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 dominate 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
1
The main reason why there is little empirical work is data limitation. To determine whether futures markets properly
facilitate hedging, hedgers need to be segregated from speculators in total market activity. In prior work, researchers, such as
Wang (2003), de Roon et al. (2000), Chang et al. (2000) and Bessembinder and Senguin (1993), use the commercials versus
non-commercials classification by the Commodity Futures Trading Commission (CFTC) to disaggregate total market activity
into 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 futures
markets, although almost always the attempt was unsuccessful.
Applied Financial Economics Letters ISSN 1744–6546 print/ISSN 1744–6554 online ß 2006 Taylor & Francis
http://www.tandf.co.uk/journals
DOI: 10.1080/17446540500461729
189
C. Ciner
190
by Llorente, Michaely, Saar and Wang (2002,
LMSW).3 Their model, which is discussed further
below, suggests that trading volume on a financial
market acts as a signal to market observers about
whether hedging or speculation is the main motive to
trade. They conduct an empirical investigation of
their model using US stock market data and find
supportive evidence. Moreover, Lucey (2005) and
Ciner and Karagozoglu (2004) apply the model in
Irish and Turkish equity markets, respectively.4
However, this is the first study to focus on the
futures markets within the LMSW framework.
In empirical analysis, the study shows that days
with high trading volume are followed by price
reversals (negative return autocorrelations) in all
three energy futures contracts, namely, the crude
oil, heating oil and unleaded gasoline futures. This
finding suggests that hedging is relatively more
important than speculation as the main motive to
trade in energy futures markets, which is perfectly
consistent with the conclusions of Ederington and
Lee (2002).
II. Background and Hypotheses
LMSW propose a simple equilibrium model to
examine the relation between trading volume and
price movements in asset markets. Their model
suggests that returns are generated by three separate
sources: public information, hedging and speculation.
It is assumed that public news causes only a white
noise component, while returns generated by hedging
and speculation are serially correlated. Hedging
trades do not reflect new information and the
expected payoff from the asset remains the same.
Hence, the asset must be sold at a discount to attract
other 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 a
hedging trade, therefore, an initial negative return is
followed by a positive return in the second period,
generating negative return autocorrelations.
Speculative trades, on the other hand, are caused
by the asymmetric information of informed
traders. LMSW argue that private information
will be only partially incorporated into prices in the
current period and therefore, prices will continue to
change in the same direction in the next period.
Consequently, speculative trades generate positive
return autocorrelations.
Volume has a prominent role in the LMSW model.
Specifically, LMSW argue that volume can be used
to distinguish between price changes due to public
information and those due to hedging or speculation.
Public news is incorporated into prices via normal
trading, while hedging and speculative trades are
characterized by extensive volume. Hence, as stated
in the introduction, the central implication of the
LMSW approach is that high volume days will be
followed by price reversals, when hedging is the
primary motive to trade, however, price continuations will be observed when speculation is the primary
motive. This proposition can be examined by estimating the following equation:
Rt ¼ þ 0 þ 1 Vt1 þ 2 V2t1 þ 3 h1=2
t1 Rt1 þ ut
ð1Þ
in which Vt denotes log volume series, Rt denotes
returns, calculated as log price differences, and ht1
is the conditional volatility series obtained from the
following GARCH model5:
Rt ¼ "R,t
"R,t j
t1 t:dð0, ht , vÞ
ht ¼ 0 þ 1 "2R,t1 þ 2 h2t1 þ et
in which the residual term "R,t follows a conditional
Student’s t distribution (t.d) with degrees of
freedom and a conditional variance ht. t1 is the
information set that contains all relevant information
at time t 1.
The model in Equation 1 is a modified version of
the regressions in LSMW.6 It measures the interaction between return autocorrelation and lagged
volume by 1, lagged volume squared by 2, and
conditional volatility by 3. Squared volume series
are included to account for nonlinear relations
between return autocorrelations and volume and 3
examines linkages between conditional variance and
volume (see, Karpoff (1987) for a survey of volumevolatility linkages).
However, the main coefficient interest in the
investigation is 1, the measure of interaction between
3
The study focuses on the energy futures markets for two main reasons. First, the energy futures markets are among the most
active and liquid futures markets. Second, the recent work of Ederington and Lee (2002), suggesting that potential hedgers
are 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.
Hedging or speculation in derivative markets
191
Table 1. Sample summary statistics
Crude oil
Mean
Std. Deviation
Skewness
Kurtosis
Heating oil
Unleaded gasoline
Returns
Volume
Returns
Volume
Returns
Volume
0.00003
0.023
1.733
27.895
0.014
0.342
0.601
1.033
0.00009
0.0275
1.529
25.352
0.014
0.343
0.147
0.223
0.00003
0.026
0.461
11.859
0.016
0.314
0.215
0.358
Note: This table provides descriptive statistics of the data set. The sample covers the period
between 2 January 1990 and 26 December 2001, for a total of 3002 observations. The volume
series are detrended using a 200-day moving average component.
return autocorrelation and lagged volume. If hedging
is relatively more important than speculation on the
energy futures markets, high volume days will be
followed by price reversals and 1 will be negative
and statistically significant. On the other hand, if
speculation is the primary trading motive, price
continuations are expected following high volume
days and 1 will be positive and significant.
III. Data and Findings
The data consist of daily closing prices and trading
volume for crude oil, heating oil and unleaded
gasoline futures contracts traded on the NYMEX.
The data span the period between 2 January 1990 and
26 December 2001, for a total of 3003 observations
and are obtained from the NYMEX. The closing
prices are constructed as continuous series by rolling
over nearby contracts, which are typically the most
active. Returns are calculated as log price differences
and volume is detrended using a 200-day moving
average component to obtain stationary series.7 The
detrended volume is calculated as:
Vt ¼ logðVt Þ 1
X
1
logðVtþi Þ
200 i¼200
in which Vt denotes daily trading volume. Some
summary statistics for daily returns and volume can
be found in Table 1. The findings suggest that energy
futures returns, on average, have zero mean, negative
skewness and excess kurtosis.
Equation 1 is estimated originally by the ordinary least squares (OLS). However, the Godfrey
(1978) test points to significant autocorrelation in
residual; hence, the error terms are modelled as
autoregressive processes and reestimate the regressions by the maximum likelihood (ML) method. Lags
of one through five are considered and the appropriate lag determined for the autoregressive structure
by calculating the Godfrey test against white noise
alternatives.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 are
followed by price reversals. This finding implies that,
within the context of LMSW, hedging is relatively
more important in energy futures markets, in line
with the arguments of Ederington and Lee (2002).
This is also consistent with the overall conclusions of
Chang et al. (2000) on stock index futures markets.
Furthermore, estimates of 2 suggest significant
nonlinearities in the volume-return autocorrelation
linkage for the heating oil futures, as implied by the
framework of LMSW, although not for the other
contracts.
IV. Conclusion
Evidence is provided on whether hedging or speculation is the principle motive behind trading in energy
futures markets. Within the context of the LMSW
model, the findings indicate that hedging is more
important than speculation as the main motive to
trade in energy futures markets, which corroborate
results published in prior work.
This finding is of interest to participants and
regulators in futures markets. As pointed out by
Pashigian (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 a
100-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 autoregressive
structure of lags higher, up to five. It is found that two autoregressive lags are appropriate for the crude oil and heating oil
futures contracts, while four lags are indicated for the unleaded gas futures contract.
C. Ciner
192
Table 2. Regression results
Crude oil
Heating oil
Unleaded gasoline
0.0002
(0.63)
0.0003
(0.50)
0.0001
(0.70)
1
0.144
(0.01)
0.256
(0.00)
0.218
(0.00)
2
0.134
(0.17)
0.432
(0.00)
0.228
(0.07)
3
0.509
(0.64)
5.138
(0.00)
3.474
(0.27)
1
0.368
(0.00)
0.282
(0.00)
0.461
(0.00)
2
0.192
(0.00)
0.106
(0.00)
0.206
(0.00)
3
–
–
0.133
(0.00)
4
–
–
0.074
(0.00)
Note: The following regression is estimated by the maximum likelihood method.
n
X
Rt ¼ þ 0 þ 1 Vt1 þ 2 V2t1 þ 3 h1=2
i uti
t1 Rt1 þ ut þ
i¼1
Two lags are found to be appropriate for crude and heating oil futures contracts in the moving
average component, while four lags are indicated for the unleaded gasoline contract.
are sometimes regarded as forums where informed
traders can fleece unsophisticated investors, justifying regulatory attempts to curb trading. The results
of the present study are not supportive of this
viewpoint. Furthermore, the findings of the present
study are against the notion that increased volume
in futures markets could destabilize the underlying
spot market.
Acknowledgements
I wish to thank Harlan Platt, Dan Rogers and
Ahmet Karagozoglu for helpful comments. The usual
disclaimer applies.
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