price asymmetry and basis risk of bm&f bovespa hydrous ethanol future contract.pdf

Proceedings of the Conference on Transnational Corporations and Development in Brazil (2013)

229

Price Asymmetry and Basis Risk of BM&F Bovespa Hydrous

Ethanol Future Contract:

Dynamics of Biofuels Markets in Brazilian South-Center

João Ricardo Tonin, Antonio Augusto de Jesus Godoy,

Julyerme Matheus Tonin and Joilson Dias

Abstract: In the world economic environment, Brazilian strategic position concerning biofuels

can be viewed as highly privileged. This position is achieved, primarily, by combination of

three physical comparative advantages: huge continental extension, fertile soils and tropical

climates, which favors sugar cane plantations. Nevertheless, to remain attractive to foreign

investments for sugar cane industry is necessary that price hedge mechanisms become truly

efficient. In this context, this study’s main goal is to evaluate hydrated ethanol future prices

behavior at BM&F Bovespa, side by side with price behavior at major spot trading locations in

the Brazilian South-Center: Paulínia-SP, Maringá-PR e Ribeirão Preto-SP, through the

identification of an asymmetric transmission process between future and spot prices. This

analysis is based on nonlinear time series models of TAR and M-TAR family; the departure

approach used was cointegration with threshold, firstly developed by Enders and Siklos (2001),

for the May 8th

2010 to December 28th

2012 period. Starting results indicate that ethanol futures

contracts have an asymmetric short-term price transmission mechanism for Ribeirão Preto-SP

and Paulínia-SP, although in a longer-term analysis the effect vanishes. For adjustment speeds,

lesser values were found for Maringá-PR, suggesting that the greater the physical proximity

between spot and future markets, the greater is the integration degree among them.

Keywords: Biofuels, future markets, price asymmetry, TAR model, basis risk

JEL Classification: C53; G14, Q40

1. Introduction

The search for sustainable growth, environmentally sound and economically feasible, in a

context of growing concerns about global warming, again puts ethanol in a prominent position.

The motivation to take advantage of economies of scope arising from the byproducts of sugar

production, coupled with increasing demand with the advent of flex fuel cars have triggered the

growth of ethanol production in Brazil during the 2000s. With the enactment of Law 12.490, of

September 16, 2011, the National Agency of Oil, Natural Gas and Biofuels (ANP) has gained

Price Asymmetry and Basis Risk of BM&F Bovespa Hydrous Ethanol Future Contract

230

control of the biofuels1 industry, thus consolidating the process of deregulation and expanding

importance of private risk management policies, especially those related to agricultural

derivatives.

Aiming to reduce exposure to price risk of the participants in the hydrous ethanol supply chain,

BM&F Bovespa2, through the circular-office 018/2010 launched the future contract of hydrous

ethanol with daily settlement. The delivery point (only for pricing purposes) of the contract

corresponds to the city of Paulínia – São Paulo State. Thus, agents that integrate supply chain of

hydrous ethanol could from now on mitigate their price risk; this could be achieved with

replacement of price adverse oscillations in the physical markets by the sole basis risk.

Nevertheless, if there are arbitrage costs (whether tax, logistical or transaction) the postulates of

Law of One Price (LOP) cannot be applied, reducing the effectiveness of hedging strategy.

Given these conditions, this study aimed to evaluate the behavior of daily future and spot prices

time series for hydrous ethanol in Brazil, at the cities3 of Paulínia – São Paulo State, Maringá –

Paraná State and Ribeirão Preto – São Paulo State, seeking to identify: a. the existence of

asymmetric price transmission; and b. transaction costs involved in the adjustment process

between spot and future markets. Specifically, we intended to calculate the basis between prices

of first expiration future contract open at BM&F Bovespa and the physical spot prices in

Paulínia–SP4, Maringá–PR

5 and Ribeirão Preto–SP. This has been done through an application

of nonlinear time series analysis, which addresses cointegration with threshold adjustment, a

methodology first developed by Enders and Siklos (2001). This model seeks to identify the

magnitudes of price changes during the transmission process between these locations,

contextualizing the performance of the contract vis-à-vis the commodity behavior related to

market structures, from May 18, 2010 to December 28, 2012.

2. Performance of ethanol contracts at BM&F bovespa

Trading of hydrous ethanol started on March 17th

,6 2010, event that was concurrent with a

favorable scenario for the expansion of hydrous ethanol consumption on the market. According

to ANP (2012), in this period hydrous ethanol parity consumer prices stood at 55% of the sold

price of gasoline, below the threshold of 70% deemed to limit the economic viability of its use

as a gasoline substitute. From the start of hydrous ethanol futures trading, the volume of futures

contracts traded had adverse performance compared to that found in the spot market, according

to BM&F Bovespa (2012A). The volume traded increased from 7,642 contracts in the third

quarter of 2010 to 25,957 contracts in the second quarter of 2011. From this period, the traded

volume in the futures market stabilized at around 19,000 contracts per quarter. This corresponds

1 Biofuels are substances derived from renewable biomass, such as biodiesel, ethanol and other substances established by regulation of ANP (BRAZIL, 2011).

2 Brazilian Stock, Mercantile and Futures Exchange.

3 These are the main Brazilian spot markets for hydrous ethanol.

4 Acronym for São Paulo State.

5 Acronym for Paraná State.

6 The ethanol futures contract was launched on 17/05/2010 by the circular letter 018/2010-DP (BM&F Bovespa, 2010).

João Ricardo Tonin, Antonio Augusto de Jesus Godoy, et. al.

231

loosely to the size of the spot market, which showed an average consumption of 2.5 million

cubic meters quarterly.

In this context, one can infer that the growth in the volume of contracts traded in the futures

market by the second quarter of 2011 is mainly linked to the insertion of a new hedging tool.

Moreover, after an adapting period for market participants, the volume in the futures market

tends to stabilize at some average level, following the performance that occurs in the spot

market. Such results show that the physical and futures markets are cointegrated in the long run,

and that the performance of futures market is directly linked to the performance of the spot

market, and vice-versa. This greater integration between the physical (spot) market and the

futures market implies that the variations in the spot market, in the case of hydrous ethanol, are

reflected in the futures market. With the bad weather, very present in the 2011/2012 season,7 the

volatility of daily returns was above average throughout the period, especially between March

2011 and May 2011 (around 20%), higher than the volatility found in the 2010/2011 (11.41%)

and in the 2012/2013 (11.00%). It’s important to highlight that in the period of increased

volatility, the industry went through a supply shortage due to production problems.

With such increase in volatility, it is expected that the ethanol productive chain use hedging

tools in futures/options markets to mitigate risk in the spot market. These characteristics can be

noticed when it’s looked at the hedged percentage of ethanol sales in the domestic market. In

the 2010/2011 season (probably due to poor knowledge of operation mechanisms of the future

contracts), were hedged 9.8% of sales in the period. This amount increased to 25.3% of sales in

the 2011/2012 and 22.7% in the harvest for 2012/2013. The small decrease of

representativeness of the season 2012/2013 is possibly due to the reduction in price volatility

throughout the period.

Regarding commitment of traders in 2010/2011, in average 76.3% of contracts were traded by

non-financial companies, such as: industrial plants (usinas), wholesalers and trading companies.

The remaining share was distributed among the individual agents (21.9%), non-resident

investors (1.3%), institutional investors (1.0%) and banks (0.1%). In the season 2011/2012, we

highlight the growing share of non-financial companies, that have reached a remarkable market

share of 81.4% of the contracts, and the growth of banks' participation in operations (from 0.1%

to 4.0%), demonstrating that there was a growing demand for hedging tools.

Against this background, it is observed that the ethanol direct-related agents seek to mitigate

their risks by widening use of available hedging tools. Financial institutions have expanded

their operations Over-the-Counter (OTC) due to the growth in this market. Briefly describing,

banks offer a credit instrument with relatively-low interest rates, avoiding margin calls and

collateral deposits for the client through the period in which the contract is open. In the next

harvest (2011/2012), participation of the market agents has broadened even more. Operations

made bycorporate non-financial firms have risen to 91.3%, and financial institutions went to

4.9%, reinforcing the agents increase in propensity to use of hedging tools.

7 The sample period comprises three crops of cane sugar, but two of them have targeted periods, and the 2010/2011.

season matches 15/05/2010 to 31/10/2011, the 2011/2012 corresponds to 01/04/2011 to 31/03/2012 and the 2012/2013.

harvest begins on 01/04/2012 and runs through 28/12/2012.


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However there are some regulatory measures, taken by the Brazilian government in periods

when the ethanol supply is restricted, that can negatively affect the liquidity and performance of

ethanol futures contracts. These measures are listed in Law 12.490/20118, and grant ANP the

power to assure that regulated agents are requested to maintain minimum stocks of biofuels,

establishing guarantees and evidence of capacity to meet minimum demand levels for biofuels

market (BRAZIL, 2011). Thus ANP, checking for problems in the productive sector that

endangered the national supply/demand equilibrium, published ANP Resolution967/2011, which

aims to ensure the fulfillment of the demand for anhydrous ethanol generated by the mandatory

blending of gasoline. In this spirit, ANP Resolution 67/2011 enforces the usinas and wholesalers

need to provide proof of anhydrous ethanol stock levels sold in the same period of the last year

(one month in advance of the commercialization month,), as well as the forecast of sanctions for

the two agents involved in the transaction (ANP, 2011).

As result, the production of anhydrous ethanol in Southern-Central Brazil has grown from 7.47

million cubic meters in 2011/2012 sugar cane crop to 8.30 million cubic meters for 2012/201310

,

corresponding to a growth of approximately 11.1% over the period. In contrast, there was a

reduction in the production of hydrous ethanol, leaving 13.08 million m3 in 2011/2012 crop to

12.75 million cubic meters in 2012/2013 crop, representing a reduction of approximately 2.5%

YoY (UNICA, 2012). These changes in the business structure of ethanol favors the

strengthening of trading mechanisms based on the forward market (OTC), mainly off-season,

because market participants concerned about meeting the requirements of the ANP, along with

the reduction of risk exposure, eventually make this kind of contracts for the supply of ethanol.

As previously mentioned in this paper, some activities tend to influence the liquidity of ethanol

futures contracts, and by consequence ethanol futures market consolidation in Brazil.

Concerning this subject, Pennings and Leuthold (1999) highlighted that the presence of price

volatility of the base asset, the level of activity, the size of the commodity spot market and the

degree of product homogeneity tend to increase liquidity for future contracts. Nevertheless,

factors such as market concentration, greater vertical integration and a high degree of

government intervention can cause economic distortions and reduce price risk for underlying

assets, harming or even impairing the consolidation of futures market for the commodity.

Shortly, the performance of ethanol futures contracts at BM&F Bovespa is directly linked to the

performance of the product on the spot market. However, changes in the macroeconomic

framework, jointly with freer markets, can interfere positively in this relation, as well as

negatively when the market has issues such as high concentration, vertical integration of

production and increased government interference in industry. This work shows some of the

most relevant aspects related to the price transmission process from spot market to the futures

market, and vice-versa. It also approaches the concept of basis risk, and how this concept is

embedded in the agricultural commodities market, highlighting key works that addressed the

8 Law No. 12,490 of November 16, 2011 (BRAZIL , 2011).

9 ANP Resolution 67 of 9 December 2011 (ANP, 2011).

10 Forecast of the Crop 2012/2013, Sugar Cane in Brazil South–Center, published on September 20, 2012 (UNICA,

2012).


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issue. Finally, it shows how the TAR and M-TAR models used for the study must be inserted in

an analysis process concerning the ethanol futures and spot markets.

3. Literature review

In the economic system, transaction costs arise on situations that require resources to create,

keep and change structures of market institutions and organizations (FURUBOTN and RICHTER,

2000). These costs are, in general, due to opportunistic behavior, uncertainty, bounded

rationality and governance structure of the market. Following these basic ideas, Fiani (2002)

points out that complex environments, subjected to bounded rationality and uncertainty, imply

in information asymmetry; i.e., different information, provided to the parties involved in a

market transaction, can possibly affect the outcome of the business. These gaps allow market

agents to pursue hedging actions, considering an environment of uncertainty, as well as

opportunist actions who seek to maximize profit.

By consequence, transaction costs in ethanol spot market can distort pricing process in the

futures market, resulting in financial losses to a potential contractor. Moreover, these operations

increase trading risk, while keeping gross expected returns on investment unchanged (what

could possibly reduce speculators’ incentive to participate in the futures market). A final effect

is that, with little or no participation of speculators in trading volume, the overall average

liquidity of the ethanol futures market is reduced. Part of such requirements is contained in the

framework of the Efficient Market Hypothesis (EMH). According to Fama (1970), an efficient

market is one that incorporates instantly and perfectly all known information concerning the

process of price formation11

, and in which no market agent should be able to get abnormal

economic profits using sole publicly available information. In this sense, if market agents are

rational, their expectations about future prices are equal to optimal forecasts that use all

available information.

On this subject, Newbold et al. (1999) argue that futures prices are biased estimators of the

physical (spot) market prices, because the agents involved cannot process in a rational way all

available information, causing the arise of a risk premium that can be fixed or random over time.

However, in longer terms, futures markets tend to be more efficient than in shorter terms, due to

many disturbances of EMH in short time lapses. It is worth of mention, yet on this particular

subject, the works of Moraes, Lima and Melo (2009); Amado et al. (2005); Frees (2009); Neto,

Fraga and Marques (2010); and Perobelli (2005). All these studies pointed out for the existence

of a long-run relationship between spot and future prices of agricultural commodities traded at

BM&F Bovespa. This has demonstrated that, at BM&F Bovespa, agricultural commodity futures

prices are mostly nonbiased estimators of spot market prices.

In this paper, the use of cointegration with threshold adjustment approach, first developed by

Enders and Siklos (2001), will identify the existence of an asymmetric price transmission

mechanism between ethanol spot and futures markets, and subsequently analyze the role of

transaction costs that are involved in the adjustment process between these two markets. This

11 Nowadays this is known as the weak formulation of EMH.


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method, in the current literature, is primarily used for time series of commodity prices on the

spot market, in order to verify if transaction costs can interfere with the process of price

formation of the product at different delivery points. Additionally, is highlighted the work of

Alves and Lima (2010) that, through a TAR model, analyzed the spatial integration of markets

for hydrous and anhydrous ethanol in Brazil, using daily prices time series for the cities of

Ribeirão Preto–SP, Maringá–PR, Paulínia–SP, Maceió–AL and Araçatuba–SP, in a period that

ranged from May 2003 to December 2008. The work of Cunha and Azevedo (2011) sought to

analyze the influence of transaction costs on the behavior of the basis risk, explaining the

difference between daily spot prices for corn at Jatahy–GO and closing prices of corn futures

contracts at BM&F Bovespa. Other work in the agricultural economics field related to the price

asymmetry subject was Cunha and Sousa (2010), which aimed to assess the effects of

incorporating transaction costs on the spatial integration of Northeast melon markets.

Shortly, the existence of transaction costs may cause price transmission mechanism to become

asymmetric, increasing the disparity between different markets for the same commodity. As a

result, it is found that the greater the magnitude of the asymmetry, the lower the degree of

market integration in the short and long term, influencing in a negative way the efficiency of

futures contracts. Another component of price risk, which is inserted into the basic purpose of

hedging with futures contracts by market agents, is the basis volatility and asymmetry between

spot and futures prices. Moreover, from both academic literature and practitioners experience

related to the agricultural futures markets, it is clear that prices at some specific geographical

zone may differ from those at the futures markets. These facts can lead to opportunistic

behavior, uncertainty and bounded rationality among agents in the market. To evaluate the basis

behavior for hydrous ethanol futures and spot markets, this study used an Autoregressive

Moving Averages (ARMA) Model, with its formulation as presented by Hamilton (1994).

According to Leuthold, Junkus and Cordier (1989), the difference between spot and futures

prices of a commodity in a given delivery location, for a given expiration month of the future

contract, is known simply as basis. As the futures prices normally carry some premium over

spot prices (situation known in market jargon as contango), this premium reflects the cost of

carry (in other words, the cost to keep) a physical position (e.g., in inventories) to the expiration

date of the contract; this implies that the basis takes negative values in contango markets.

However, due mainly to instability in demand for commodities, sometimes a positive-basis

situation called backwardation can occur (situation also known among practitioners as "inverted

market"). In backwardation markets, spot prices exceed futures prices.

For Purcell and Koontz (1999), basis behavior is linked to transportation costs, commodities

quality standards, unexpected changes in supply and demand of the product and of its

substitutes, inadequate storage capacity, government interference in pricing, deficiencies in

transport modals (e.g. lack of railways, waterways, even of vicinal roads) or immediate need of

resources by producers, among other factors. The work by Kahl and Curtis (1986) recognize, in

addition, that interest rates and price levels themselves are also determinants of the basis.

Changes in basis can affect expected financial results in futures markets operations, i.e., the

expected result with settling the so-called perfect hedge (theoretical) may not be reached. This


235

means that futures prices may not behave in same way as spot market prices, while a position

taken on the futures market gets closer to its expiration date. To Figlewski (1984), these non-

matched temporal variations of changes in future and spot prices comprise what is known as

basis risk. Thus, economic agents that trade in the futures markets exchange commodities price

risk for sole basis risk. If there exists an asymmetry between spot and future prices, plus a high

basis risk, the main objective of agricultural futures contracts – to reduce or to mitigate the price

risk in commodities trading – will not be achieved, and this can also reduce the efficiency of the

futures contracts market.

4. Methodology

4.1. Data description

The main data sources used for this study correspond to the daily ESALQ/BM&F Bovespa

indicator of hydrous ethanol, basis in Paulínia–SP (ESALQ); the price series of the first

expiration month of the hydrous ethanol future contract at the BM&F Bovespa (venc1); the

hydrous ethanol spot prices for delivery in Maringá–PR (mrga) and Ribeirão Preto–SP (rbro).

The data were obtained from CMA Series 4 trading platform, for the period of May 18th

, 2010

to December 28th

, 2012, comprising 648 daily observations. For the basis analysis (spot and

futures prices difference), series base1, base2 and base3 were created by the authors, and

indicate the respective basis for spot markets at Paulínia–SP, Ribeirão Preto–SP and Maringá–

PR, vis-à-vis the hydrous ethanol future contract of first maturity at BM&F Bovespa. The price

series are measured at Cost, Insurance and Freight (CIF) criteria to each city, and are exempt

from the respective added value state tax rates (ICMS12

).

4.2. Methodology details

4.2.1. TAR and M-TAR models

In conventional analysis of markets integration, there are two cointegration tests to determine

the long-term relationships between the prices in analysis, namely: cointegration tests of Engle

and Granger (1987) and Johansen (1995). The first consists in estimating equation cointegration

between prices by OLS (Ordinary Least Squares), whose bivariate version can be represented by

adjustment of the following equation:

where and indicate the prices prevailing in the markets j and i, respectively, and is the

random error term. Following, one should check for the stationarity of , which can be

detected by means of the autocorrelation function and its resulting correlogram, as well as by

unit root tests. In general, the most used test is the Augmented Dickey–Fuller (ADF) test; the

latter can be expressed as:

12 Imposto Sobre Circulação de Mercadorias e Serviços. This tax rate varies according to the Brazilian state.

(1)


236

If there’s rejection of the null hypothesis (existence of a unit root), prices are cointegrated, i.e.,

there exists integration between markets i and j. However, the critical values of the ADF test

cannot, sometimes, follow the original distribution tabulated by Dickey and Fuller (1979). This

happens because is estimated. In this case, the critical values appropriate to the test can be

found in Engle and Granger (1987) and MacKinnon (1991).

To overcome this restriction on the Engle and Granger (1987) analysis method of market

integration, it should be considered that the difference between market prices j and i is smaller

than the transaction costs. The most appropriate econometric instrument that takes the presence

of nonlinearities and discontinuities in the relationship between the prices into account is

cointegration with threshold. According to Enders and Granger (1998) and Enders and Siklos

(2001), one way to consider the asymmetric adjustment of the model is to specify as a

threshold autoregressive (TAR) process, which is the model that has been used in this work.

Thus, the number of residues characterized in equation (2) can be rewritten as:

where is the number of residues obtained from regression (1); is a dummy variable that

will have unitary value, if and null if in which t is the threshold

parameter; the error term, independent of et, assuming zero mean, constant variance and

no serial autocorrelation. The values of and capture the asymmetrical adjustment, i.e., if

is positive, the adjustment will be given by ; but, if negative, will be captured by

To the above cited authors, when the adjustment path proves to be more persistent in

one direction than in another (high or low markets), the resulting model takes the form of a

momentum-threshold autoregressive (M-TAR) process, which can be written as:

where is a dummy variable which will have unit value if and zero if

For each one of the specified models, the null hypothesis 0, which refers to absence

of cointegration, is tested. To conduct this test, statistics F and values for the TAR and M-

TAR models are used, considering the critical values as tabulated by Enders and Siklos (2001)

and Wane et al. (2004). The critical values for these statistics depend on the sample size and the

number of variables. In this context, the hypothesis of cointegration is not rejected, is also

important to test the hypothesis , that is related to the case of symmetric adjustment.

(2)

(3)

(4)


237

For the model to be identified is required that the residuals of equations (3) and (4) ) are

uncorrelated. To do this, were performed correlation tests for each one of the models. The

models that showed serial correlation in the disturbances were adjusted using lags in the

endogenous variable. Akaike Information Criteria (AIC) and Bayesian Schwartz Criteria (BIC)

were the criteria used for identifying the number of lags suitable. The cointegration analysis of

TAR and M-TAR models, besides including transaction costs in the estimate, allows a check for

speed of the deviation from equilibrium conditions. For this purpose, according to Piggott and

Goodwin (2001), is necessary to calculate half-life. This procedure calculates the time taken for

50% of deviations from the equilibrium to be eliminated, that is, it refers to the average time

required for a given shock to return halfway back from its initial value. The calculation of the

half-life can be done as follows:

As shown, equation 5 states that if there is a price difference between markets that exceeds

transaction costs (threshold), this difference creates an opportunity for short-term gains of

arbitrage, which make fast adjustments. Nevertheless, if the price differential is less than the

transaction costs, adjustments can be slow or even not occur.

4.2.2. Basis forecast

Forecasting a stationary time series is a technique mostly based in ARMA (autoregressive

moving-average) models. An ARMA (p,q) family model comprises the sum of an autoregressive

component, AR(p), and a moving-average component, MA(q). Equation (6) describes an ARMA

model (HAMILTON, 1994):

q

j

jtj

p

i

itit azz11

~~

In which,

tz~ = ARMA (p, q) model;

p

i

iti z1

~ = AR (p) pure model;

q

j

jtja1

= MA (q) pure model.

A necessary condition for identification of the model associated with such stochastic processes

is stationarity of the ARMA (p, q). Stationary of a time series points out that mean, variance and

autocorrelations can be approximated by long enough time-based averages, for a single set of

observations. Is common to check the stationarity of a time series through the application of a

unit root test, for example the Augmented Dickey-Fuller (ADF) test? Rejecting the null

hypothesis of unit root can identify the ARMA (p, q) suitable to forecast, using the criteria of

Box and Jenkins (1984): parsimony, efficiency and predictive estimation outside the sample

(ENDERS, 2010). In this context, to select ARMA (p, q) models to describe the basis in regional

(5)

(6)


238

markets, were used as selection parameters Akaike Information Criteria (AIC), Schwarz

Bayesian Criteria (SBC) and the adjusted R2.

5. Results obtained

Before performing unit root tests, was necessary to determine the number of lags by the Akaike

and Schwartz Information Criteria. The absence of unit root is required to obtain non-

autocorrelated estimated residuals, with a white noise structure. For this purpose, were added

four lags in the estimation for the first and second models, and only one lag for the third model.

The next step is to determine the order of integration using a unit root test. For this purpose, the

test performed was the Augmented Dickey-Fuller (ADF) [Dickey and Fuller (1979)], according

to the procedures described by Enders (2010) and Rao (1994).

As shown in table 1, the ADF test indicates that the prices series for venc1, esalq, mrga and rbro

are stationary in first difference. These results corroborate the initial conditions for the TAR and

M-TAR model estimates, i.e., that the series are integrated of order (1). In addition, concurrently

with the analysis above, the ADF test indicated that the series base1, base2 and base3 are

stationary in level, eliminating the need to add orders of integration for the ARIMA model. To

verify the asymmetry in prices between the futures market of BM&F Bovespa and the main

spot markets for hydrous ethanol in Brazilian Central South, this work estimated three models,

which in turn aim to verify the asymmetry of the central market for the price spot of Paulínia–

SP, (model 1), Ribeirão Preto–SP (model 2) and Maringá–PR (model 3).

Table 1. Augmented Dickey-Fuller (ADF) unit root test.

Série Laga Augmented Dickey-Fuller (ADF)¹ Integração

venc1² 1

-2.850

I(0)

mrga² 5

I(0)

rbro ² 3

I(0)

esalq² 2

I(0)

venc1³ 0 -22.903** 262.270** - - - - I(1)

.mrga³ 0 -23.014** 264.830** - - - - I(1)

rbro³ 2 -15.900** 126.410** - - - - I(1)

esalq³ 1 -9.018*** 40.660*** - - - - I(1)

base1² 1 -6.003** 18.060** - - - - I(0)

base2² 1 -7.169** 25.710** - - - - I(0)

base3² 0 -8.424** 35.490** - - - - I(0)

* Significant at 1%; ** significant at 5%; and ns

not significant. ¹ Critical values for , , and correspond respectively to -3.447, 2.885 and 1.943 at 5%, -4.068, -3.485 and -2.582 at 1% level, while the


239

critical values for and correspond to 6.250, 4.680 and 4.710 at 5% and 8.270, 6.090 and 6.700

at the 1% level, as shown in Dickey and Fuller (1981). ² Variable level, ³ variable in first difference. For

definition of the lag was used the smallest lag indicated by Criterion Akaike or Schwartz. Source:

Research Data.

The first step is to perform a traditional Engler and Granger (1987) cointegration test. This

procedure consists in estimating by Ordinary Least Squares (OLS) a model between the two

markets, extract the residuals, and make the ADF unit root test upon the residuals. If the null

hypothesis that the model residuals have a unit root is rejected, then there exists a long-term

relationship between the variables. As a result, the cointegration equation will check the

response of the spot market prices when there are variations in prices of futures contracts.

Results are shown in table 2.

Table 2. Cointegration relations between the price of the first expiration of the hydrous ethanol at

BM&F Bovespa versus Paulínia–SP, Ribeirão Preto–SP and Maringá–PR spot markets prices

Markets Cointegration equation

R² F calculated

Model 1:

venc1and esalq

0.96 14748.39***

Model 2:

venc1 and rbro

0.95 11436.99***

Model 3:

venc1and mrga

0.92 7034.87***

Note: *** significant at 1%, ** significant at 5%, * significant at 10% and not significant.

Source: Research Results.

Moreover, table 2 discloses that hydrous ethanol futures prices on BM&F Bovespa directly

influence spot prices at Paulínia–SP, Ribeirão Preto–SP and Maringá–PR. The constant

coefficient in the markets was found to be not statistically significant. Following, the ADF test

for residuals of the three estimated models was done. Results are contained in table 3.

Table 3. ADF unit-root test for estimated models residuals.

Models Variable

statistics

Critic value

1% 5% 10%

Residuals of model 1 -8.378*** -3.430 -2.860 -2.570



Note: *** significant at 1%, ** significant at 5%, * significant at 10% and not significant.



240

In short, the cointegration test of Engler and Granger (1987) allow us to infer that the three

analyzed markets keep a long-term relationship between them, i.e., price changes in the central

market tends to directly affect other peripheral markets. This result can be seen in Table 3,

which shows that the three estimated models have originated stationary residuals. To provide

robustness analysis, the Johansen (1995) cointegration test was also performed, seeking to

determine if there exists a cointegrating vector between the analyzed variables. Results of

Johansen test are shown in table 4.

Table 4. Cointegration test of Johansen (1995).

Model Rank Parms LL Egenvalue SBIC HQIC AIC

venc1 and Esalq 0 6 -5174.304 16.080 16.054 16.038

1 9 -5137.648 0.107 15.996* 15.958* 15.934

venc1 and rbro 0 6 -5742.357 17.838 17.813 17.797

1 9 -5696.682 0.132 17.727* 17.688* 17.665

venc1 and mrga 0 6 -5719.763 17.768 17.743 17.727

1 9 -5696.182 0.070 17.725* 17.687* 17.663

* Identification of the cointegration vectors through Information Criterion Schwarz's Bayesian (SBIC),

Akaike's (AIC) and Hannan and Quinn (HQIC).


For the three estimated models, SBC criteria and HQIC (Hannan-Quinn Information Criteria)

indicate that there is at least one cointegration vector among the variables. With these results,

the work of analysis can move forward, checking if there is cointegration between variables

when taking the threshold existence into account. This is done by estimating the TAR and M-

TAR models.

As shown in table 5, all models rejected the null hypothesis that 0, indicating that

the use of the threshold cointegration model is the most adequate. Further, testing for

checks whether the pricing adjustments are equal for positive and negative in short-term

estimation by the TAR model parameters; for a longer period of time, such analysis is

performed by the model M-TAR. As a research result, there was found to exist price asymmetry,

in the short term, for the futures contract traded at BM&F Bovespa with the spot markets in

Paulínia–SP, Ribeirão Preto–SP and Maringá–PR. Nevertheless, this price asymmetry tends to

be mitigated over a longer period of time. To the spot market in Maringá–PR, the presence of

asymmetry both in the short and long term was not detected.

Regarding the speed of price adjustments, as an expected work result, the distance between the

peripheral markets and the main market was a key factor in analysis, i.e., the more far away

from the exchange the spot market is located, more time is required for price adjustment to

occur. The results were estimated in days: 0.33 days (Paulínia–SP), 1.00 day (Ribeirão Preto–

SP) and 2.10 days (Maringá–PR). Notice that for best fitting of the models, the estimate was

made without autocorrelation, and followed the Bayesian (BIC) and Akaike (AIC) selection


241

criteria for the necessary lags. Correspondingly, the analysis is preceded of an identification of

series behavior, i.e., if it is a pure autoregressive process (AR (p)), a pure moving-average

process (MA (q)) or an auto-regressive and moving-average (ARMA (p, q)) process. To

determine which process better represent the series and the order of terms p and q, it is

necessary to analyze the autocorrelation function (ACF) and the partial autocorrelation function

(PACF).

Table 5. Results of of TAR and M-TAR Model Estimates.

Tests Model 1¹ Model 2¹ Model 3¹

TAR M-TAR TAR M-TAR TAR M-TAR

74.282 31.593 29.677 21.700 17.476 16.265

{9.660}*** {9.930}*** {9.64}*** {9.770}***

{9.660}**

* {9.760}***

78.450 0.645 9.158 5.545 1.618 4.284

{9.660}*** {7.47}**

-0.074 -0.157 -0.134 -0.109 -0.096 -0.063

(0.026)*** (0.052)*** (0.032)**

* (0.037)*** (0.024)*** (0.029)**

-0.808 -0.206 -0.363 -0.109 -0.184 -0.148

(0,073)*** (0.028)*** (0.063)**

* (0.037)*** (0.058)*** (0.028)***

Adjustmen

t speed¹ 0.324 - 1.001 - 2.103

F (DWA²)

Prob > F

(DWA²) [0.795] [0.715] [0.104] [0.124] [0.102] [0.144]

AIC 5646 5719 5939 5929 5931 5920

BIC 5664 5737 5962 5956 5949 5942

Observations 646 646 645 644 646 645

Lags 1 1 2 3 1 2

*** Significant at 1%, ** significant at the 5% * significant at 10% and not significant. The figures

in brackets refer to the standard error in brackets refer to the p-value and the brackets, the values of

statistical table and obtained in Wane et al (2004).1 Values in days for the adjustment, calculated

by the method of Goodwin and Piggott (2001).2 Alternative Durbin-Watson test.

Source: Research results.

Settled this framework, by verifying the ACF and PACF functions, the model that presents best

fit for the first application was an ARMA (1, 3). Thus, the coefficient of the autoregressive

parameter of order 1 shows that 82% of the variations of the t-1 period are


242

transmitted to the subsequent period t. The value of the moving-average estimated

coefficient , indicates that every week there is an error in setting this variable that

is based upon the three previous days prices, with a magnitude around -7%. For the second

application, the best-fitting model was an ARMA (1, 1). The coefficient of the autoregressive

parameter of order 1 shows that approximately 86% of the variations in period t-1

are transmitted to the subsequent period t. The value of the estimated moving-average

coefficient -0190) indicates that every week there is an error in setting this variable that is

based on the values of the previous week, with an approximate magnitude of -19%.

Regarding the third application, results were similar to the second application, only with lower

magnitudes. In this model the autoregressive parameter is and moving-average

(table 6).

Table 6. Estimation of the parameters of the univariate ARMA model

Modelo Série Parâmetros¹ Estimativa Erro Padrão AIC BIC Obs

ARMA (1;3)

base1 0.822 (0,017)** 5738.69 5756.58 648

-0.072 (0,024)**

ARMA (1;1)

base2 0.857 (0,016)** 5962.92 5980.81 648

-0.190 (0,055)**

ARMA

(1;1) base3

0.911 (0,011)** 5961.37 5979.27 648

-0.177 (0,022)**

**is rejected the null hypothesis at 1% significance level, * is rejected the null hypothesis at 5%

significance level, not significant. 1 : autoregressive parameter estimated.2 : moving-

average estimated parameter.

Source: Research results.

Therefore, as seen from market models analysis, is possible to forecast basis behavior for

hydrous ethanol. The models have as one of their characteristics an autoregressive component,

which causes most of the variations of ethanol prices to be influenced by its lagged prices in the

very short term. Asides with this fact, ethanol prices also show a moving-average behavior,

implying that economic agents tend to correct their expectations over time. This phenomenon is

well-known in the agricultural markets, because market agents involved take into account a

huge ensemble of information such as weather forecasts, production level, productivity,

inventories levels etc., as well as other factors related to supply and demand, that tend to create

a seasonal component in the price of such products during the harvest epoch.

6. Concluding remarks

Nowadays, the use of financial tools to mitigate market risk is increasingly present in the

commodities market. In Brazilian biofuels industry, because most of hydrous ethanol is to be


243

sold on the domestic market, the creation of BM&F Bovespa hydrous ethanol future contract has

improved the market, which became then even more dynamic. However, due to increased

government regulation and increased market concentration in the industry, the future contract

for ethanol in the BM&F Bovespa is more used to hedging-only operations, what caused the

reduction of individual and foreign investors in contract trading, thus reducing market liquidity

as a consequence.

In this work, the main goal was to analyze the efficiency of the futures contracts, in order to

verify whether there is an asymmetric price transmission process from future market prices for

the main spot markets. As noted, the markets at Paulínia–SP and Ribeirão Preto–SP showed a

process of asymmetric price transmission in the short term, but this was found to be dumped in

the long run. Regarding the speed of adjustment, the basis for Maringá–PR had the lowest

adjustment speed, approximately two days to transfer shocks from future to spot market prices.

Regarding basis risk between spot and futures markets, this risk has an autoregressive

component that allows current prices to be related to past prices, as well as a moving-average

component. The latter reflects the agents’ expectations realignment, based on the information

ensemble inherent to the production process, creating seasonal behavior of prices especially

during harvest periods. The analyzed models suggest that few or even no features in basis

behavior could not be forecasted to some degree, turning the basis risk into a component

possible of being mitigated.

Given this framework, one could expect that a growth in the industry output, reduction of state

intervention (nowadays unlikely to happen) and a more dynamic trading environment for

hydrous ethanol in the coming years could contribute to expand the liquidity of futures contracts

at BM&F Bovespa. In consequence, these factors could certainly allow industry agents to

perform a reduction of the price asymmetry found in this analysis, as well as a better risk

management in this commodity markets.

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About the Authors

João Ricardo Tonin, M. Sc. Student in Economic Theory – Economics Postgraduate Programme (PCE) – State University of Maringá, Brazil (UEM), 5790 Colombo Avenue, Maringá-PR, Antonio Augusto de Jesus Godoy, M. Sc. Student in Economic Theory – Economics Postgraduate Programme (PCE) – State University of Maringá, Brazil (UEM). MBA in Asset Management (EESP-FGV). 5790 Colombo Avenue, Maringá-PR. Julyerme Matheus Tonin, Prof. Msc., Department of Economics, Universidade Estadual de Maringá (UEM), 5790 Colombo Avenue, Maringá-PR. Joilson Dias, Chairholder professor – Economics Department (DCO) – State University of Maringá, Brazil (UEM). Ph. D. in Economics, University of South Carolina (USA), 5790 Colombo Avenue, Maringá-PR.

Contact Information

João Ricardo Tonin, Tel: 55 (44) 3011-4905; E-mail: [email protected]; Antonio Augusto de Jesus Godoy, Tel: 55 (44) 3011-4905; E-mail: [email protected], Julyerme Matheus Tonin, Tel: 55 (44) 3011-5234; E-mail: [email protected]; Joilson Dias, Tel: 55 (44) 3011-4905; E-mail: [email protected].

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