paper v. physical oil

Paper oil and physical oil: has speculativepressure in oil futures increased volatility inspot oil prices?

Babajide Fowowe

Lecturer, Department of Economics, University of Ibadan, Ibadan, Nigeria. Email:[email protected]

Abstract

A number of authors have attributed the high and volatile oil prices experienced since the turn ofthe 21st century to increased speculative activities arising from a relaxation of regulations infutures markets. This study examined the effects of speculative pressure on the volatility of spot oilprices. I constructed two measures of speculative pressure and modelled the volatility in oil returnsby using the GARCH autoregressive conditional jump intensity model of Chan and Maheu, whichmodels the effects of extreme news events in returns. Empirical results showed a significant posi-tive coefficient for speculative pressure, implying that increased speculative pressure has contrib-uted to volatile oil prices. This result is robust to different GARCH estimators and measures ofspeculative pressure.

1. Introduction

The 21st century has been marked by unprecedentedly high and volatile oil prices. InJanuary 2000, the price of oil was $25/b; it remained relatively stable until September2003, when it started rising, and this surge continued until it reached a peak of $145/b inJuly 2008. The oil price subsequently plummeted, and by December 2008, it was less than$40/b. The onset of the global financial crisis has been identified as the brake on the bullrun that the oil price experienced.

A number of authors have attributed the recent high oil prices to increased speculativeactivities in oil futures. Medlock and Jaffe (2009) noted that there has been an increasednumber of speculators in the oil futures markets since the 1990s, and not only has theirnumber increased, but they have accounted for a greater proportion of activity in the US oilfutures markets than physical players in the oil industry in recent years (p. 3). Medlock andJaffe (2009) also noted that the market presence of non-commercial traders increased byover 15-fold, while the market position of commercial traders doubled, and that thisincrease in activities on the futures markets has been identified as a major cause of the highand volatile oil prices by both industry players and the academia. This increase in specula-

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tive activities has been largely attributed to the Commodity Futures Modernization Act(CFMA) of 2000, which relaxed regulation in futures markets in the United States. TheCFMA clarifies that certain over-the-counter (OTC) derivatives transactions (includingthose involving oil) are outside of the jurisdiction of the Commodity Futures TradingCommission (CFTC).1 This created a series of ‘loopholes’which created opportunities forsome derivatives transactions (including those involving oil) to be conducted withoutregulatory oversight from the CFTC.2 The ‘Enron loophole’ arose because the CFMAstipulated that OTC derivatives trading in some ‘exempt commodities’ would not besubject to CFTC regulation, with oil being on the ‘exempt’ list. The ‘London loophole’refers to the case where exchanges outside the United States can offer energy futures con-tracts in the United States, but are not subject to regulatory oversight by the CFTC. Thespecific example relates to ICE Futures Europe, which operates from London and whoseWest Texas Intermediate (WTI) futures contract is not subject to CFTC regulation. The‘Swaps loophole’ arose because the CFMA exempted swaps transactions from positionlimits, and institutional investors have exploited this loophole to take larger positions thanthey would be able to do if they traded directly on exchanges, where they might be con-strained by speculative position limits (Jickling and Cunningham, 2008, p. CRS-8;Medlock and Jaffe, 2009, p. 9).

The Organization of the Petroleum Exporting Countries (OPEC) has been a loud voiceblaming the high and volatile oil prices on speculative activity in the oil futures markets.OPEC (2007) published the findings from a joint EU–OPEC workshop held in 2006 on theimpact of financial markets on the price of crude oil. The workshop found that while anumber of factors, such as demand growth (especially from China and the United States),a lack of refining capacity, low investment in crude production and political instability,were responsible for the recent increase in oil prices, speculation had been the primarycause of the increased volatility of oil prices (p. 43). OPEC (2009) also reported the con-clusions from another EU–OPEC workshop held in 2009.The workshop noted the positiveimpacts of oil futures on the oil price, such as price discovery, eliminating payment risk,allowing different expectations and information on participants’ part to be incorporatedinto prices, and providing a cost-effective hedge (p. 25). However, the negative effects ofthe futures markets on oil prices, which were manifested in increased volatility, were alsoenumerated. Such negative consequences of financial markets include the occurrence oflarge intraday price volatility, super-spike-type expectations and price dynamics in futuresmarkets that exaggerated high price expectations (p. 26). OPEC (2011) maintained thatthere was sufficient supply in the oil market and that the high prices seen in the early part of2011 of about $100/b represented a risk premium of between $15 and $20, primarily as aresult of speculative activities (p. 7).

This study conducts an empirical analysis of the effects of speculation on the volatilityof oil prices. Oil price dynamics have been shown to exhibit high volatility, jumps

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and random variation (Askari and Krichene, 2008; Gronwald, 2009) and generalisedautoregressive conditional heteroscedasticity (GARCH)-type models have been used tomodel variables that exhibit these types of characteristics (Francq and Zakoian, 2010).However, many GARCH models only account for smooth persistent changes in volatilityand do not capture the discrete jumps in asset returns (Chan and Maheu, 2002; Maheu andMcCurdy, 2004). In order to model this abnormal information, jump models have beenincorporated into GARCH models to give GARCH–jump mixture models.

We have employed the GARCH autoregressive conditional jump intensity (ARJI)model of Chan and Maheu (2002) and Maheu and McCurdy (2004) in order to model thevolatility of oil prices while taking account of discrete jumps in the prices. To the best ofour knowledge, we are not aware of any other study that has incorporated jump dynamicsinto a GARCH model to examine the effects of speculation on oil prices. Our results willtherefore offer particularly new and interesting insights into the role speculators play inaffecting the volatility of crude oil prices.

The rest of the paper is organised as follows: the next section conducts a review ofempirical literature into the effects of futures markets on the underlying spot market, whilesection 3 presents the data and methodology. Section 4 contains the results of the empiricaltests, and the final section concludes.

2. Literature review

Considerable research has been conducted into the role futures markets play in affectingeither prices or the volatility of the underlying spot market. These studies can be broadlyclassified into two types: those that examine futures in commodities and those thatexamine futures in stock indexes. What is observed from the literature is that majority ofthe early studies were conducted for commodities. Foster (1994) provides a review of earlyliterature, finding that out of 14 studies published between 1901 and 1991, 10 were studiesconcerned with futures in commodities. However, in recent times, the majority of studieshave focused on futures in stock indexes, and some such studies include those by Antoniouand Holmes (1995), Chang et al. (1999), Board et al. (2001), Darrat et al. (2002), Baeet al. (2004), Kasman and Kasman (2008), and Bohl et al. (2011). Some recent studiessuch as those by Morgan (1999) and Chatrath and Song (1999) have examined the effect offutures markets on commodity spot markets. The primary focus of this study is on energyfutures, and studies that have focused on energy futures include those by Foster (1994),Fleming and Ostdiek (1999), Sanders et al. (2004), and Du et al. (2009). Below, we sum-marise the studies conducted for stock markets and other commodities and provide adetailed review of studies conducted for energy markets.

In summary, for the studies conducted on stock indexes, the studies that found thatfutures trading increased the volatility of the underlying stock index were Antoniou and

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Holmes (1995) for the FTSE-100 stock index, Chang et al. (1999) for the Nikkei stockindex, and Bae et al. (2004) for the Korean stock price index. The studies that found anegative effect of futures trading on the volatility of the underlying stock index were Boardet al. (2001) for the FTSE-100, Darrat et al. (2002) for the S&P 500 stock index, andKasman and Kasman (2008) for the Turkish stock market. Bohl et al. (2011) found noeffect of futures trading on the Polish Stock Index.

For the studies conducted for commodity markets, Morgan (1999) showed that the spotprice volatility of potatoes was less pronounced after the establishment of the LondonPotatoes Futures Market. Chatrath and Song (1999) examined the effect of futures tradingon cash market volatility for five agricultural products—wheat, oats, soybeans, corn andcotton—and the results showed that speculative commitments were negatively related tovolatility, thus offering conclusions contrary to the belief that the participation of specula-tors results in more volatile markets.

Fleming and Ostdiek (1999) examined the effects of the introduction of energy deriva-tives on the crude oil market. The authors focused on the WTI crude oil market and con-ducted two separate analyses. First, they examined the effects of the introduction of oilfutures on crude oil volatility using weekly data over the period from 5 February 1982 to26 December 1997; they then examined the relationship between futures trading activityand spot market volatility using daily data over the period from 1 September 1983 to 31December 1997. The authors used a stochastic volatility model to examine how the intro-duction of crude oil futures has affected the structure of crude oil volatility. Estimationresults showed that volatility increased following the introduction of crude oil futures. Theincrease was prominent over the first three to four weeks. Results for the introduction ofother energy derivatives showed no significant effect on crude oil volatility. Results for theexamination of the effects of futures trading on market depth and liquidity showed thatvarious components of trading volume had a significantly positive effect on spot marketvolatility. Conversely, various components of open interest had a significantly negativeeffect on spot market volatility.

Sanders et al. (2004) examined the impact of traders’ positions in energy futuresmarkets on energy futures returns. The authors used weekly data over the period from 6October 1992 to 28 December 1999. The results of Granger causality tests showed thatreturns led all traders’ positions. Specifically, positive futures returns were found to leadan increase in the net long position of non-commercials, while conversely, positive futuresreturns were found to lead a decrease in the net long position of commercials. For non-reporting traders, returns still led net long positions, but the directional impact was mixed.Positive returns led to net buying by non-commercials but to net selling by commercials.Further results showed that trader positions do not Granger-cause returns.

Foster (1994) examined the effects of the onset of trading in crude oil futures on spotprice volatility. The author used both daily and weekly data over the period from January



1986 to July 1990 for Brent crude oil. Including a dummy variable in a GARCH modelshowed an insignificant coefficient for the dummy variable, thus suggesting that the size ofspot market volatility had not been significantly affected by the introduction of futurestrading.The author then proceeded to estimate GARCH models using first weekly and thendaily data for pre-futures and post-futures trading periods. The results showed that forweekly data, the overall magnitude of volatility had not undergone significant change, butthe nature of volatility had been significantly transformed. The author concluded that thisimplied that the market had developed a stronger response to new information, which wouldbeamanifestationof themarket functioningmoreefficiently, and thus, futuresmarketshavea price discovery function. For the daily data, it was found that the market appeared to be lessefficient.The author concluded that the spot market appeared to have become more efficienton a weekly basis, with the efficiency gains being generated by the actions of speculatorsbrought by the futures markets, while at the same time, inefficiencies arose on a daily basis.It was speculation that supported the efficiency improvements at the weekly level, as theshort-run noise effects did not persist, but information improvements emerged.

Du et al. (2009) examined the impact of speculation on crude oil price volatility. Theauthors used weekly data on crude oil futures prices over the period from 16 November1998 to 26 January 2009 and adopted the stochastic volatility with Merton jump in return(SVMJ) model. The authors used an index of speculation that was constructed as the ratioof non-commercial positions to total positions in futures. Empirical results showed thatspeculation in oil futures significantly increased oil price volatility.

3. Data and methodology

3.1. DataThis paper makes use of the Commitments of Traders (COT) reports from the CFTC todevelop a measure of speculative pressure. The COT reports are published every Friday,and they contain data on traders’ futures markets positions for the preceding Tuesday. TheCOT reports classify traders into three categories: commercial, non-commercial and non-reportable traders. Commercial traders are those traders who use futures contracts in theparticular commodity for hedging purposes, and by the CFTC regulations, this means thatthey are commercially engaged in business activities hedged by the use of the futures oroptions markets. Commercial traders are generally referred to as hedgers because they areusually producers and consumers who trade in futures to offset the risk of prices movingunfavourably for their ongoing business activities. Non-commercial traders are thosetraders who trade in futures for reasons other than to hedge their exposure to market risk.Non-commercial traders are generally speculators who are not involved in physical deliv-ery of the commodity; rather, they are financial players who seek profits by taking marketpositions to gain from changes in the commodity price. Commercial and non-commercial

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traders are grouped as reporting traders because they control positions that exceed thereporting threshold and, as such, are required by the CFTC to reveal their actual positionson a daily basis. Reporting levels are adjusted periodically by the CFTC, and the reportablepositions constitute about 70 to 90 per cent of the total open interest in any market(Commodity Futures and Trading Commission, 2012a).

The third group of traders are classified as non-reportable traders, also termed ‘smalltraders’because their positions fall below the level required for reporting. Thus, there is noinformation concerning their motives for taking up positions in the futures markets.3

This study makes use of the data for non-commercial traders or speculators, as pro-vided in the COT reports, to develop a measure of speculative pressure. Some limitationsof the data must be addressed. Firstly, because of speculative position limits placed on non-commercials, some large traders might be inclined to classify themselves as commercials(Sanders et al., 2004; Tornell and Yuan, 2012). Therefore, true hedging positions are asubset of all commercial traders’ positions (Sanders et al., 2004). Secondly, there is noway of knowing the motives of non-reporting traders (Sanders et al., 2004; Tornell andYuan, 2012). In addition to this, the data contained in COT reports underestimate the truesize of open interest in crude oil because they exclude the positions that are negotiated offthe exchange, primarily in swap deals (Parsons, 2010). Finally, some swap dealers engagein some commercial activities, while some commercial traders are also engaged in swapsactivity, and because CFTC staff classify traders based on the information in their Form40s, some human error might creep into the classification (Commodity Futures andTrading Commission, 2012b).

Despite these limitations, the data contained in COT reports still represent the bestavailable data with which to examine the effects of futures markets on the underlying spotmarket. In particular, for our purpose, which is to examine speculative pressure, the datalimitations do not pose serious problems for our estimations. This is because there areno obvious incentives to self-classify as a speculator, and therefore, reporting non-commercials likely represent a relatively pure subset of total speculative positions, and thisis probably the most precise classification (Sanders et al., 2004, p. 431).

The specific data that are used are the commitments of traders in WTI futures on theNewYork Mercantile Exchange (NYMEX), and we construct two variables that will serveas proxies of speculation.

Firstly, following de Roon et al. (2000) and Sanders et al. (2004), we develop a specu-lative pressure variable that is the per cent net long position of speculators. This variablecaptures the net position of the average trader under CFTC classification (Sanders et al.,2004) and is defined as follows:

SPNCL NCS

NCL NCS NCSPt

t t

t t t

=−

+ + ( )2(1)



where SP = speculative pressure, NCL = non-commercial long position, NCS = non-commercial short position and NCSP = non-commercial spreading position.

We follow Sanders et al. (2004) in defining our second measure of speculation, andthis second variable is the percentage of total open interest held by speculators, defined asfollows:

STOINCL NCS NCSP

TOIt

t t t

t

=+ + ( )[ ]

( )2

2(2)

where NCL, NCS and NCSP are as defined previously, STOI = speculation as percentageof total open interest and TOI = total open interest.

The data on oil prices are from the Energy Information Administration, and we makeuse of the spot price of WTI.Although these data are available on both a daily and a weeklybasis, weekly data are used because the data from the COT reports are available only on aweekly basis. We calculate returns in oil prices as follows:

WTIR WTI WTIt t t= −( )−100 1ln ln (3)

where WTIR = returns in oil prices and WTI = spot price of WTI.Although the COT reports are available from 1986, weekly publications started in

1992. This influenced our study period, and thus we made use of weekly data over theperiod from 30 September 1992 to 27 December 2011.

3.2. MethodologyThis paper examines the impact of speculative pressure on volatility of crude oil prices.GARCH-type models have been the preferred tool for modelling volatility of time seriessince the ARCH and GARCH models were proposed by Engle (1982) and Bollerslev(1986), respectively. Chan and Maheu (2002) and Maheu and McCurdy (2004) positedthat many GARCH models only capture the effects of normal information on asset returnsand fail to examine the effects of extreme news or abnormal information and that suchextreme news events cause infrequent, unanticipated, and large price movements that arereferred to as jumps (Kao et al., 2011). Chan and Maheu (2002) and Maheu and McCurdy(2004) developed the ARJI model, which postulates that jump intensity follows anautoregressive moving average (ARMA) process and incorporates the GARCH effectof the returns series. Thus, jumps are incorporated into the GARCH model to give aGARCH–jump mixture model—the GARCH–ARJI (GARJI) model. This study makesuse of the GARJI model of Chan and Maheu (2002) and Maheu and McCurdy (2004) toestimate the effects of speculative pressure on oil prices; it is summarised below.

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Given a set of returns at time t − 1 and the two stochastic innovations ε1,t and ε2,t, thetime-series model of returns (Rt) can be written as:

R Rt i t ii

p

t t= + + +−=∑μ φ ε ε

11 2, , (4)

where ε1,t, which is a mean-zero innovation with a normal stochastic process, is assumed tobe obtainable as follows:

ε ω β α ε11

2

1

0 1, , ,t where NID and= ( ) = + +−=

−=

∑ ∑h z z h ht t t t i t ii

p

j t jj

q

∼ (5)

and

ε π2 1, ,t t kk

nt= =∑ (6)

where ε2,t denotes a jump innovation assigned to be a conditionally mean zero, and ε1,t iscontemporaneously independent of ε2,t, while ∑ =k

nt k

t1π , is the jump component affecting

returns from t − 1 to t.The conditional jump size πt,k is assumed to be independent and nor-mally distributed with a mean θ and a variance δ2. The variable nt denotes the discretecounting process governing the number of jumps that arrive between t − 1 and t, which isdistributed as a Poisson random variable with the parameter nt > 0.

Equation (4) shows that the return series includes the normal stochastic process. Thestochastic jump process is assumed to have a Poisson distribution with a time-varying con-ditional intensity parameter λt, which, conditional on Ωt−1, is assumed to describe thearrival of a discrete number of jumps, where nt ∈{0,1,2 . . .}, over interval [t − 1,t]. Theconditional density of nt is as follows:

P n je

jjt t

t tj

=( ) = =−

−

Ω 1 0 1 2λ λ

!, , , …

The conditional jump intensity λt denotes the expected number of jumps conditionalon the information set Ωt−1, which is parameterised as

λ λ ρλ γζt t i t j= + +− −0 (7)

Following the exclusion of all jumps occurring during a single unit interval, the condi-tional probability density function can be expressed as



f R f R n j P n jt t t t t t tj

Ω Ω Ω− − −=

∞

( ) = =( ) =( )∑1 1 10

, (8)

4. Empirical analysis

4.1. Summary statisticsSummary statistics of the variables of interest are presented in Table 1. Four variables areconsidered: the spot price of WTI, returns of WTI, speculative pressure and speculationpercentage of total open interest. The first part of the table contains statistics over the fullperiod, and it can be seen from the variance of WTI that oil prices have been very volatile.This is not surprising, as the minimum oil price over this period is US$11/b, while themaximum is US$142.50/b. Figures for returns of oil prices show that returns of oil priceshave on average been positive over the study period. The kurtosis coefficient is positive

Table 1 Summary statistics

WTI WTI RETURNS SP STOI

Full Sample (2 October 1992–23 December 2011)Minimum 11.000 −19.234 −0.546 0.059Maximum 142.520 25.125 0.726 0.449Mean 42.668 0.152 0.053 0.232Variance 820.595 18.546 0.033 0.015Skewness 1.043 [0.000] −0.256 [0.000] −0.089 [0.248] 0.416 [0.000]Kurtosis 3.150 [0.332] 5.859 [0.000] 4.081 [0.000] 2.323 [0.000]Jarque–Bera 183.102 [0.000] 352.453 [0.000] 50.263 [0.000] 102.282 [0.000]

Pre-CFMA (2 October 1992–29 December 2000)Minimum 11.000 −13.849 −0.546 0.059Maximum 35.910 16.289 0.726 0.196Mean 20.124 0.046 0.079 0.119Variance 24.572 16.173 0.058 0.001Skewness 1.000 [0.000] 0.143 [0.228] −0.128 [0.278] 0.486 [0.000]Kurtosis 3.935 [0.000] 4.492 [0.000] 3.414 [0.082] 3.187 [0.433]Jarque–Bera 87.557 [0.000] 41.325 [0.000] 4.266 [0.118] 17.601 [0.000]

Post-CFMA (5 January 2001–23 December 2011)Minimum 18.280 −19.234 −0.421 0.099Maximum 142.520 25.125 0.247 0.449Mean 59.625 0.231 0.033 0.318Variance 749.424 20.344 0.013 0.009Skewness 0.475 [0.000] −0.477 [0.000] −1.458 [0.000] −0.270 [0.008]Kurtosis 3.443 [0.031] 6.471 [0.000] 5.692 [0.000] 2.192 [0.000]Jarque–Bera 26.200 [0.000] 309.405 [0.000] 375.996 [0.000] 40.908 [0.000]

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and statistically significant, showing that the distribution is leptokurtic, thereby implyingthat the distribution of returns has larger, thicker tails than the normal distribution. In addi-tion to this, the Jarque–Bera statistic for oil returns is statistically significant, which indi-cates the returns series follow a non-normal distribution. All these statistics indicate thepresence of ARCH effects and support our use of GARCH models. The second panel ofTable 1 considers the pre-CFMA period, while the third panel provides summary statisticsfor the post-CFMA period. It is seen that the period following the passing of the CFMAhas been a period of very volatile oil prices, as the minimum and maximum oil prices areUS$18.28/b and US$142.52/b, respectively. Corresponding values for the period beforethe CFMA are US$11.00/b and US$35.91/b, respectively. In addition to this, the varianceof oil prices post-CFMA is 749.42, which is very much bigger than 24.57, which is thevariance for the pre-CFMA period.

4.2. Empirical resultsThe results of estimating the GARJI models are presented in Tables 2 and 3. For robust-ness purposes, in addition to the GARJI model, we also estimated a constant-intensityjump model as proposed by Jorion (1988). For the constant-intensity jump model (Jorion,1988), we have imposed the restrictions of a constant jump intensity (λt = λ0), while the

Table 2 Estimation results of GARJI model using SP

Constant-intensity jump model GARJI model

Coefficient Standard error Coefficient Standard error

μ 0.100 0.129 0.117* 0.063φ1 0.119*** 0.031 0.108*** 0.023φ2 −0.126*** 0.030 −0.109*** 0.021κ 2.140*** 0.599 2.220*** 0.515ω 0.739** 0.309 0.729*** 0.008α 0.074*** 0.016 0.050*** 0.0009β 0.866*** 0.032 0.891*** 0.005θ −1.637 2.241 −2.830*** 1.093δ2 7.799*** 2.151 7.906*** 1.007λ0 0.031*** 0.013 0.017*** 0.0003ρ — — 0.707*** 0.008γ — — 1.428*** 0.278Function value −2789.167 −2786.536

* Significance at the 10% level; ** significance at the 5% level; *** significance at the 1% level.



GARJI model (Chan and Maheu, 2002; Maheu and McCurdy, 2004) allows λt to evolveaccording to a parsimonious ARMA structure.

The estimated models take the following form:

WTIR WTIR WTIR SPt t t t t= + + + + +− −μ ϕ ϕ κ ε ε1 1 2 2 1 2, , (9)

WTIR WTIR WTIR STOIt t t t t= + + + + +− −μ ϕ ϕ κ ε ε1 1 2 2 1 2, , (10)

where

ε1 0 1, , ,t t t th z z= ( )∼ NID

h ht i t ii

p

j t jj

q

= + +−=

−=

∑ ∑ω β α ε1

2

1

ε π2 1, ,t t kk

nt= =∑

λ λ ρλ γζt t i t j= + +− −0

Table 3 Estimation results of GARJI model using STOI

Constant-intensity jump model GARJI model


μ −0.137 0.238 −0.101 0.227φ1 0.140*** 0.030 0.146*** 0.031φ2 −0.106*** 0.032 −0.097*** 0.032κ 1.627* 0.931 1.589* 0.925ω 0.722** 0.297 0.596*** 0.221α 0.074*** 0.016 0.026** 0.011β 0.868*** 0.031 0.921*** 0.022θ −1.777 2.516 −1.926 1.074δ2 8.147*** 2.452 6.930* 1.481λ0 0.028 0.020 0.015** 0.007ρ — — 0.878*** 0.082γ — — 0.758* 0.434Function value −2793.739 −2788.363


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Table 2 contains the results when the variable measuring speculative pressure is SP,which is the percent net long position of speculators, while Table 3 contains the resultswhen the variable measuring speculative pressure is the speculation percentage of totalopen interest (STOI).

It is seen fromTable 2 that the GARCH effects (ω, α, β) for WTI returns are all positiveand highly significant, thereby implying that a strong GARCH effect exists. The param-eters α and β sum up to 0.94 and 0.941 in the constant-intensity and GARJI models respec-tively, thus implying that there is persistence in the conditional variance.

Regarding the coefficient of SP, this coefficient is positive and highly significant at the1 per cent level in both models. The implication of this result is that increased pressurefrom speculators resulted in increased volatility of oil prices over the study period. Thisresult is in line with the result of Fleming and Ostdiek (1999), who found that the introduc-tion of oil futures increased the volatility of oil prices, and it also supports the results of Duet al. (2009), who found that speculation increased crude oil price volatility.

The jump size mean (θ) and jump variance (δ2) are both statistically significant, thusconfirming persistence in the return fluctuations. The negative value of the jump meanimplies that the jump behaviour incited by abnormal information tends to lead to a nega-tive effect on returns. The positive sign on the jump variance signifies that the volatilityincited by abnormal information tends to increase the return volatility.

Turning to the parameters for jump intensity (λ0, ρ, γ), it is seen that these parametersare all significant at the 1 per cent level, thus confirming the intensity of jumps. The sig-nificance of the jump parameter λ0 implies that there exists jump behaviour for oil pricereturns whenever there is abnormal information. The significance of the parameters ρ andγ confirms the existence of time-varying jumps on the arrival of news events. Thus, theprobability of jumps incited by abnormal information will vary over time.

The results of estimation of the GARJI model using STOI as the measure of specula-tive pressure are presented in Table 3. Most of the results are similar to what was obtainedfrom Table 2. The coefficient on STOI is positive and significant at the 10 per cent level,thus confirming the result from Table 2 that speculative pressure has increased volatility inoil prices.

We find that the GARCH parameters (ω, α, β) are still significant, and the jump sizemean and variance are also significant, in Table 3. Finally, we see that the jump intensityparameters (λ0, ρ, γ) are all significant and positive and that the conclusion from Table 3still holds, that is, the probability of jumps incited by abnormal information will varyover time.

4.3. Robustness testsWe have conducted some sensitivity analyses to test the robustness of our results tochanges in the estimation techniques and variables used to measure speculative pressure.



Firstly, we have estimated the effects of speculative pressure as measured by SP andSTOI using GARCH(1,1) and GARCH(1,1)-M (GARCH-in-mean) models, and theresults are presented in Table 4. The mean equations for the GARCH and GARCH-Mequations, respectively, are

WTIR t t tSP= + +ψ θ υ (11)

and

WTIREXRET STOIt t t t= + + +ψ θ ζσ υ2 (12)

The variance equation for both models is

σ ϖ α υ α σt t p t q2

12

22= + +− − (13)

We obtain similar results to those obtained using the GARJI estimation, as it is seenthat speculative pressure as measured by SP has a highly significant positive coefficient.Again, the coefficient on STOI is only significant in the GARCH(1,1) model at the 10per cent level and is insignificant in the GARCH(1,1)-M model.

We next test the robustness of our results by constructing new measures of speculativepressure that add the figures for the non-reporting group. This group of traders have beenreferred to as small speculators by some authors, such as Sanders et al. (2004), and foreach measure of speculative pressure of non-commercial traders obtained previously, thatis, SP and STOI, we have added the corresponding measures for non-reporting traders or

Table 4 Estimation results of GARCH(1,1), GARCH(1,1)-M models

Speculative variable = SP Speculative variable = STOI

GARCH (1,1) GARCH (1,1)-M GARCH (1,1) GARCH (1,1)-M

Mean equationψ 0.039 (0.143) −0.361 (0.307) −0.183 (0.253) −0.392 (0.365)θ 2.361*** (0.694) 2.425*** (0.652) 1.693* (0.985) 1.593 (1.013)ζ — 0.025 (0.018) — 0.014 (0.018)

Variance equationω 0.939*** (0.331) 0.932*** (0.327) 0.899*** (0.312) 0.893*** (0.308)α 0.071*** (0.015) 0.070*** (0.015) 0.070*** (0.014) 0.069*** (0.015)β 0.874*** (0.027) 0.875*** (0.027) 0.878*** (0.025) 0.879*** (0.025)


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‘small speculators’. This gives us two new variables—SPTOTAL and STOITOTAL,which, following Sanders et al. (2004) and de Roon et al. (2000), are derived as follows:

SPTOTALNCL NCS

NCL NCS NCSP

NRL NRS

NRL NRS=

−+ + ( )

+−+

t t

t t t

t t

t t2

where SPTOTAL = total of speculative pressure of ‘large speculators’ and ‘small specula-tors’, NRL = non-reporting traders’ long position, and NRS = non-reporting traders shortposition.

STOITOTALNCL NCS NCSP

TOI

NRL NRS

TOI=

+ + ( )[ ]( )

++

( )t t t

t

t t

t

2

2 2

Estimation results using these ‘total speculative pressure’ variables are presented inTables 5 and 6. Table 5 contains the results from the GARCH(1,1) AND GARCH(1,1)-Mestimations, while Table 6 contains the results from GARJI estimations. We find thatspeculative pressure as measured by SPTOTAL is significant and positive at the 1 per centlevel in both Tables 5 and 6, while STOITOTAL is weakly significantly positive at the 10per cent level. These results corroborate our previous results and confirm that speculativepressure in oil futures has increased volatility in crude oil prices.

5. Conclusion

This study investigated the relationship between speculative pressure and volatility of oilprices. Following de Roon et al. (2000) and Sanders et al. (2004), we constructed two

Table 5 Estimation results of GARCH(1,1), GARCH(1,1)-M models

Speculative variable = SPTOTAL Speculative variable = STOITOTAL

GARCH(1,1) GARCH(1,1)-M GARCH(1,1) GARCH(1,1)-M

Mean equationψ 0.084 (0.130) −0.291 (0.308) −0.902 (0.653) −1.183 (0.690)θ 1.641*** (0.464) 1.680*** (0.463) 3.045* (1.771) 3.036* (1.697)ζ 0.024 (0.018) 0.018 (0.017)

Variance equationω 0.930*** (0.321) 0.925*** (0.336) 0.902*** (0.328) 0.895*** (0.277)α 0.071*** (0.015) 0.071*** (0.015) 0.071*** (0.014) 0.071*** (0.013)β 0.875*** (0.027) 0.875*** (0.029) 0.877*** (0.027) 0.877*** (0.022)




measures of speculative pressure from data provided in the COTS reports from the CFTC.We employed the GARCH autoregressive conditional jump intensity model of Chan andMaheu (2002) and Maheu and McCurdy (2004) in order to model the effects of extremenews events in oil price returns. The empirical results showed that increased speculativepressure has led to increased volatility of oil prices. This result implies that speculatorshave played an important role in destabilising the oil market.

Notes

1. Established in 1974, the Commodities Futures Trading Commission is the primary regulatingbody for the futures markets in the United States.

2. Jickling and Cunningham (2008) provide a comprehensive review of the ‘loopholes’.3. Despite this fact, this group of traders are regularly referred to as small speculators (Sanders

et al., 2004).

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Table 6 Estimation results of GARJI models using SPTOTAL

Speculative variable = SPTOTAL Speculative variable = STOITOTAL

GARJI model GARJI model


μ 0.171 0.117 −0.268 0.637φ1 0.128*** 0.032 0.149*** 0.031φ2 −0.119*** 0.032 −0.095*** 0.032κ 1.540*** 0.456 1.456* 0.759ω 0.599** 0.248 0.603** 0.243α 0.027** 0.013 0.026** 0.012β 0.919*** 0.028 0.920*** 0.025θ −1.938* 1.144 −1.896* 1.089δ2 6.971*** 1.438 6.999*** 1.487λ0 0.009 0.006 0.009 0.006ρ 0.876*** 0.076 0.879*** 0.074γ 0.713* 0.428 0.750* 0.428Function value −2784.424 −2789.318


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