Applied Financial Economics Letters, 2006, 2, 189–192

Hedging or speculation in derivative

markets: the case of energy futures

contracts

Cetin Ciner

Cameron School of Business, University of N. Carolina – Wilmington,

Wilmington, NC 28403, USA

E-mail: cinerc@uncw.edu

This study examines whether hedging or speculation is the principal motive

behind trading in energy futures markets. This question is important since

facilitating risk allocation is considered to be one of the main benefits of

the futures markets, while excess speculation in futures markets could

destabilize the underlying spot market. Studying the linkage between

volume and subsequent price movements leads to the conclusion that

hedgers dominate speculators in all of the markets examined.

I. Introduction

An important benefit of futures markets to society,

along with price discovery, stems from the facilitation

of risk allocation (hedging). While many empirical

studies focus on the accuracy of price discovery, few

papers provide evidence on the relative importance of

hedging versus speculation as the main form of

trading activity in futures markets.1 This bifurcation

is important because futures markets are sometimes

portrayed as forums where informed traders can

fleece unsophisticated investors, leading to regulatory

attempts to control the amount of trading in

futures markets.2 Furthermore, if speculators domi-

nate the futures markets, it could be argued that

futures market trading might destabilize underlying

spot markets. Thus, the amount of risk allocation,

relative to speculation, is important to regulators and

policy makers.Ederington and Lee (2002) report on the first study

that examines who actually trades in a major futures

market. They document the trading activities of the

223 largest traders in the heating oil futures market,

who account for almost 80% of the total trading

volume and open interest. They show that potential

hedgers, defined as traders who have positions on

both spot and futures markets, dominate the trading

activity.The present study further investigates whether

hedging or speculation is more prevalent in energy

futures (crude oil, heating oil and unleaded gasoline)

markets by relying on a relatively new approach

1The main reason why there is little empirical work is data limitation. To determine whether futures markets properlyfacilitate hedging, hedgers need to be segregated from speculators in total market activity. In prior work, researchers, such asWang (2003), de Roon et al. (2000), Chang et al. (2000) and Bessembinder and Senguin (1993), use the commercials versusnon-commercials classification by the Commodity Futures Trading Commission (CFTC) to disaggregate total market activityinto hedgers and speculators.2 Chang et al. (2000) report that over 100 bills have been introduced to lower or even abolish the amount of trading in futuresmarkets, although almost always the attempt was unsuccessful.

Applied Financial Economics Letters ISSN 1744–6546 print/ISSN 1744–6554 online � 2006 Taylor & Francis 189http://www.tandf.co.uk/journalsDOI: 10.1080/17446540500461729

by Llorente, Michaely, Saar and Wang (2002,LMSW).3 Their model, which is discussed furtherbelow, suggests that trading volume on a financialmarket acts as a signal to market observers aboutwhether hedging or speculation is the main motive totrade. They conduct an empirical investigation oftheir model using US stock market data and findsupportive evidence. Moreover, Lucey (2005) andCiner and Karagozoglu (2004) apply the model inIrish and Turkish equity markets, respectively.4

However, this is the first study to focus on thefutures markets within the LMSW framework.

In empirical analysis, the study shows that dayswith high trading volume are followed by pricereversals (negative return autocorrelations) in allthree energy futures contracts, namely, the crudeoil, heating oil and unleaded gasoline futures. Thisfinding suggests that hedging is relatively moreimportant than speculation as the main motive totrade in energy futures markets, which is perfectlyconsistent with the conclusions of Ederington andLee (2002).

II. Background and Hypotheses

LMSW propose a simple equilibrium model toexamine the relation between trading volume andprice movements in asset markets. Their modelsuggests that returns are generated by three separatesources: public information, hedging and speculation.It is assumed that public news causes only a whitenoise component, while returns generated by hedgingand speculation are serially correlated. Hedgingtrades do not reflect new information and theexpected payoff from the asset remains the same.Hence, the asset must be sold at a discount to attractother traders to take the other side of the transaction.Price rises back to its original level in the next period,since the fundamental value is unchanged. In ahedging trade, therefore, an initial negative return isfollowed by a positive return in the second period,generating negative return autocorrelations.

Speculative trades, on the other hand, are causedby the asymmetric information of informedtraders. LMSW argue that private informationwill be only partially incorporated into prices in the

current period and therefore, prices will continue tochange in the same direction in the next period.Consequently, speculative trades generate positivereturn autocorrelations.

Volume has a prominent role in the LMSW model.Specifically, LMSW argue that volume can be usedto distinguish between price changes due to publicinformation and those due to hedging or speculation.Public news is incorporated into prices via normaltrading, while hedging and speculative trades arecharacterized by extensive volume. Hence, as statedin the introduction, the central implication of theLMSW approach is that high volume days will befollowed by price reversals, when hedging is theprimary motive to trade, however, price continua-tions will be observed when speculation is the primarymotive. This proposition can be examined by esti-mating the following equation:

Rt ¼ �þ �0 þ �1Vt�1 þ �2V2t�1 þ �3h

1=2t�1

� �Rt�1 þ ut

ð1Þ

in which Vt denotes log volume series, Rt denotesreturns, calculated as log price differences, and ht�1

is the conditional volatility series obtained from thefollowing GARCH model5:

Rt ¼ "R,t

"R,tj�t�1 � t:dð0, ht, vÞ

ht ¼ �0 þ �1"2R,t�1 þ �2h

2t�1 þ et

in which the residual term "R,t follows a conditionalStudent’s t distribution (t.d) with � degrees offreedom and a conditional variance ht. �t�1 is theinformation set that contains all relevant informationat time t� 1.

The model in Equation 1 is a modified version ofthe regressions in LSMW.6 It measures the inter-action between return autocorrelation and laggedvolume by �1, lagged volume squared by �2, andconditional volatility by �3. Squared volume seriesare included to account for nonlinear relationsbetween return autocorrelations and volume and �3examines linkages between conditional variance andvolume (see, Karpoff (1987) for a survey of volume-volatility linkages).

However, the main coefficient interest in theinvestigation is �1, the measure of interaction between

3The study focuses on the energy futures markets for two main reasons. First, the energy futures markets are among the mostactive and liquid futures markets. Second, the recent work of Ederington and Lee (2002), suggesting that potential hedgersare more active on the heating oil futures market, provides a priori expectations to compare the results of the present study.4 Lucey (2005) argues that the conclusions of LMSW do not obtain on the Irish market, while Ciner and Karagozoglu (2004)find supportive evidence in the Turkish case.5 In a strict sense, returns do not exist in futures markets since there is no initial investment.6 The regression estimated by LMSW does not include a conditional volatility term.

190 C. Ciner

return autocorrelation and lagged volume. If hedgingis relatively more important than speculation on theenergy futures markets, high volume days will befollowed by price reversals and �1 will be negativeand statistically significant. On the other hand, ifspeculation is the primary trading motive, pricecontinuations are expected following high volumedays and �1 will be positive and significant.

III. Data and Findings

The data consist of daily closing prices and tradingvolume for crude oil, heating oil and unleadedgasoline futures contracts traded on the NYMEX.The data span the period between 2 January 1990 and26 December 2001, for a total of 3003 observationsand are obtained from the NYMEX. The closingprices are constructed as continuous series by rollingover nearby contracts, which are typically the mostactive. Returns are calculated as log price differencesand volume is detrended using a 200-day movingaverage component to obtain stationary series.7 Thedetrended volume is calculated as:

Vt ¼ logðVtÞ �1

200

X�1

i¼�200

logðVtþiÞ

in which Vt denotes daily trading volume. Somesummary statistics for daily returns and volume canbe found in Table 1. The findings suggest that energyfutures returns, on average, have zero mean, negativeskewness and excess kurtosis.

Equation 1 is estimated originally by the ordi-nary least squares (OLS). However, the Godfrey(1978) test points to significant autocorrelation in

residual; hence, the error terms are modelled asautoregressive processes and reestimate the regres-sions by the maximum likelihood (ML) method. Lagsof one through five are considered and the appro-priate lag determined for the autoregressive structureby calculating the Godfrey test against white noisealternatives.8

The findings, reported in Table 2, indicate that�1 is negative and statistically significant in all cases,suggesting that days with high trading volume arefollowed by price reversals. This finding implies that,within the context of LMSW, hedging is relativelymore important in energy futures markets, in linewith the arguments of Ederington and Lee (2002).This is also consistent with the overall conclusions ofChang et al. (2000) on stock index futures markets.Furthermore, estimates of �2 suggest significantnonlinearities in the volume-return autocorrelationlinkage for the heating oil futures, as implied by theframework of LMSW, although not for the othercontracts.

IV. Conclusion

Evidence is provided on whether hedging or specu-lation is the principle motive behind trading in energyfutures markets. Within the context of the LMSWmodel, the findings indicate that hedging is moreimportant than speculation as the main motive totrade in energy futures markets, which corroborateresults published in prior work.

This finding is of interest to participants andregulators in futures markets. As pointed out byPashigian (1986) and Stoll (1998), futures markets

7 LMSW also use a 200-day moving average component to detrend daily volume. The analysis is also conducted using a100-day moving average component to detrend the series. The overall findings are qualitatively the same.8 If the test is significant at any lag less than five, but not for greater, then the test is recalculated against an autoregressivestructure of lags higher, up to five. It is found that two autoregressive lags are appropriate for the crude oil and heating oilfutures contracts, while four lags are indicated for the unleaded gas futures contract.

Table 1. Sample summary statistics

Crude oil Heating oil Unleaded gasoline

Returns Volume Returns Volume Returns Volume

Mean �0.00003 0.014 �0.00009 0.014 �0.00003 0.016Std. Deviation 0.023 0.342 0.0275 0.343 0.026 0.314Skewness �1.733 �0.601 �1.529 �0.147 �0.461 �0.215Kurtosis 27.895 1.033 25.352 0.223 11.859 0.358

Note: This table provides descriptive statistics of the data set. The sample covers the periodbetween 2 January 1990 and 26 December 2001, for a total of 3002 observations. The volumeseries are detrended using a 200-day moving average component.

Hedging or speculation in derivative markets 191

are sometimes regarded as forums where informedtraders can fleece unsophisticated investors, justi-fying regulatory attempts to curb trading. The resultsof the present study are not supportive of thisviewpoint. Furthermore, the findings of the presentstudy are against the notion that increased volumein futures markets could destabilize the underlyingspot market.

Acknowledgements

I wish to thank Harlan Platt, Dan Rogers andAhmet Karagozoglu for helpful comments. The usualdisclaimer applies.

References

Bessembinder, H. and Senguin, P. J. (1993) Price volatility,trading volume, and market depth: evidence fromfutures markets, Journal of Financial and QualitativeAnalysis, 28, 2015–34.

Chang, E., Chou, R. Y. and Nelling, E. F. (2000) Marketvolatility and demand for hedging in stock indexfutures, Journal of Futures Markets, 20, 105–25.

Ciner, C. and Karagozoglu, A. (2004) Informationasymmetry, speculation and foreign trading activity:emerging market evidence, paper presented at EasternFinance Association Meetings, Norfolk, VA.

de Roon, R. A., Nijman, T. E. and Veld, C. (2000) Hedgingpressure effects in futures markets, Journal of Finance,55, 1437–57.

Ederington, L. and Lee, J. H. (2002) Who trades futuresand how: evidence from the heating oil futures market,Journal of Business, 75, 353–73.

Godfrey, L. G. (1978) Testing against general autoregres-sive and moving average error models when theregressors include lagged dependent variables,Econometrica, 46, 1293–301.

Karpoff, J. (1987) The relation between price changes andtrading volume: a survey, Journal of Financial andQuantitative Analysis, 22, 109–26.

Llorente, G., Michaely, R., Saar, G. and Wang, J. (2002)Dynamic volume-return relation of individual stocks,Review of Financial Studies, 15, 1005–47.

Lucey, B. (2005) Speculation or hedging in the Irish stockmarket, Applied Financial Economics Letters, 1, 9–14.

Pashigian, P. B. (1986) The political economy of futuresmarket regulation, Journal of Business, 59, 55–84.

Stoll, H. R. (1998) Regulation of financial markets: afocused approach, Multinational Finance Journal, 2,87–99.

Wang, C. (2003) The behavior and performance of majortypes of futures traders, Journal of Futures Markets,23, 1–31.

Table 2. Regression results

Crude oil Heating oil Unleaded gasoline

� �0.0002 �0.0003 �0.0001(0.63) (0.50) (0.70)

�1 �0.144 �0.256 �0.218(0.01) (0.00) (0.00)

�2 �0.134 �0.432 0.228(0.17) (0.00) (0.07)

�3 �0.509 �5.138 �3.474(0.64) (0.00) (0.27)

�1 0.368 �0.282 �0.461(0.00) (0.00) (0.00)

�2 �0.192 �0.106 �0.206(0.00) (0.00) (0.00)

�3 – – �0.133(0.00)

�4 – – �0.074(0.00)

Note: The following regression is estimated by the maximum likelihood method.

Rt ¼ �þ �0 þ �1Vt�1 þ �2V2t�1 þ �3h

1=2t�1

� �Rt�1 þ ut þ

Xni¼1

�iut�i

Two lags are found to be appropriate for crude and heating oil futures contracts in the movingaverage component, while four lags are indicated for the unleaded gasoline contract.

192 C. Ciner