterfall of deflation but too close to the other and it

burns in the hellfire of inflation


50

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50,000

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DJI

A (l

oga

rith

mic

sca

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Rea

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d V

ola

tilit

y (%

)

Volatility at World's End DeflationDow Jones Industrial Index (RHS) vs. 1-month Realized Volatility of DJIA (LHS)

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Volatility shocks are rightfully associated with deflationary crashes

Financial media pundits called the 2008 crash an “unprecedented” period of volatility

VIX index reached 20+ year high of 80.86 on November 20th, 2008

2008 was only “unprecedented” if you assume data from the inception of the VIX index in 1990

Historical DJIA realized volatility data going back to 1929 shows volatility climbed to similar levels or higher a total of 6 times in the past 80 years! VXO, precursor to VIX, hit 150.19 on Oct 19, 1987 / 2008 was rare but not unprecedented!

Weimar Germany would have experienced over 2000% monthly realized volatility

$1mm variance swap struck in 1919 at 17.5% (average vol for period) would payoff $417 billion by 1923 (hypothetical)

Germany in 1920-21 had no surface inflation, a booming stock market, and briefly the strongest currency in the world

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4 Source: “Economics of Inflation; A Study of Currency Depreciation in Post-War Germany" by Constantino Bresciani-Turroni Out of Print / 1968 (1) Based upon monthly realized variance from available stock price data.

Vol is a statistic indifferent to price direction increasing when assets decline only because prices fall faster than they rise

How would volatility markets respond to an inflationary shock? (e.g. 20%+ inflation a year for 3 years)

Extreme inflation could turn variance markets backwards… literally… as volatility could rise in conjunction with stocks

Impacts in how risk is spread between right and left tails of probability distributions

Extreme volatility can also occur in hyperinflation

Volatility historically spikes when markets decline and vice versa… but this is a rule and not a law… extreme volatility can also occur in hyperinflation

0000001101001,00010,000100,0001,000,00010,000,000100,000,000

0

20

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Feb

-18

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-18

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g-18

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v-18

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-19

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-19

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g-19

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v-19

Feb

-20

May

-20

Au

g-20

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g-23

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v-23

Pe

rfo

rman

ce in

pap

er

mar

ks (

mil

)

Pe

rfo

rman

ce a

dj.

fo

r fi

xed

exc

han

ge Performance of German Stock Market during Weimar Republic Hyperinflaton

Adj. according to USD exchange rate

Adj. according to wholesale index numbers

In paper marks, Weimar

0

500

1,000

1,500

2,000

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-18

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lati

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(%)

Weimar VIX?(1)

Realized Volatility of German Stock Market during Weimar Republic Hyperinflation(monthly volatility data annualized)

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Impossible Object Illustration highlighting the limits of human perception challenging whether our awareness of naïve reality is

relevant to our understanding of truth… vast importance to mathematics, art, philosophy, and modern risk

Modern financial markets are an impossible object

Illustration by Brendan Wiuff based on concept by Christopher Cole

When global central banks manipulate the cost of risk the mechanics of price discovery break down resulting in paradoxical expressions of value that should not exist according to efficient

market theory

Fear and safety are now interchangeable in a speculative and high-stakes game of perception. The efficient frontier is now contorted to such a degree that traditional empirical views are no longer relevant.”

Modern financial markets are an impossible object Volatility of an impossible object is our changing perception of risk

Volatility of an Impossible Object

6

Flash Crash

(Black Monday 1987, Flash Crash)

Slowly building crash with slow recovery

End of leveraging cycle

High volatility, but relatively muted VOV

Great Depression

Global Recession

Flash Crash

Hyper-speed crash with fast recovery

Market Fragmentation & Self-Reflexity

Extreme Volatility of Volatility

Predictable (in retrospect) Unpredictable (even In retrospect)


Market fragmentation and self-reflexity

High volatility of volatility

Common sense says do not trust your common sense

Volatility itself is now a paradox (both in time and space)

Temporal Paradox

Low Spot-VIX but steep VIX Futures term structure

Power law distortions in daily volatility moves

Steep Volatility-of-Volatility term structure & Skew but low-spot VOV

Spatial Paradox

Low volatility-of-volatility (realized) but higher potential for volatility-of-volatility (implied)

Historically expensive gamma on tails of probability distribution

Steeper volatility-of-volatility skew

Volatility is Global Macro

Bull Market in Fear is Defined by

1. Volatility Kindling / Higher Potential Vol of VIX 2. VIX Futures are less effective hedging tools 3. Unbalanced VIX Option Shadow Gamma

4. Shadow VIX Theta 5. Skew shift 6. Volatility of VIX futures increasingly driven by

short squeeze rather than VIX itself

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Unknown Unknowns Known Unknowns

Volatility of Volatility Volatility

Everything you need to know about trading volatility

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Debt Ceiling Crisis

China hard landing

War with Iran

European Crisis

Global Recession

Fiscal Austerity

“There are known knowns; there are things we know that we know. There are known unknowns; that is to say there are things that, we now know we don't know. But there are also unknown unknowns – there are things we do not know, we don't know.”

Donald Rumsfeld, United States Secretary of Defense

?

Modern volatility markets can put a price on “unknown unknowns” via the volatility-of-volatility

Episodes of elevated implied vol-of-vol are associated with lower equity returns

SPX periods of high realized volatility-of-VIX underperform low by 13% annually

Individual stocks with high implied vol-of-vol underperform low VOV stocks by 10% annually(1)

Today everyone is afraid of the next 2008 but I am afraid of the next 1987…. in stocks… but more likely bonds

Regimes of Volatility-of-Volatility (2007 to 2012)

Period Average

Volatility Regime Vol of VIX VIX indexSPX Return

(annual)

Total

(2007 to Sep 2012)87.5 24.8 +1%

Bull Market

(2006 to July 2007)81.7 13.8 +5%

Credit Crisis Onset

(Aug 2007 to Aug

2008)

82.7 23.0 -11%

Market Crash

(Sep 2008 to Feb

2009)

95.7 49.6 -71%

Recovery to Flash

Crash

(Mar 2009 to May

80.8 26.7 +35%

Post-Flash Crash

Steepening

(May 2010 to Oct

90.5 23.2 +10%

LTRO Steepening

(Nov 2011 to Sep

2012)

97.7 20.3 +16% Vanilla Options

VIX Index

Implied Volatility

Vol Term Structure

Forward Volatility

Convexity

Tail Risk Hedging

Vol Curve Trades

Many investors who trade volatility (VIX ETNs, VIX futures) don’t realize they are actually

trading a market expectation of future uncertainty (volatility-of-volatility)… not volatility itself… there is a big difference

Episodes of elevated uncertainty (volatility-of-volatility) are associated with lower equity returns

but they are hard to predict

Many people who trade volatility do not realize they are only trading a market expectation of future

uncertainty… not volatility itself

Risks that you know and can

quantity

Risks that you know but can’t

quantify

Risks that you don’t know but could quantify

Risks that you don’t know and can’t quantify

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1. Abnormally Steep Volatility Term-Structure

2. Distortions in Volatility from Monetary Policy

3. Expensive Portfolio Insurance

4. Violent Volatility Spikes and Hyper-Correlation

The new volatility regime is a reflection of investor neurosis generated by forced participation in risk assets by the financial oppression of global central banks

What is the “Bull Market in Fear”? New paradigm for pricing risk that emerged after the 2008 financial crisis as

related to our collective fear of the next deflationary crash

Bull Market in Fear is not about where volatility is today as so much as it is about where markets think volatility will be tomorrow

I. Emotional Post-traumatic Deflation Disorder

Desire for safety and security at any cost

II. Monetary Forced participation in risk assets drives desire for hedging

Unspoken feeling that gains in financial assets are “artificial”

III. Macro-Risks Debtor-developed economies face structural headwinds

Unrest in Middle East, Iran, Japan & China Tensions

IV. Regulatory Government regulation (Dodd-Frank, Volcker rule) has

constrained risk appetite for banks to supply volatility

Lower demand for structured products by investors

Structural imbalances in supply-demand dynamics of volatility markets

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Greater Demand for

Volatility

Less Supply of Volatility

I. Emotional Post-traumatic Deflation Disorder Memories of deflationary collapse create visceral and

primitive desire to avoid that pain again Desire for safety and security at any cost

II. Monetary Forced participation in risk assets by the financial

oppression of global central banks results in greater demand for hedging

Unspoken feeling that broad based gains in financial assets are “artificial”

III. Geopolitical Risk Factors Debtor-developed economies face demographic and

structural headwinds Unrest in Middle East

IV. Structural and Regulatory Greater government regulation (Dodd-Frank, Volcker rule)

has constrained risk appetite for banks to supply volatility to the market

Lower demand for structured products by investors (which sell vol)

TEMPORAL PARADOX - Abnormally Steep VIX Futures Term Structure

10

BULL MARKET IN FEAR

"There is no terror in the bang, only in the anticipation of it." Alfred Hitchcock

The most extreme term-structure for forward volatility in two decades reflects continued anticipation of a deflationary collapse and structural imbalance in risk

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VIX

M3

M6

0.50x

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0.90x

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1.50x

1.70x

1.90x

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ay-0

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Bull Market in Fear / VIX Futures Curve 2004 to Present

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30

35

Spot Month 1 Month 2 Month 3 Month 4 Month 5 Month 6 Month 7 Month 8

Forw

ard

VIX

ind

ex (%

)

Low Volatility? Really?VIX Futures Curve Comparison

August 2012 vs. September 2008

August 17, 2012 / Lowest VIX in 5 years at time

September 15, 2008 / Day after Lehman Bros. Bankruptcy

February 19, 2013

TEMPORAL PARADOX - Abnormally Steep VIX Futures Term Structure

11

Low VIX index does not mean cheap volatility

On August 17th 2012 spot VIX touched a 5 year low at 13.45 however…

It was more expensive to buy forward volatility at 6-12 months with the VIX at 13.45 in 2012 than it was one day after Lehman went bankrupt in 2008 when the VIX was at 31

Volatility hedge executed at the August 2012 low in spot-VIX would have already lost -12% of its value even while VIX increased by +15%

Successful hedging requires going beyond simplistic heuristics based on the absolute price of the VIX

!

Volatility is more than the VIX index

Overt focus on VIX is analytical equivalent using the 1yr UST to explain the entire bond market!

Volatility is more than the VIX index

Overt focus on VIX is analytical equivalent using the 1yr UST to explain the entire bond market!

Low VIX index does not mean cheap volatility Forward volatility more expensive in August 2012 at the 5 year low in the VIX than it was the day after Lehman went bankrupt

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130%

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No

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Jan-1

3

VIX

Ind

ex

(%)

Fed

BS

% C

han

ge s

ince

Se

pte

mb

er

20

08

No Fed ActionQEIQEIIOp. Twist+LTRO(ECB)QEIIIVIX

TEMPORAL PARADOX - VIX Regimes Defined by Central Banking

12

Since 2008 global central banks have expanded their balance sheets by $9 trillion - enough fiat money to buy every person on earth a 55'' wide-screen 3D television

VIX spikes consistently occur after the end of central bank balance sheet expansion

Orwellian financial repression as central banks define the risk premium in markets

16 central banks have eased since the fourth quarter of last year

Fed and ECB pledged unlimited purchases of bonds to support the system

If Fed follows through on promise to buy $40bn MBS it will own the entire market in a decade

Risk and Vol Returns in Fed BS Regimes

Crisis and Recovery (September 2008 to September 2012)

Period Average Weekly Change

SPX VIX 21d SV Fed BS

Fed Balance Sheet ↑ 0.6% -1.7% 0.0% 1.5%

Fed Balance Sheet > +1σ ↑ 3.2% -7.4% 0.0% 8.1%

Fed Balance Sheet ↓ 0.0% 1.3% -2.0% -0.9%

Fed Balance Sheet < -1σ ↓ 1.2% 2.7% -1.9% -4.7%

Post-Crisis Recovery Period (Mar 2009 to Sep 2012)

Period Average Weekly Change

SPX VIX 21d SV Fed BS

QEI con't (March09-Jun 09) 1.4% -2.6% -2.5% 0.6%

Post QEI (Jun09-Oct10) 0.2% -0.2% -0.8% 0.2%

QEII (Sep10-June11)(1) 0.5% -1.0% 0.0% 0.5%

Post-QEII (July11-Nov11) -0.2% 2.2% 2.3% -0.1%

LTRO (Dec11 to Sep 0.3% -1.5% -2.7% 0.0%

(1) period following announcement of QEII at Jackson Hole August 2010.

Sources: Federal Reserve Bank, ECB, Bloomberg

Flash Crash

Aug 2011 Crash

QEII

LTRO (ECB)

Fed Balance Sheet Expansion and VIX index

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-50

.0%

-35

.0%

-20

.0%

-5.0

%

10

.0%

25

.0%

0%

10%

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50%

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Cu

mu

lati

ve

Pro

ba

bil

ity

Implied 12m %G/Lin S&P 500 index

S&P 500 Index 12-month % Contribution to Model-Free Variance by Expected Returns

(1995 to March 2012)

40%-50%

30%-40%

20%-30%

10%-20%

0%-10%

SPATIAL PARADOX - High Cost of Tail Risk Insurance

13

Fat Left Tails have Dominated the Distribution of S&P 500 index Variance

You are not smart for hedging what everyone else already knows! Since the 2008-crash strips of OTM SPX options show 21% contribution to a -50% or more crash(1)

Realized probability of a 50% log drop in markets is only 2.93% (using DJIA data to 1928)

1995 to 2012

Fear of deflation is not MISPLACED but it is MISPRICED

You are not smart for hedging what everyone else already knows! Tail risk insurance is now priced at multiple times the eight decade probability of those declines being realized

representing irrational exuberance for fear

What happens when everyone sells their portfolio insurance at the same time!?

Note: Artemis calculates the implied probability distribution using interpolated weights from variance swap pricing. This methodology may occasionally give higher weightings to tails in down markets than other methods like taking the second derivative of call prices, fitting mixture of normal PDFs to recover prices, or fitting vol models (SVI,SABR).

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0%

2%

4%

6%

8%

10%

12%

14%

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13

Yie

ld(%

)

Volatility Yield (%) vs UST Bond Yields (%)1990 - 2013

-25% from SPX Strike Rate Breached

Volatility Yield (sell 1yr SPX put / -25% discount)

10yr UST Yield

30yr UST Yield

SPATIAL PARADOX – VOLATILITY and BONDS

14

For the first time in history the annualized short volatility yield (OTM SPX Put) is competitive with the yield on long dated UST Bonds!

WOW!

I find it funny when academics claim the US government will never default because it can just print money… that is like saying my house will never be burglarized because if someone tried I could just light it on fire

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1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

-65%-55%-45%-35%-25%-15%-5%

SPATIAL PARADOX – VOLATILITY and BONDS

15

When the “Bull Market in Fear” meets a “Bubble in Safety” a short equity option position and “risk-free’ UST bond have similar risk-to-reward payoffs!

0.00x

0.20x

0.40x

0.60x

0.80x

May-03 May-04 May-05 May-06 May-07 May-08 May-09 May-10 May-11 May-12

TLT 20+ US Treasury Bond ETF - 5% OTM Vol Skew

Yield to Risk / UST Bond vs. "Volatility Bond" (Collateralized Short Put on S&P 500 index)Investment Stress Test #1 Stress Test #2 Stress Test #3 Stress Test #4

Volatility Bond / Short SPX Put + Collateral SPX ↓ -9% SPX ↓ -14% SPX ↓ -25% SPX ↓ -50%

Yield MaturityEst. MTM

Loss

Historic Prob.

%

Risk to

Reward

Est. MTM

Loss

Historic Prob.

%

Risk to

Reward

Est. MTM

Loss

Historic Prob.

%

Risk to

Reward

Est. MTM

Loss

Historic Prob.

%

Risk to

Reward

SPX Put (Strike @-25%) 2.69% 1 year -2% 68% 1.373x -4% 39% 0.616x -11% 13% 0.242x -33% 2% 0.081x

SPX Put (Strike @2009 lows) 0.51% 1 year -0.4% 68% 1.319x -0.9% 39% 0.588x -3% 13% 0.176x -15% 2% 0.034x

US Treasury Bond UST Rates ↑ 100bps UST Rate ↑ 200bps UST Rate ↑ 325bps UST Rate ↑ 600bps

Yield MaturityEst. MTM

Loss

Historic Prob.

%

Risk to

Reward

Est. MTM

Loss

Historic Prob.

%

Risk to

Reward

Est. MTM

Loss

Historic Prob.

%

Risk to

Reward

Est. MTM

Loss

Historic Prob.

%

Risk to

Reward

US Treasury Bond / 10-year 1.87% 10 years -9% 68% 0.214x -17% 39% 0.113x -25% 13% 0.074x -41% 2% 0.045x

US Treasury Bond /30-year 3.09% 30 years -18% 68% 0.176x -31% 39% 0.099x -44% 13% 0.070x -62% 2% 0.050x

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

-65%-55%-45%-35%-25%-15%-5%

1yr Volatility Bond (short OTM SPX Put Option Collateralized)

Lond Dated UST Bonds

Risk (Loss in Stress Test)

Efficient Frontier / Long Dated UST Bond vs. 1yr OTM Short Puts (collateralized)

10yr UST Bond

30yr UST Bond SPX Put (Strike @ 2009 lows)

SPX Put (Strike @-25% OTM)

Risk / Unrealized Loss in Stress Test Scenario

SPX ↓ -9% to -14% 68% to 33% probability

SPX ↓ -50% 2% probability

10yr UST Bond

30yr UST Bond

SPX Short Put (Strike @-25% OTM)

Rates ↑ 320bps to 600bps 13% to 2% probability

Rates ↑ 100bps to 200bps 68% to 33% probability

Note: All data as of February 17, 2013. Estimated unrealized loss on position given stress test scenario. Historic probability data based on period of 1960 - 2012 for the UST bonds and 1950 to 2012 for the S&P 500 index. Option pricing based on estimated local volatility shifts, however actual shifts may differ from estimates during a real crash depending. All stress tests are assumed to occur close to the purchase period of the instrument. Unrealized losses may differ closer to maturity.

Higher rate volatility can be realized in deflation and inflation

When risk-free is risky it is time to buy volatility on safety itself

Look to buy long-dated forward rate volatility (10yr straddles fwd starting) to exploit this mispricing in risk

SPX ↓ -25% 13% chance

SPX Put Stress Test

UST Bond Stress Test

Risk / Unrealized Loss in Stress Test Scenario

Efficient Frontier / Risk to Reward Comparison Long Dated UST Bond vs. 1yr OTM Short Puts (collateralized)

Ret

urn

/ Y

ield

Risk Free Assets are Risky

We all know shorting volatility is very dangerous…

So… which is riskier right now? 1. Short collateralized far OTM S&P 500 index put (-25% or -50% OTM for 1yr)

2. Long a “risk-free” US treasury bond (10-30 yrs)

For the first time in history the volatility yield is competitive with the yield long dated UST Bonds

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0%

5%

10%

15%

20%

-50% -43% -35% -28% -20% -13% -5% +3% +10% +18% +25% +33% +40% +48%

Cu

mu

lati

ve P

rob

abili

ty W

eig

hti

ng

One Year Gain/Loss % in S&P 500 index

Mirror Reflection: Deflation vs. HyperinflationS&P 500 Probability Distributions in different Regimes of Risk

1-year Gain-Loss%

Implied from March 2012 SPX options

Simulated from in 2013-2022 Hyperinflationary Model (1 scenario of 10k)

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D PA

RA

DO

X The more people fear the LEFT TAIL the more you should buy the RIGHT…

16

Maybe it is correct to buy tail risk insurance ... but is everyone just hedging the wrong tail?

Volatility in the Mirror:

Right tails dominate left tails

Volatility driven by increases in stock prices

SPX calls at premium to puts

Volatility term structure would invert with higher asset prices

Note: Artemis created a model to simulate the behavior of the S&P 500 index and volatility during an inflationary shock. The model is not intended to be a prediction of the future but is merely a rudimentary stochastic-based method to understand what modern markets may look like in rampant inflation. The simulation runs 10,000 price scenarios for the S&P 500 index over 10 years modeling daily stock price behavior using a generalized Wiener process (Wiener.. not Weimar) and a drift rate that assumes linkages between annual CPI and equity performance. We assume inflation rises sharply from current levels of 2.87% in 2012 to 26% by 2015 and stays elevated at that level until 2017 (20% a year overall). The average volatility shifts are based upon assumptions regarding equity return to variance parameters observed in prior inflationary episodes (1970s US & 1920s Germany). The simulation shows annualized SPX returns for the decade at +9.94% but adjusted for inflation this drops to -9.8%.

No precedent for how modern derivatives market would perform in the hell of destructive inflation

…but it is a valuable exercise to theorize! … Volatility markets turn backwards… literally

Double Convexity

Far-OTM long-dated equity call options cheap form of inflation protection

Double convexity as prices influenced by rising volatility and interest rates

Volatility and rates are self-reinforcing in inflation crisis

0%

5%

10%

20.0%30.0%40.0%

0

500

1,000

1,500

2,000

2,500

15%20%

25%30%

35%40%

45%

50%

5yr UST Yield

Val

ue

of C

all O

pti

on

5yr implied vol

Double Convexity in Inflation Boom SPX 10yr OTM Call - 10K Strike

5 yrs to expiry/ SPX @ 3,000 (16% annual gain)

Future?

PARADOX IS FUNDAMETNAL

17


VIX index Fire Danger is High – greater potential for Vol-of-VIX VIX Derivatives

1. Unbalanced Shadow Delta 2. Unbalanced Shadow Gamma 3. Shadow Theta

VIX Structured Product Inconsistent hedging ratios Vol-of-VIX derivatives driven by structured product flow rather than VIX


Effects of Volatility Paradox

I. VIX index

Fire danger (VOV) is higher despite low spot-Vol Higher potential for Volatility-of-Volatility (kindling) Skew Realization

II. VIX Futures & Options

Inconsistent hedging ratios

Loss of hedging effectiveness Roll-Yield Deception

III. VIX Structured Products & ETNs

To many players shorting the front of the curve

Shadow Risk in “dynamic” VIX structured products

Roll Yield Short Squeeze?

Inconsistent Deltas Shadow Gamma Shadow Theta

VO

LATILITY P

AR

AD

IGM

AN

D PA

RA

DO

X

0

0.2

0.4

0.6

0.8

1

20

00

20

01

20

02

20

03

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

Co

rre

lati

on

(0-1

)

HIGHER CORRELATIONS lead to...S&P 500 Sector Correlation (60 day)

2000 to 20120

0.2

0.4

0.6

0.8

1

20

00

20

01

20

02

20

03

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

Co

rre

lati

on

(0-1

)

HIGHER CORRELATIONS lead to...S&P 500 Sector Correlation (60 day)

2000 to 2012

45

65

85

105

125

145

165

185

205

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

Vo

lati

lity

(%)

More VIOLENT VOLATILITY SPIKESVolatility of VIX index (60 day)

2000 to 2012

(0.80)(0.60)(0.40)(0.20)

-0.20 0.40 0.60 0.80 1.00

Au

g-0

3

Feb

-04

Au

g-0

4

Feb

-05

Au

g-0

5

Feb

-06

Au

g-0

6

Feb

-07

Au

g-0

7

Feb

-08

Au

g-0

8

Feb

-09

Au

g-0

9

Feb

-10

Au

g-1

0

Feb

-11

Au

g-1

1

Feb

-12

Au

g-1

2

Hedge Fund Strategies 12m Correlation to ATM Short Straddle on SPX

(HFRX Absolute Return, Equity Nuetral, Hedge Index, Merger Arb, RV Arb, Convertible Arb / Monthly)

18

Hyper-Correlation Drift

“Bull Market in Fear” has registered the highest cross-asset correlation readings between stocks, sectors, countries, and different asset classes in history

Massive headache for diversification

Fire Risk can be high when the forest is calm Higher correlations are kindling for violent VIX fires (spike)

Volatility-of-VIX has reached new highs every year since 2008 in concurrence with higher correlation drift

Implied volatility of an index is more sensitive when average correlations are higher

Hence volatility-of-volatility as a second derivative is sensitive to changes in both correlations and average volatility of index components (see left chart)

Higher correlations (e.g. >0.75) imply higher Vol-of-VIX (100+ vs. 90 historic average)

Relationship between Correlation and Volatility

Volatility of an index is more sensitive when average correlations are higher (higher volatility of volatility)

Hence Volatility-of-VIX has reached new highs every year since 2008 in concurrence with correlation drift

Volatility of Volatility WILDFIRE - Correlations

We are all volatility traders now!

In correlated markets asset selection is negated and alpha becomes increasingly driven by rising and falling volatility

Many hedge fund strategies converge to simple synthetic short (or long) volatility trades

Modern volatility markets can put a price on “unknown unknowns” via the volatility-of-volatility

Episodes of elevated implied vol-of-vol are associated with lower equity returns

SPX periods of high realized volatility-of-VIX underperform low by 13% annually

Individual stocks with high implied vol-of-vol underperform low VOV stocks by 10% annually(1)

VO

LATILITY P

AR

AD

IGM

AN

D PA

RA

DO

X

Volatility of Volatility WILDFIRE - Correlations

Today the difference between high implied and falling realized correlations makes hedging single stock names cheaper than buying index vol Volatility-of-volatility microstructure is calmer than at any point

over the past six years of data (below)

The VIX index registered the lowest intra-day movement in history on January 11th (1.14%)

S&P 500 index had biggest yearly reduction in daily moves in eight decades (since FDR 1934 USD devalue)

Source: Barlcays GlobalVol

VO

LATILITY P

AR

AD

IGM

AN

D PA

RA

DO

X

Volatility-of-VIX microstructure is calmer in 2012 however the last 30 minutes of the trading day have become increasingly more violent than previous periods

19

S&P 500 Implied Correlation (12m)

S&P 500 Realized Correlation (12m)

BSM Put-Call Parity Relationship is consistent when VIX options are priced using VIX futures as the underlying

When the VIX futures curve is in contango deep in-the-money VIX puts will trade at a discount to intrinsic value when evaluated against spot VIX

Likewise deep in-the-money calls will trade at a discount during backwardization

Most commercial options programs do not make this adjustment, erroneously pricing implied vol from the spot VI

5. M

OD

EL FR

EE VO

LATILITY E

XP

OSU

RE

VIX options widely misunderstood VIX options are priced off VIX Futures, NOT the VIX Index

20

Violation of BSM put-call parity when vol calculated on spot VIX index

BSM Put-Call parity holds when vol is calculated based on the VIX futures (accurate method)

Expiration

Type Strike Jun-11 Jul-11 Aug-11 Sep-11 Oct-11

Put 16 60% 43% 35% 26% 23%

Put 17 65% 43% 35% 24% 22%

Put 18 72% 45% 36% 22% 20%

Call 18 103% 119% 127% 130% 65%

Call 19 103% 115% 123% 126% 67%

Call 20 110% 116% 118% 124% 66%

Vix Index 17.88 17.88 17.88 17.88 17.88

Expiration

Type Strike Jun-11 Jul-11 Aug-11 Sep-11 Oct-11

Put 16 69% 63% 58% 51% 44%

Put 17 75% 66% 62% 53% 47%

Put 18 84% 73% 67% 56% 58%

Call 18 87% 72% 66% 51% 65%

Call 19 90% 73% 68% 56% 67%

Call 20 98% 79% 69% 61% 66%

VIX Future 18.40 20.00 21.05 22.40 22.30

Understanding VIX Options

Understanding VIX options

21

Dimensions of VIX optionality

VOV Term Structure (z-axis) & VIX Skew (x-axis)

VO

LATILITY P

AR

AD

IGM

AN

D PA

RA

DO

X

Mar-13

Ap

r-13

May-1

3

Jun

-13

Jul-13

40%

60%

80%

100%

120%

140%

160%

-1.0σ

0.5σ

2.0σ

3.5σ

5.0σ

Maturity

Vo

l of

Vo

l

Moneyness (Sigma)

VIX Volatility Surface

65

75

85

95

105

115

125

135

8 18 28 38 48 58 68 78

Vo

lati

lity

of

the

VIX

(1

m O

pti

on

s)

VIX index

New Regimes of FearVIX index vs. Vol of VIX / SKew of the VIX (Smoothed)

Bull Market (Jan 2006 to Jul 2007)

Credit Crisis Onset (Aug 2007 to Aug 2008)

Market Crash (Sep 2008 to Feb 2009)

Recovery to Flash Crash (Mar 2009 to May 2010)

Post-Flash Crash Steepening (May 10 to Sep 11)

LTRO Steepening Regime (Nov 11 to Mar 12)

QEIII Regime (Sep 12 to Feb 13)

SPATIAL PARADOX – Volatility of Volatility Skew

22

Vol of VIX skew has built in a higher probability of volatility spikes to account for this “wildfire” effect

VO

LATILITY P

AR

AD

IGM

AN

D PA

RA

DO

X

45

55

65

75

85

95

0.08 0.17 0.25 0.33 0.42 0.50

Vo

lati

lity

of

VIX

Fu

ture

s (%

)

Expiration / Terms

New Regimes of FearVolatility of VIX Futures Term Structure / 2006 to 2013

Bull Market Jan 2007 to July 2007)Credit Crisis Onset (Aug 2007 to Aug 2008)Market Crash (Sep 2008 to Feb 2009)Recovery to Flash Crash (Mar 2009 to May 2010)Post-Flash Crash Steepening (May 2010 to Oct 2011)LTRO Steepening (Nov 2011 to Aug 2012)QEIII (Sep2012-Feb2013)

Temporal PARADOX – Volatility of Volatility Term Structure

23

Steepening VIX VOL Term Structure

Volatility of VIX Term Structure has steepened in the “bull market for fear” implying greater futures delta sensitivity to spot-VIX movement

VO

LATILITY P

AR

AD

IGM

AN

D PA

RA

DO

X

60

70

80

90

100

110

120

130

140

Jan-07

Mar-0

7

May-0

7

Jul-07

Sep-0

7

No

v-07

Jan-08

Mar-0

8

May-0

8

Jul-08

Sep-0

8

No

v-08

Jan-09

Mar-0

9

May-0

9

Jul-09

Sep-0

9

No

v-09

Jan-10

Mar-1

0

May-1

0

Jul-10

Sep

-10

No

v-10

Jan-11

Mar-1

1

May-1

1

Jul-11

Sep

-11

No

v-11

Jan-12

Mar-1

2

May-1

2

Jul-12

Sep

-12

No

v-12

Jan-13

Vo

l of

VIX

(%)

Volatility of Volatility Drift

TEMPORAL PARADOX – VOV is falling from highs

24

Nonetheless VVIX has continued to drift lower tempered by the bull market in equities and QEIII – volatility can’t fight the Fed VIX Future Log-Contract Prediction Success/Failure %

Volatility Regime

Within

Prediction

Bound

Greater than

Upper Bound

Less than

Lower Bound

Total

(2006 to Mar 2012)72% 10% 19%

Bull Market

(2006 to July 2007)89% 6% 5%

Credit Crisis Onset (Aug 2007

to Aug 2008)72% 14% 14%

Market Crash

(Sep 2008 to Feb 2009)66% 23% 10%

Recovery to Flash Crash

(Mar 2009 to May 2010)71% 8% 21%

Post-Flash Crash Steepening

(May 2010 to Oct 2011)70% 10% 20%

LTRO Steepening

(Nov 2011 to Mar 2012)41% 0% 59%

8

18

28

38

48

58

68

78

Au

g-06

No

v-06

Feb

-07

May

-07

Au

g-07

No

v-07

Feb

-08

May

-08

Au

g-08

No

v-08

Feb

-09

May

-09

Au

g-09

No

v-09

Feb

-10

May

-10

Au

g-10

No

v-10

Feb

-11

May

-11

Au

g-11

No

v-11

Feb

-12

Futu

re V

IX in

de

x vs

. VO

V R

ange

VIX 1-month Range Implied by VIX Log Contract vs Actual Future VIX2006 to March 2012

30%

40%

50%

60%

70%

80%

90%

100%

110%

Au

g-0

6

Oct

-06

Dec

-06

Feb

-07

Apr

-07

Jun-

07

Au

g-0

7

Oct

-07

Dec

-07

Feb

-08

Apr

-08

Jun-

08

Au

g-0

8

Oct

-08

Dec

-08

Feb

-09

Apr

-09

Jun-

09

Au

g-0

9

Oct

-09

Dec

-09

Feb-

10

Apr

-10

Jun-

10

Au

g-1

0

Oct

-10

Dec

-10

Feb-

11

Apr

-11

Jun-

11

Au

g-1

1

Oct

-11

Dec

-11

Feb-

12

VO

V R

ange

as

% o

f Sp

ot V

IX

High-Low Range of 1m VIX Implied by VIX Log Contract/ Spot VIX2006 to March 2012

45

95

145

195

245

295

Jan

-00

Ap

r-00

Jul-0

0

Oct-0

0

Jan

-01

Ap

r-01

Jul-0

1

Oct-0

1

Jan

-02

Ap

r-02

Jul-0

2

Oct-0

2

Jan

-03

Ap

r-03

Jul-0

3

Oct-0

3

Jan

-04

Ap

r-04

Jul-0

4

Oct-0

4

Jan

-05

Ap

r-05

Jul-0

5

Oct-0

5

Jan

-06

Ap

r-06

Jul-0

6

Oct-0

6

Jan

-07

Ap

r-07

Jul-0

7

Oct-0

7

Jan

-08

Ap

r-08

Jul-0

8

Oct-0

8

Jan

-09

Ap

r-09

Jul-0

9

Oct-0

9

Jan

-10

Ap

r-10

Jul-1

0

Oct-1

0

Jan

-11

Ap

r-11

Jul-1

1

Oct-1

1

Jan

-12

Ap

r-12

Jul-1

2

Vo

l of

Vo

l (%

)

Volatility of Volatility Drift

Realized Vol-of-SPX Realized Vol

Realized Volatility of VIX

Implied Vol-of-VIX (VVIX)

VO

LATILITY P

AR

AD

IGM

AN

D PA

RA

DO

X

65

75

85

95

105

115

125

135

8 18 28 38 48 58 68 78

Vo

lati

lity

of

the

VIX

(1

m O

pti

on

s)

VIX index

New Regimes of FearVIX index vs. Vol of VIX / SKew of the VIX (Smoothed)

Bull Market (Jan 2006 to Jul 2007)

Credit Crisis Onset (Aug 2007 to Aug 2008)

Market Crash (Sep 2008 to Feb 2009)

Recovery to Flash Crash (Mar 2009 to May 2010)

Post-Flash Crash Steepening (May 10 to Sep 11)

LTRO Steepening Regime (Nov 11 to Mar 12)

QEIII Regime (Sep 12 to Feb 13)

VIX Exchange Traded Products vs. Traditional Volatility Strategies

25

VIX ETPs gain in popularity despite muddled performance in comparison to classic volatility strategies

VIX options are priced from VIX futures (not the VIX) hence their volatility increases exponentially as a function of time to expiration and SPX forward skew

Systematic VIX ETN strategies that rely on constant hedging relationships will not track back-tests during changing SPX forward skew regimes due to “shadow” delta drift

VO

LATILITY P

AR

AD

IGM

AN

D PA

RA

DO

X 0.3

0.5

0.7

0.9

1.1

1.3

1.5

1.7

1.9

2.1

De

c-10

Jan

-11

Feb

-11

Ma

r-11

Ap

r-11

Ma

y-11

Jun

-11

Jul-1

1

Au

g-1

1

Sep

-11

Oct-1

1

No

v-11

De

c-11

Jan

-12

Feb

-12

Ma

r-12

Ap

r-12

Ma

y-12

Jun

-12

Jul-1

2

Gro

wth

of

$1

VIX ETPs vs. Traditional SPX Volatility Trades

Dec 2010 to July 2012

Traditional Volatility Trading Volatility ETNs

ATM Long

Straddle

ATM Short

Straddle10% OTM Put VXX VXZ XIV XVIX

Vol Bias Long Vol Short Vol Long Vol Long Vol Long Vol Short Vol Short Vol

Annualized Return -28.62% 31.31% -15.36% -52.35% -26.98% 12.13% -3.61%

Sortino Ratio -1.77x 0.79x -0.61x -1.94x -1.53x 0.18x -0.45x

Sharpe Ratio -1.20x 0.85x -0.45x -1.05x -0.87x 0.16x -0.30x

Return to Drawdown -0.57x 1.10x -0.36x -0.65x -0.58x 0.15x -0.20x

Max Drawdown -50.16% -28.39% -42.87% -80.38% -46.50% -79.53% -18.06%

Note: Prior to 1990 there was not VIX index. We have substituted the CBOE VXO index, the precursor to the VIX, which was available starting in 1986.

26

When the ‘shoeshine boy’ is shorting VIX ETNs maybe it is time to be cautious Front-month VIX futures are increasingly influenced by short squeezes due to rising popularity

of short selling strategies

Shorting the front of the Vol Curve Central Banks are fighting a World War € for the right to unseat the

Japanese Yen as next carry trade king

QE lowers currency volatility (see EUR,GBP,JPY) and increases the correlation between currency strength and risk asset volatility (see USD vs. VIX)

May 2012 VIX spike to 25

Ratio of 1m VIX Future Volatility vs. VIX Volatility (2007 to 2012)

Late 2011 VIX rebound to 30

VO

LATILITY P

AR

AD

IGM

AN

D PA

RA

DO

X

20

12

(VIX

Fut)

20

12

(Vix)

20

11

(Vix)

20

10

(Vix)

20

09

(Vix)

20

08

(Vix)

20

30

40

50

60

70

80

90

100

9:31

AM

9:47

AM

10:0

3 A

M

10

:19

AM

10

:35

AM

10:5

1 A

M

11:0

7 A

M

11

:23

AM

11

:39

AM

11:5

5 A

M

12

:11

PM

12:2

7 PM

12:4

3 PM

12:5

9 PM

1:1

5 P

M

1:3

1 P

M

1:4

7 P

M

2:0

3 P

M

2:1

9 P

M

2:3

5 P

M

2:51

PM

3:07

PM

3:2

3 P

M

3:3

9 P

M

3:5

5 P

M

4:1

1 P

M

Vo

lati

lity

of

VIX

by

Min

ute

(%

an

nu

aliz

ed

)

Volatility of VIX Index vs 1m Vix Future (2012) by Trading Minute

Averages by Year (annualized)

2012 (VIX Fut) 2012 (Vix) 2011 (Vix) 2010 (Vix) 2009 (Vix) 2008 (Vix)

Shorting the front of the Vol Curve

Volatility-of-VIX futures in the last 15 minutes is substantially higher than that of the VIX index itself demonstrating the power of structural flows Volatility-of-volatility microstructure is calmer than at any point




Source: Calculations executed by Artemis Capital Management LLC with data from CQG data factory. Average executed trades by minute.

VO

LATILITY P

AR

AD

IGM

AN

D PA

RA

DO

X


27

VORTEX WISHING WELL Great Vega Short could work if these conditions are always met 1. Asset prices do not crash too far again and; 2. Other debtor-developed nations do not copy the strategy; 3. Taxpayer funded margin or government borrowing is unlimited

Volatility of an Impossible Object

28

Short Roll Yield Vol-of-Vol Timing

Leverage

If these conditions are not met before self-sustaining growth is revived the asymmetrical return distribution of the strategy will result in ruin Traders call this a Martingale process, similar to constantly doubling down your bet while gambling… It works only if your bankroll is unlimited …. so the real question is whether the debtor-developed world has unlimited borrowing capability? Despite higher asset prices little evidence experimental monetary policy is helping the middle and lower class who do not own stocks and do not have access to credit

Flash Crash

(Black Monday 1987, Flash Crash)

Mega-Cycle Crash

(2008 Crash, Great Depression)

Slowly building crash with slow recovery

End of leveraging cycle

High volatility, but relatively muted VOV

Great Depression

Global Recession

Flash Crash


Market Fragmentation & Self-Reflexity

Extreme Volatility of Volatility

Predictable (in retrospect) Unpredictable (even In retrospect)


Market fragmentation and self-reflexity

High volatility of volatility

Evolution of a Volatility Flash Crash (aka Killer Rabbit)

Artificially Low Vol from Monetary Expansion +

Higher potential for Volatility-of-Volatility +

Dangerous Global Macro catalysts +

VIX Derivatives Short Squeeze =

? ?

Short Vol Squeeze Hyper Vol-of-Vol

Self-Reflexivity

VO

LATILITY P

AR

AD

IGM

AN

D PA

RA

DO

X

12

13

14

15

16

17

18

19

20

VIX Month 1 Month 2 Month 3 Month 4 Month 5 Month 6

VIX Futures Curve following Largest VIX % Spikes (VIX < 20 to start)

2004-2013

February 27, 2007 / 50% Vix Log Move

February 25, 2013 / 29% Vix Log Move

March 30, 2006 / 27% Vix Log Move

March 13, 2007 / 26% Vix Log Move13

14

15

16

17

18

19

20

9:3

1 A

M

9:4

9 A

M

10

:07

AM

10

:25

AM

10

:43

AM

11

:01

AM

11

:19

AM

11

:37

AM

11

:55

AM

12

:13

PM

12

:31

PM

12

:49

PM

1:0

7 P

M

1:2

5 P

M

1:4

3 P

M

2:0

1 P

M

2:1

9 P

M

2:3

7 P

M

2:5

5 P

M

3:1

3 P

M

3:3

1 P

M

3:4

9 P

M

4:0

7 P

M

Minute by Minute Performance of VIX and Front Month Future

VIX Jumps 29% (log) on February 25th, 2013

VIX Index

March VIX Future

Shorting the front of the Vol Curve

February 25th 2013 - Volatility Killer Rabbit Volatility-of-volatility microstructure is calmer than at any point




Source: Calculations executed by Artemis Capital Management LLC with data from CQG data factory. Average executed trades by minute.

VO

LATILITY P

AR

AD

IGM

AN

D PA

RA

DO

X


29

10% of future volume in last

minute of trading!

The Next Volatility Regime

30


VIX index Fire Danger is High – greater potential for Vol-of-VIX VIX Derivatives

1. Unbalanced Shadow Delta 2. Unbalanced Shadow Gamma 3. Shadow Theta

VIX Structured Product Inconsistent hedging ratios Vol-of-VIX derivatives driven by structured product flow rather than VIX


Three Possible Macro-VIX regimes for the next decade

I. Bull Market in Fear = New Normal

Post-2008 vol environment of steep term-structure is here to stay

Traders short the front and buy the back but with violent corrections High Implied Correlations, Volatility of Volatility, but low spot-vol

VO

LATILITY P

AR

AD

IGM

AN

D PA

RA

DO

X

II. Bear Market in Fear = Japanization of US Volatility

Positive real rates lead to volatility as fixed income alternative Long-term volatility and skew collapse as investors short rich vol Rise of volatility short sellers builds systemic risk

III. Inflationary Volatility Spiral (Japan moving to this regime)

Runaway inflation actually drives higher volatility

Options skew “flips” to compensate (OTM Calls ↑ Vol) OTM calls re-priced as we all have been hedging the wrong tail

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

6% 7% 8% 9% 10% 11% 12% 13% 14% 15%

Allo

cati

on

fo

r 3

% R

eal

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Nominal Expected Return on Stocks

Optimal Portfolio with Positive Real Rates(Stocks, Bonds, Cash & Vol) / Portfolio Target = 3% real return

Inflation = 3%

Long Volatility (-3% nominal return, -6% real return)Cash (3.5% nominal return, 0.5% real return)Stocks (SPX, 3-15% nominal return, 0-9% real return)Bonds (10yr UST / 6% nominal return, 3% real return)

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Nominal Expected Return on Stocks

Optimal Portfolio in Financial Repression(Stocks, Bonds, Cash & Vol) / Portfolio Target = 3% real return

Inflation = 3%

Long Volatility (-3% nominal return, -6% real)Cash (0% nominal return, -3% real)Stocks (SPX, 3-10% nominal return, 0-7% real)Bonds (10yr UST / 2% nominal return, -1% real)

Bull Market in Fear and Modern Portfolio Theory

Bull Market in Fear is Explained by Markowitz Portfolio Theory

Long volatility exposure extremely valuable to portfolio optimization in financial repression despite substantial negative carry because it hedges forced over-allocation to equity

5-12% is optimal volatility portfolio exposure in negative real interest rate environment!

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Equity %

Long Volatility %

Fixed Income %

Fixed Income %

Cash %

Cash %

Cash %

Post-Modern Volatility can be more than just FEAR

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Volatility is the ultimate post-modern asset for our existential economic future because it protects you from the fracture of the abstraction

Forward Variance

Volatility of Volatility

Volatility

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Visualizing Volatility

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Volatility at World’s End: Two Decades of Movement in Markets is a depiction of real stock market variance using trading data from 1990 to 2011. The visuals are designed from S&P 500 index option data replicating the implied volatility wave (or variance swap curve) extending to an expiration of one year. The front of the volatility wave contains the same data used to calculate the CBOE VIX index. The movement of this wave demonstrates changing trader expectations of future stock market volatility. As the wave moves through time the expected (or implied) volatility surface transforms into a realized volatility surface derived from historical S&P 500 index movement. The transition represents what professional traders call ‘volatility arbitrage’. The color variation in the volatility waves show the volatility-of-volatility or internal movement of the wave. The track underneath the volatility wave represents underlying S&P 500 index prices.

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Christopher Cole, CFA – General Partner and Founder Contact Information Reference Material & Acknowledgements

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Artemis Research:

Volatility of an Impossible Object: Risk, Fear, and Safety in Games of Perception

Volatility at World’s End: Deflation, Hyperinflation and the Alchemy of Risk, March 30, 2012

Fighting Greek Fire with Fire: Volatility Correlation, and Truth, September 30, 2011

Is Volatility Broken? Normalcy Bias and Abnormal Variance, March 30, 2011

The Great Vega Short- volatility, tail risk, and sleeping elephants, January 4, 2011

Unified Risk Theory - Correlation, Vol, M3 and Pineapples, September 30, 2010

Artwork:

"Volatility of an Impossible Object" by Brendan Wiuff / Concept by Christopher Cole 2012 / copyright owned by Artemis Capital Management LLC

“Jack-o-Lantern” Istock photo / used based on purchase of rights

“Ocean Waves” Istock photo / used based on purchase of rights

"Odysseus facing the choice between Scylla and Chrybdis" by Henry Fuseli 1794 / public domain

"Penrose Triangle, Devil’s Turning Fork & Necker’s Cube” Derrick Coetzee / Public Domain

Reference Material:

Kritzman, M. and Y. Li. “Skulls, Financial Turbulence, and Risk Management.” The Financial Analysts Journal, May/June 2010

“Simulacra and Simulation” by Jean Baudrillard / University of Michigan / 1994

"A Tale of Two Indices" by Peter Carr & Liuren Wu December 22, 2005

“VIX Derivatives: A Poor Practitioner’s Model” Maneesh Deshpande / May 19 2011

“Understanding VIX Futures and Options” Dennis Dzekounoff; Futures Magazine/ August 2010

“The Volatility Surface: A Practitioner’s Guide.” Jim Gatheral / John Wiley and Sons, Hoboken, NJ, 2006

"Think Fast and Slow" by Daniel Kahneman / Farrar, Staus and Giroux 2012

“Options, Futures, and Other Derivatives” John C. Hull, Fifth Edition; Prentice Hall 2003

"Lifetime Odds of Death for Selected Causes, United States, 2007" / National Safety Council 2011 Edition

“Volatility Trading” Evan Sinclair, Wiley Trading 2008

"Dying of Money: Lessons of the Great German and American Inflations" by Jens O. Parsson / Wellspring Press 1974

"Economics of Inflation; A Study of Currency Depreciation in Post-War Germany" by Constantino Bresciani-Turroni Out of Print / 1968

“Variance Swaps” Peter Allen, Stephen Einchcomb, Nicolas Granger; JP Morgan Securities / November 2006

"Laughter in the Dark - The Problem of the Volatility Smile" by Emanuel Derman May 26, 2003

“Robust Hedging of Volatility Derivatives” Roger Lee & Peter Carr; Columbia Financial Engineering Seminar / September 2004

“More than you Ever Wanted to Know About Volatility Swaps” Kresimir Demeterfi, Emanual Derman, Michael Kamal & Joseph Zou; Goldman Sachs / March 1999

“The Performance of VIX Option Pricing Models: Empirical Evidence Beyond Simulation” Zhiguang Wang; Florida International University / April 2009

“Recent Developments in VIX Exchange Traded Products” Maneesh Deshpande/ April 3, 2012

"Deflation: making sure 'it' doesn't happen here" by Ben S. Bernanke (speech) / US Federal Reserve November 2002

"US Options Strategy TVIX Explosion Drives Vol-of-Vol Higher" Deutsche Bank February 23, 2012

"Unknown Unknowns: Vol-of-Vol and the Cross Section of Stock Returns" Guido Baltussen, Sjoerd Van Bekkum and Bart Van Der Grient / Erasmus School of Economics & Robeco Quantitative Strategies/ July 30, 2012

Definition of "Impossible Object" / Wikipedia / http://en.wikipedia.org/wiki/Impossible_object

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Christopher Cole, CFA – General Partner and Founder

Artemis Vega Fund L.P. Artemis Capital Management, L.L.C.

520 Broadway, Suite 350 Santa Monica, CA 90401

[email protected] www.artemiscm.com

Christopher Cole, CFA

Managing Partner & Portfolio Manager (310) 496-4526 phone

(310) 496-4527 fax [email protected]

Contact Information Artemis Capital Management – Contact Information

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Christopher Cole, CFA

Managing Partner & Portfolio Manager / Artemis Capital Management LLC

Christopher R. Cole, CFA is the founder of Artemis Capital Management LLC and the portfolio manager of the Artemis Vega Fund LP. Mr. Cole’s core focus is systematic, quantitative, and behavioral based trading of exchange-traded volatility futures and options. His decision to form a fund came after achieving significant proprietary returns during the 2008 financial crash trading volatility futures. His research letters and volatility commentaries have been widely quoted including by publications such as the Financial Times, Bloomberg, International Financing Review, CFA Magazine, and Forbes. He previously worked in capital markets and investment banking at Merrill Lynch. During his career in investment banking and pension consulting he structured over $10 billion in derivatives and debt transactions for many high profile issuers. Mr. Cole holds the Chartered Financial Analyst designation, is an associate member of the NFA, and graduated Magna Cum Laude from the University of Southern California.

Key Information/ Biography

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Legal Disclaimer

THIS IS NOT AN OFFERING OR THE SOLICITATION OF AN OFFER TO PURCHASE AN INTEREST IN ARTEMIS VEGA FUND, L.P. (THE “FUND”). ANY SUCH OFFER OR SOLICITATION WILL ONLY BE MADE TO QUALIFIED INVESTORS BY MEANS OF A CONFIDENTIAL PRIVATE PLACEMENT MEMORANDUM (THE “MEMORANDUM”) AND ONLY IN THOSE JURISDICTIONS WHERE PERMITTED BY LAW. AN INVESTMENT SHOULD ONLY BE MADE AFTER CAREFUL REVIEW OF THE FUND’S MEMORANDUM. THE INFORMATION HEREIN IS QUALIFIED IN ITS ENTIRETY BY THE INFORMATION IN THE MEMORANDUM.

AN INVESTMENT IN THE FUND IS SPECULATIVE AND INVOLVES A HIGH DEGREE OF RISK. OPPORTUNITIES FOR WITHDRAWAL, REDEMPTION AND TRANSFERABILITY OF INTERESTS ARE RESTRICTED, SO INVESTORS MAY NOT HAVE ACCESS TO CAPITAL WHEN IT IS NEEDED. THERE IS NO SECONDARY MARKET FOR THE INTERESTS AND NONE IS EXPECTED TO DEVELOP. NO ASSURANCE CAN BE GIVEN THAT THE INVESTMENT OBJECTIVE WILL BE ACHIEVED OR THAT AN INVESTOR WILL RECEIVE A RETURN OF ALL OR ANY PORTION OF HIS OR HER INVESTMENT IN THE FUND. INVESTMENT RESULTS MAY VARY SUBSTANTIALLY OVER ANY GIVEN TIME PERIOD.

CERTAIN DATA CONTAINED HEREIN IS BASED ON INFORMATION OBTAINED FROM SOURCES BELIEVED TO BE ACCURATE, BUT WE CANNOT GUARANTEE THE ACCURACY OF SUCH INFORMATION.

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volatility paradigm and paradox

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