February 7, 2007 

Market Growth, Trader Participation and Pricing in Energy Futures Markets 

Michael S. Haigh Jeffrey H. Harris James A. Overdahl Michel A. Robe* 

Abstract 

 We use a unique dataset on futures trader positions to document major 

changes in the size and term structure of the U.S. crude oil futures market. 

We show that, as recently as 2000 trading activity in this market was heavily 

concentrated at the near end of the maturity spectrum. Since then, overall 

open interest has grown two-fold, with trader activity at the back end of the 

maturity spectrum increasing over twice as much as the market as a whole. 

The market growth in long-term (>3 years) positions generally started in 

2004, which coincides with the growth in participation by commodity swap 

dealers in the futures markets. An analysis of the composition of traders 

participating in the market shows that almost all large-trader categories 

(commodity swap dealers and arbitrageurs; hedge funds; commercial dealers; 

and commercial producers) now carry aggregate net positions in long-term 

contracts comparable in magnitude to the size of their net positions in shortterm (<3 months) contracts prior to 2003. Amidst this market growth, the 

prices of one-year and two-year futures became co-integrated with the price of 

the near-month futures for the first time in 2004. We provide evidence that 

the pricing convergence is linked to the growth in futures trading by 

commodity swap dealers and arbitrageurs. Our results have significant 

implications for those interested in the effectiveness of hedges constructed 

with long-term crude oil futures contracts and for those interested in the 

quality of information contained in futures prices across the term structure. 

Keywords: Energy futures, Maturity Spectrum, Trader categories, Evolution. 

JEL codes: G10, G13, L89 

* Haigh: Associate Chief Economist, U.S. Commodity Futures Trading Commission (CFTC), 1155 21st Street, NW, 

Washington, DC 20581. Tel: (+1) 202-418-5063. Email: mhaigh@cftc.gov. Harris: CFTC and Alfred Lerner 

College of Business and Economics at the University of Delaware, 320 Purnell Hall, Newark, DE 19716. Tel: (+1) 

302-831-1812. Email: harrisj@lerner.udel.edu. Overdahl: Chief Economist, U.S. Commodity Futures Trading 

Commission, 1155 21st Street, NW, Washington, DC 20581. Email: joverdahl@cftc.gov. Tel: (+1) 202-418-5165. 

Robe: CFTC and Kogod School of Business at American University, 4400 Massachusetts Avenue NW, 

Washington, DC 20016. Tel: 202-885-1880. Email: mrobe@american.edu. Special thanks to Steven Cho for his 

help with the Large Traders Report data and for many useful comments. Thanks also to David Bessler, Andrei 

Kirilenko, David Reiffen, and participants at the OPEC workshop on the Impact of Financial Markets on the Price 

of Oil (Vienna) and to a seminar at the CFTC for helpful comments. This paper reflects the opinions of its authors 

only, and not those of the U.S. Commodity Futures Trading Commission, the Commissioners, or any other staff 

upon the Commission. All remaining errors and omissions, if any, are the authors’ sole responsibility. 
 1

I. Introduction

 Various types of hedgers and speculators interact in derivatives markets. Floor brokers 

for example, may take positions, process information, and discover prices in these markets while 

serving clients. In addition, arbitrageurs in derivatives markets may also serve to alleviate price 

discrepancies and to improve the transfer of risk amongst investors.1 As derivatives markets 

continue to expand, a natural question is to what extent changes in the magnitude and the 

composition of trading affect pricing. In this paper, we use a unique dataset of New York 

Mercantile Exchange (NYMEX) trader positions to document the significant growth in West 

Texas Intermediate (WTI) crude oil futures market since 2000.2 We utilize proprietary details 

about trader positions from the Commodities Futures Trading Commission’s (CFTC) Large 

Trader Reporting System (LTRS) to show that this growth can be traced to a diverse set of trader 

types.3

 We find that increased participation by dealer/merchants, hedge funds and especially 

commodity swap dealers and arbitrageurs contribute significantly to improved price efficiency in 

one- and two-year contracts. In this regard, we demonstrate how increased participation by both 

commercial and non-commercial traders can enhance market quality in commodity markets. 

The NYMEX crude oil futures market has grown steadily this century across all futures 

expiration dates. In nearby contracts (those expiring within three months) where price discovery 

is centered, daily net positions have grown by 145% from early 2000 through mid-2006. Growth 

has been even more dramatic in long-dated contracts (those expiring in three years or more), 

exceeding 262% over this same time frame. Contracts for six or more years did not exist prior to 

1999. As recently as 2000, the crude oil futures market was relatively illiquid at the far end, with 

open interest in long-dated contracts amounting to less than 4.5% of total open interest. For most 

categories of traders, however, we find that growth in the long-dated market has now made the 

size of daily net positions in long-dated futures comparable in magnitude to the size of the 

nearby market in 2000. 

 

1

 See Basak and Croitoru (2006). 

2

 These contracts comprise the world's largest-volume futures market on a physical commodity (Source: Nymex). 3

 The CFTC provides historical public data in weekly Commitment of Traders (COT) Reports which aggregate these 

trader types into broad “Commercial” and “Noncommercial” traders. Each of these groups are quite heterogeneous 

and these reports do not break down activity by different maturities. On January 5, 2007 the CFTC began 

publishing COT—Supplemental reports which also include aggregate futures and options positions of Index 

Traders, but only for 12 agricultural commodities. 
 2

 Our uniquely detailed position data allow us to examine how various types of traders 

contribute to the development of both nearby and long-dated markets. We show that these 

markets have experienced greater participation from nearly every type of trader at all term 

horizons. In nearby contracts, commodity swap dealers have accounted for a major part of 

growth, with floor brokers, dealer/merchants and manufacturers also contributing significant 

proportions. In long-dated contracts, growth stems from an even wider set of participants with 

hedge funds, commodity swap dealers, floor brokers, dealer/merchants and producers each 

contributing substantially. 

 Notably, this period of significant market growth coincides with changes in the 

fundamental relation between futures prices at different maturities as well. More specifically, we 

find that prices of nearby, one- and two-year futures contracts first become statistically 

significantly co-integrated in July, 2004 and have remained significantly linked through mid2006 (the end of our study period). We provide evidence that this pricing convergence is linked 

to the growth in the market activity of dealer/merchants, hedge funds and commodity swap 

dealers in the one- and two-year contracts. For example, we attribute the growth in hedge fund 

trading, which is mainly in the form of calendar spread trading, to be significantly responsible 

for the pricing convergence.4

 We hypothesize that dealer/merchants, hedge funds and commodity 

swap dealers convey information from clients and swap markets to the futures market and 

provide competition for order flow in these contracts which enhances price discovery and 

linkages between these market prices. 

The development of price co-integration across contracts could allow traders to hedge 

and arbitrage across futures contracts more effectively. The significance of this development 

cannot be overstated, since the lack of derivative market integration can lead to poor market 

liquidity and detrimental outcomes for traders who might rely on its existence for price discovery 

and hedging purposes. For instance, in the early 1990s, when the vast majority of exchange-

 

4

 For example, according to the CFTC’s January 23rd 2007 COT report spread trading by Non-Commercial traders – 

a category dominated by hedge funds (see Haigh, Hraniova and Overdahl (2007)) accounted for 28.4% of all open 

positions, whereas outright long and outright short trading by the Non-Commericals contributed just 8.4% and 6.5% 

of total open interest respectively. 
 3

traded crude oil futures had a maturity of less than four months, Metallgesellschaft AG lost over 

$1.3 billion while executing a hedging strategy that went awry. Contributing to these losses was 

the company’s inability to hedge long-term oil delivery commitments with a matching-maturity 

portfolio of futures contracts and its inability to efficiently roll over large short-term positions 

without experiencing large price impacts.5

 

 As the Metallgesellschaft case illustrates, limitations in the maturity structure of 

commodity futures markets can be a significant impediment to implementing effective hedging 

strategies. Therefore, our results document that the growth in dealer/merchants, hedge fund and 

commodity swap dealer participation during the past few years marks an important milestone in 

the development of longer-term futures markets. Furthermore, our results provide empirical 

support for the notion that greater participation by specific types of traders, such as arbitrageurs, 

help to alleviate price discrepancies as modeled by Basak and Croitoru (1996). 

 Our results linking market development to the participation of specific traders is 

important for academics who have written at length about the market impact of different types of 

traders, risk management and speculative strategies, price discovery, and commodity forward 

pricing curves. We show that specific trader types, particularly financial traders who may have 

little vested interest in the underlying commodity, can add important dimensions toward 

integrating derivative markets and making markets in general more informationally efficient. 

The fact that increased arbitrageur and swap dealer activity explains much of the convergence in 

nearby, one- and two-year futures prices complements evidence in Roll et al. (2006) that market 

liquidity is related to the efficacy of arbitrage activities. 

 Our results are also crucial for policy makers, who look to futures prices as reliable 

indicators of market expectations when implementing monetary and fiscal policies. We show 

that futures prices at various contract horizons can display varying degrees of reliability, 

depending not only on overall market activity but also on the types of trader types taking 

positions in the contracts. Policy makers also make decisions that affect the development of 

 

5

 See Culp and Miller (1995) and Marthinsen (2005) for discussions of Metallgesellschaft AG’s energy-derivatives 

trading. 
 4

derivatives markets.6

 Our results provide evidence that longer-term futures contracts have been 

developing organically in the competitive market. Our results also demonstrate a strong link 

between the trading positions taken by specific categories of traders and the price linkages 

between futures contracts. 

 The remainder of the paper proceeds as follows. Section II places our contribution within 

the related academic literature. Section III describes the data. Section IV documents significant 

changes in the term structure of the crude oil futures market since 2000. Section V analyzes the 

impact of those structural changes on futures pricing. Section VI concludes. 

II. Related Work 

 A number of papers have used the publicly-available CFTC’s Commitment of Traders 

(COT) reports, mostly to shed some light on speculative and hedging activity in futures markets.7

 

Our analysis highlights two limitations of these reports. First, they are highly aggregated, in that 

they only differentiate between two broad categories of traders (“commercial” vs. “noncommercial” categories) that are rather heterogeneous. In the case of WTI crude oil futures, the 

“commercial trader” group includes such diverse participants as commodity swap dealers, oil 

manufacturers, oil producers, and commercial dealers. One would expect these various subcategories to hold different types of positions – and our analysis confirms this intuition. Most 

strikingly, as we illustrate later in the paper, whereas the commercial category as a whole is 

typically net short in near-months (<3 months) oil futures, we document that one of the 

commercial sub-categories (commodity swaps dealers) almost always holds a net long position 

in near-month contracts. In the first half of 2006, for instance, commercial traders as a whole 

were on average net short (-) 56,540 contracts. This aggregate figure, however, was the sum of a 

net long position of (+) 90,140 for commodity swaps dealers and a short net position of (-) 

146,680 contracts for all the other commercial sub-categories. Second, the publicly available 

reports do not break down activity at different maturities, which obscures possible differential 

 

6

 As evidence of policy maker interest, we note that the United States Congress has held a number of hearings in the 

last two years on the topics of energy prices and volatility in oil markets. Likewise, the International Monetary Fund 

(IMF) has been devoting entire chapters of its high-profile, semi-annual World Economic Outlook to the 

implications of increasing energy prices and volatility for the macroeconomy (IMF (2005, 2006)). 7

 See, e.g., Hartzmark (1987, 1991); Bessembinder (1992); Leuthold, Garcia & Lu (1994); Chang, Chou & Nelling 

(2000); de Roon, Nijman & Veld (2000); Wang (2003); Piazzesi & Swanson (2006); and references cited therein. 
 5

evolutions at the near- and far-ends of the market. Identifying such divergences is a major 

element of the present study. 

 To our knowledge, the only papers providing some evidence on the maturity structure of 

oil (or any commodity) futures markets are Neuberger (1999) and Ederington & Lee (2002).8

 In 

the process of rationalizing the use of short-maturity contracts to hedge long-term oil price risk, 

Neuberger (1999) provides some summary data on crude-oil futures market activity at different 

maturities from 1987 to 1994. Although the NYMEX first successfully introduced futures 

contracts on WTI light sweet crude oil in 1983, contract maturities beyond 12 months did not 

exist until 1989. By 1994, contracts had become available with intermediate maturities (up to 3 

years), but Neuberger states that, “even in the last two years of that dataset, only 3% of the 

contracts traded had a maturity beyond 1 year” (p. 438). One of only a handful of papers using 

the disaggregated (non-public) version of the CFTC’s COT data is Ederington & Lee (2002) 

who analyze heating-oil NYMEX futures position from June 1993 to March 1997 to document 

trading patterns across various types of traders in that market.9

 For their sample period, monthly 

expiration dates were available up to 18 months into the future, yet they find that large traders in 

their sample held 40.2% of their open interest in the upcoming winter months and 71.4% in the 

shortest-three maturity contracts. In sum, both the Neuberger and Ederington and Lee paper’s 

provide evidence consistent with the idea that prior to recent years there was precious little 

activity in far-month oil futures contracts. 

 Certainly, crude-oil futures with maturities of up to seven years have been available since 

1999. However, using data between 1999 and 2002, Lautier (2005) finds that short-, mediumand long-term crude oil futures were priced during that time period as if these contracts traded in 

segmented markets.10 More generally, the view that longer-term futures may be too illiquid to be 

useful for hedging purposes has remained a piece of conventional wisdom in many policy circles 

(where, amidst a generalized rise in energy prices, questions have been raised on how to 

 

8

 Many studies, in contrast, tackle the issue of the term structure of futures prices. See, e.g., Routledge, Seppi & 

Spatt (2000) and references cited therein. Litzenberger & Rabinowitz, 1995 investigate the theory and empirical 

evidence behind backwardation in oil futures markets. 9

 The other studies are Chang, Pinegar & Schachter (1997); Weiner (2002); and, Haigh, Hranaiova & Overdahl 

(2007). Those studies focus mostly on identifying the impact of various types of “non-commercial” traders (as 

opposed to “hedgers”) in some specific futures markets. 10 See also Pilipović, 1998; Simon and Lautier, 2005; and references cited therein. 
 6

stimulate the growth of long-term hedging markets) as well as in academic circles (where papers 

devising short-term hedging strategies for long-term price risk remain predicated on the notion 

that long-maturity contracts, if at all available, are still highly illiquid – see, e.g., VeldMerkoulova and de Roon, 2003). A key contribution of our study is to show that, since 2004, 

this conventional wisdom no longer applies in that (i) market activity in long-dated contracts (>3 

years) now routinely exceed typical levels of activity in short-term (<3 months) contracts just a 

few years ago and (ii) the prices of nearby and far-month contracts have become co-integrated. 

 The present paper is also part of a large literature on the role of specific categories of 

traders in financial markets. Hedge funds, in particular, have drawn a lot of attention from 

academics, investors, regulators, and the general public in recent years. Much of this scrutiny 

has focused on the concern that speculators, such as hedge funds, might exert a disproportionate 

and destabilizing effect on financial markets, which could ultimately lead to higher trading 

costs.11 Brunnermeier and Nagel (2004) find that hedge funds did not exert a correcting force on 

stock prices during the technology bubble. Haigh, Hranaiova & Overdahl (2007) document that 

hedge fund activity does not affect prices in energy futures markets but hedge funds are vital to 

the functioning of the market through the liquidity they provide to other participants. By contrast 

to these papers, our pricing analysis deals with the impact of various categories of traders on the 

linkages between markets rather than on the absolute price levels in a single market.12 Still, our 

results complement the research in that we find that hedge fund activity was beneficial to price 

discovery in the sense that their trading activity assists in bringing in line the prices of crude oil 

futures at different maturities.13 

 Our analyses indicates that commodity swap dealers/arbitrageurs are instrumental in cointegrating nearby, one- and two-year crude oil futures prices. Indeed, U.S. investors have 

greatly increased their exposure to commodity prices through commodity index investing in the 

 

11 Chan, Getmansky, Haas and Lo (2005) provide a concise review of the large academic literature on hedge funds. 

The evidence on whether funds are destabilizing is mixed. For example, Fung and Hsieh (2000) argue that funds 

exerted a significant market impact during the European Exchange Rate Mechanism (ERM) crisis in the early 1990s. 

By contrast, Choe et al. (1998), Fung et al. (2000), Goetzmann et al. (2000) and others conclude that hedge funds 

were not responsible for the Asian crisis in the late 1990s. 

12 Haigh, Hranaiova & Overdahl (2007) also abstract from the questions related to the maturity structure of trader 

positions that are at the heart of our analysis. 

13 Our study is also part of a literature interested in who trades derivatives, and why. Examples include Ederington 

and Lee (2002) in the case of heating oil futures markets, and Evans and Lyons (2005) in the case of foreign 

exchange derivatives. 
 7

last several years, and some of that exposure has found its way into futures markets. We 

document the growth in market activity by commodity swap dealers/arbitrageurs, and show 

empirically that this growth helps explain the improving price discovery between nearby, oneand two-year futures contracts. These findings complement recent academic research on 

possible advantages of investing in commodity futures markets (e.g., Erb and Harvey, 2006; 

Rouwenhorst and Gorton, 2006); the theoretical results of Basak and Croitoru (2006) on the role 

of financial traders with respect to price efficiency; and, the empirical finding that liquidity and 

the efficacy of arbitrage activities are related (Roll et al. (2006)).14

 

III. Data 

 We analyze daily position and pricing data for NYMEX light sweet crude oil (West 

Texas Intermediate grade, henceforth WTI) futures and options on futures contracts from the first 

week of January 2000 through the first week of May 2006. The position data compiled for this 

study originate from the CFTC’s Large Trader Reporting System (LTRS). Specifically, to help it 

fulfill its mission of detecting and deterring futures market manipulation, the CFTC’s market 

surveillance staff collects position-level information on the composition of open interest across 

all futures and options-on-futures contracts for each market. These data must be filed daily by 

traders whose positions meet or exceed the CFTC’s reporting threshold. For the WTI oil futures 

and options market in our sample period, this threshold has been 350 contracts since May 16, 

2000; prior to that date, it was 300 contracts. Many other similar positions are voluntarily 

reported and are included in the database. We find that more than 90% of all WTI futures 

positions end up being reported to the CFTC during our sample period. 

 The CFTC receives information on individual positions for every trading day. However, 

here we focus on the Tuesday reports, for two reasons. First, and most importantly, the Tuesday 

data are those which the CFTC summarizes in the weekly Commitment of Traders (COT) Report 

that it makes available to the public every Friday at 3:30 p.m. Consequently, our findings are 

 

14 Improved price discovery (shown through increased cointegration) and risk transfer are closely related (see Mattos 

and Garcia (2006)) and our results would suggest that increased cointegration in the crude oil market should lead to 

improved hedging opportunities. 
 8

directly comparable with those of the large number of extant studies that rely on COT data.15 

Second, perhaps because the Tuesday reports are publicly available, they receive particular 

attention within the Commission in terms of reconciling conflicting or erroneous reports. 

A. Publicly Available Data 

 When the CFTC publishes its crude oil COT report, traders are pooled into two broad 

categories – “Commercial” and “Non-commercial.”16 The CFTC classifies all of a trader's 

reported futures positions in a given commodity as commercial if the trader uses futures 

contracts in that particular commodity for hedging as defined in (the CFTC's) regulations. A 

trading entity generally gets classified as ‘commercial’ by filing a statement (with the CFTC) 

that it is commercially ‘engaged in business activities hedged by the use of the futures or option 

markets.’ In order to ensure that traders are classified with accuracy and consistency, the 

Commission staff may exercise judgment in re-classifying a trader if it has additional 

information about the trader’s use of the markets (CFTC, 2004). ‘Non-commercials’ comprise 

many types of mostly financial traders, such as hedge funds, mutual funds, floor brokers, etc., as 

well as those traders whose positions have been reported event though they are not registered 

with the CFTC under the CEA. 

 Table 1 shows the type of information contained in the publicly-available weekly COT 

reports of the WTI futures market and, while doing so, gives a snapshot of the overall growth of 

this market since 2002. For the third week of July each year, Table 1 shows the open interest in 

WTI light sweet crude oil futures and futures-equivalent (delta-adjusted) options positions for 

commercial (left panel) and non-commercial (right panel) traders. For each category and each 

year, the long and short “positions” are reported as fractions of the overall weekly open interest. 

For example, on the short [long] side of the 773,500 open interest on July 16, 2002, 67.2% 

[59.3%] of all positions were held by commercial traders and 1.5% [7.6%] were held by 

 

15 A minor difference is that the large trader datasets we use includes all positions reported to the CFTC by reporting 

firms – even those positions of traders small enough that they have no regulatory obligation to do so. In other 

words, even our aggregate data are a bit more precise than the publicly available ones. A second difference is COT 

frequency, which is less than weekly in studies using pre-2000 data. Precisely, starting in 1962, COT data were 

compiled on an end-of-month basis and published on the 11th or 12th calendar day of the following month. The 

CFTC switched to mid-month and month-end reports in 1990; to every 2 weeks in 1992; and, to weekly in 2000. 

16 COT reports also provide data on the overall long and short positions of non-reporting traders (NRP), which are 

the differences between the overall open interest and, respectively, the long and short positions of reporting traders. 
 9

reporting non-commercial traders, with the rest split between 24.3% in spread positions (i.e. 

calendar spread positions constructed with both long and short futures positions) by reporting 

non-commercial traders and 7% [8.8%] in short [long] positions held by non-reporting traders. 

Table 1 shows that open interest has more than doubled during that period, from less than 

800,000 contracts in 2002 to more than 1.7 million contracts in 2006 (each contract is for 1,000 

barrels of oil, or approximately $75,000 at mid-2006 prices – so that July-2006 open interest 

stood at more than $112 billion in notional value outstanding). 

B. LTRS Data 

 Whereas the publicly available data only identify the commercial and non-commercial 

categories for crude oil, the data provided for this study break down these two very broad 

categories into their respective components. That is, each reporting trader is classified into one 

of the trader groups in the CFTC’s large-trader reporting system (LTRS). Table 2 provides a list 

of these groups or sub-categories. Table 2.A provides a list of the ‘commercial’ sub-categories, 

while Table 2.B provides a similar decomposition for the ‘non-commercial’ category. In both 

Tables 2.A and 2.B, we highlight (in bold) which sub-categories are active in the crude oil 

market. 

1. Commercial Sub-Categories 

 In Table 2.A, the main sub-categories are “dealer/merchant”, which includes wholesalers, 

exporter/importers, crude oil marketers, etc.; “manufacturer”, which includes refiners, 

fabricators, etc; “producer”, a self-explanatory grouping; and “commodity swap dealers”, 

gathering all reporting swap dealers as well as arbitrageurs/broker dealers (financial swap dealers 

and arbitrageuers/broker dealer sub-categories were merged with commodity swap dealers 

partway through our sample data).17 

Traders in the dealer/merchant, manufacturer and producer sub-categories are sometimes 

referred to as “traditional” hedgers. The commodity swap sub-category, whose activity we will 

see has grown significantly since 2000, incorporates the positions of “non-traditional” hedgers, 

 

17 Haigh, Hranaiova & Overdahl (2007) identified, for the time period August 2003 – August 2004 in the WTI 

futures market, 27 commodity swap dealers, of which only one was an arbitrageur/broker dealer and only one was a 

financial swap dealer. 
 10

including “entities whose trading predominantly reflects hedging of over-the-counter (OTC) 

transactions involving commodity indices—for example, swap dealers holding long futures 

positions to hedge short OTC commodity index exposure opposite institutional traders such as 

pension funds” (CFTC, 2006). 

2. Non-Commercial Sub-Categories 

 In Table 2.B, the most active sub-categories are “floor brokers & traders”, a group 

including all reporting floor brokers and floor traders; and “hedge funds”, which comprise all 

reporting commodity pool operators (CPOs), commodity trading advisors (CTAs), “associated 

persons” (APs) controlling customer accounts as well as other “managed money” (MM) traders. 

Despite these clear distinctions in groups that comprise ‘hedge funds”, a point of terminology is 

in order at this stage. Hedge fund activity in energy derivatives markets has been a subject of 

intense scrutiny in recent years by academic researchers, market participants, policy makers and 

the media. However, there is no accepted definition of a “hedge fund” in futures markets, and 

there is nothing in the statute governing futures trading that defines a hedge fund. Furthermore, 

there is nothing that requires hedge funds to be categorized in the LTRS. Still, many hedge fund 

complexes are either advised or operated by CFTC-registered CPOs and/or CTAs and APs who 

may also control customer accounts. Through its LTRS, the CFTC therefore obtains positions of 

the operators and advisors to hedge funds, even though it is not a requirement that these entities 

provide the CFTC with the name of the hedge fund (or another trader) they are representing.18 It 

is clear that many of the large CTA’s, CPO’s, and APs are considered to be hedge funds and 

hedge fund operators. Accordingly, we will conform to the academic literature and to common 

financial parlance by referring to these three types of institutions collectively as “hedge funds.” 

In addition, for the purposes of this paper, market surveillance staff at the CFTC also identified 

 

18 A commodity pool is defined as an investment trust, syndicate or a similar form of enterprise engaged in trading 

pooled funds in futures and options on futures contracts. A commodity pool is similar to a mutual fund company or 

any investment company in the security industry, except that it invests pooled money in the futures and options 

markets. Like securities counterparts, a commodity pool operator (CPO) might invest in certain markets, such as 

financial markets, like foreign currency or treasury bonds, or commodity markets like wheat, coffee and feeder 

cattle. Unlike operators of mutual funds, however, commodity pools may be either long or short derivative 

contracts. The principle objective of the CPO is that it provides smaller investors the opportunity to invest in futures 

and options markets with greater diversification while at the same time having access to professional trading 

management. The CPO is the entity that solicits funds from others for the purpose of pooling the funds for use in 

investing in futures and options on futures interests. The commodity-trading advisor (CTA) actually manages the 

accounts and funds invested in the CPO and is the equivalent of an advisor in the securities world. 
 11

other participants who were not registered in any of these three categories but were known to be 

managing money and so these were also included in the hedge fund category (see bottom of 

Table 2). Finally, NR represents those traders that have not yet been categorized or do not fit 

any other category. “Non-reporting participants” are reporting traders who are not registered 

under the Commodity Exchange Act (CEA). 

 We use LTRS data on both futures and options positions. Because our goal is to provide 

a picture of the overall market for exchange-traded crude oil derivatives, all of the results we 

report are for the sum of the futures and futures-equivalent (i.e., delta-adjusted) options positions 

– both short and long. The options positions account for approximately 25% of the total market, 

and we obtain qualitatively similar conclusions for changes in futures only (not reported) 

although available. 

IV. Large Trader Positions

 For each group of traders, we use three measures of the group’s daily positions to assess 

changes in the market. (i) We start with the net position of each group’s daily net position, which 

may be short (-) or long (+). To provide a clear overall picture, we present annual or monthly 

averages of those daily figures in Figures 1-3 and discuss those exhibits in Section IV.A. (ii) To 

the extent that most traders hold different types of positions (long or short) at different maturities 

and at different points in time, the mere averaging out daily positions is bound to understate the 

extent of the futures market growth. In Section IV.B, we therefore carry out a similar analysis – 

but after first taking the absolute value (+) of each position. That is, we take the absolute value, 

every day, of the net position in each contract for each trader category; we then sum up these 

absolute values appropriately (e.g., for all commercial groups, for all positions less than 3 

months, etc.), and compute the various monthly or annual averages. Table 3 and Figures 4-6 

provide either annual or monthly averages of those daily figures. Whereas the analysis of 

Section IV.A tells us about the direction of different traders’ positions during sample subperiods, the analysis in Section IV.B provides us with an idea of the magnitude of these 

positions.19

 

19 The absolute values computed in Section IV.B capture market growth for traders who have an underlying hedging 

motive and, hence, hold mostly outright (long or short) positions. To the extent that many non-commercial traders 
 12

A. Net Positions 

 From Table 1, we see that the net positions (long – short) of commercial and noncommercial traders range from -3.7% (2005 commercial positions) to 9% (2003 non-commercial 

positions) of the open interest across all contract maturities. Figure 1 provides a more complete 

picture, by breaking down annual average net positions by contract maturity for commercial and 

non-commercial traders, while Figures 2 and 3 provide a further disaggregation by trader type. 

All three Figures are drawn based on the net daily positions in each contract for each trader 

category. For each trading day in the sample period, we sum up the net positions appropriately 

(e.g., for all commercial groups, or for all positions less than 3 months, etc.), and then compute 

the relevant annual (Figures 1-2) or monthly (Figure 3) averages. 

1. Net Positions for Commercial and Non-Commercial Traders – Broad Changes since 2000 

 Figure 1 shows the evolution of annual average daily net positions of commercial and 

non-commercial traders over time. Several patterns emerge from the Figure. First, and least 

surprising, is the fact that non-commercial and commercial traders on average take oppositedirection positions. As shown in Section IV.B, the difference is made up of small (nonreporting) traders, who comprise about 12% of the market at short maturities (<3 months) but 

only 3% of the market at long maturities (>3 years). 

Second, one cannot generalize from the fact that, in the three near-month contracts, 

commercial traders are typically net short (-) whereas non-commercial traders are generally net 

long (+). Instead, the direction of the two groups’ respective net exposures varies with contract 

maturity, period of observation, etc. Whereas non-commercials generally have long (+) net 

positions on average, especially in the three nearest-months contracts, they also tend to hold 

short (-) positions in contracts with maturities beyond 4 or 5 years. Another example is found in 

2001, when commercial traders (i.e., “hedgers”) were net long (+) near-months contracts, with 

 hold spread positions (i.e., are long at some maturity and short at another), however, it should be helpful to also have 

a measure of the open interest, in each contract maturity, for each trader category. This is beyond the scope of the 

current paper. 
 13

their average net short (-) position that year approximately equal to one third of their average net 

long (+) position the following year. 

Third, the annual average net positions of both of these two broad categories (commercial 

and non- commercial traders) have more than tripled since 2000, averaging approximately 

60,000 contracts a day that is each Tuesday in 2006.20 These growth rates are impressive in their 

own right. They also hide a large amount of heterogeneity among different types of commercial 

trader. These variations are the topic of the next subsection. 

2. Trader Heterogeneity 

 Figure 2 provides a vivid illustration of the heterogeneity of trader participation within 

the broad commercial group. Most strikingly, whereas the commercial category as a whole is 

typically net short (-) in near-months (<3 months) crude oil futures, we see that one of the 

commercial sub-categories (commodity swaps dealers which include commodity index traders) 

almost always holds a net long (+) position in near-month contracts. From January 1st to May 

9th 2006, for example, commercial traders as a whole were on average net short 56,540 (-) 

contracts. However, this aggregate figure was the sum of an average net long position of 90,140 

(+) for commodity swaps dealers and of an average net short position of 146,680 (-) contracts for 

all the other (i.e., for the traditional) commercial sub-categories. 

Figure 3 provides information similar to, but more detailed than, that in Figure 2. Figure 

3 shows the monthly (rather than annual) average daily net positions in futures and adjusted (i.e., 

futures-equivalent) options positions, by contract maturity, of the four most important 

commercial trader types: dealer/merchant, manufacturer, producer, and commodity swaps dealer. 

The daily net positions in each contract for each reporting trader are summed up (e.g., for all 

contracts with less than 3 months until maturity, etc.), and the monthly averages are then plotted. 

The graphs are scaled to allow for easy comparisons of the relative sizes of the net positions held 

by different trader categories. Two patterns readily emerge. First, all of these trader types have 

 

20 The apparent drop in net positions between 2004 and 2006, which is most obvious for the near-months contracts, 

is an artifact of the change in composition of this broad category discussed in Section IV.A.2. Figure 4, drawn in a 

similar fashion to Figure 2 but with the absolute values of net positions, shows continuous growth between 2001 and 

2006. 
 14

held both short (-) and long (+) net positions in at least some months of the sample period, in 

contracts of all maturities. This finding indicates that one ought to be careful when trying to 

make generalizations about the positions held by any given trader category. Second, for most 

trader categories, while the magnitude of their net positions (be they long or short) in short-term 

contracts has increased over the course of the sample period, the most dramatic change is the 

quintupling of net positions taken by commodity swap dealers. 

B. Market Growth 

 While the growth in market activity is apparent in Figures 1-3, the growth is partly 

obscured in these figures by the periodic changes in the direction of the net positions taken by 

various types of traders. That is, when these successive net long (+) and short (-) positions are 

averaged, the resulting picture could understate the actual growth of the market. 

 To tackle this issue, Figures 4, 5 and 6 as well as Table 3 carry out a similar analysis 

using the absolute values of daily trader positions. In these Figures and in Table 3, we first take 

the absolute value of the daily net positions in each contract for each trader group, then sum up 

the resulting numbers appropriately (e.g., for all positions less than 3 months, etc.), and finally 

compute annual or monthly averages. Because net positions make up a relatively constant 

fraction of open interest for all contract maturities and across the sample period (ratios not 

reported here), these figures provide a useful indication not only of the magnitude of the net 

positions but also of the market as a whole. 

Figure 4 shows continuous market growth between 2001 and 2006, with the largest 

proportional increases at maturities less than 3 months and more than 3 years. Figures 5 and 6, 

together with Table 3, provide additional details of those changes, and allow us to make several 

observations. 

(i) First, commodity swap dealers’ net positions increased by an order of magnitude, 

from just over 10,000 contracts in 2000 to almost 100,000 contracts in 2006. This massive 

growth started in 2002, and accelerated sharply from mid-2004 onward. 

(ii) Second, Figure 5 illustrates that almost all large-trader categories at the end of our 

observation period carry aggregate net positions in long-term (>3 years) contracts that are 
 15

comparable in magnitude to their analogous positions in short-term (<3 months) contracts prior 

to 2003.21 Precisely, for five out of the six major large trader categories in the crude oil futures 

market (commodity swaps dealers; hedge funds; commercial dealers/merchants; commercial 

producers; and, floor brokers and traders), there are at least some months in 2006 when the 

category’s average daily net position in long-term contracts exceeded the corresponding figure 

for short-term contracts in at least some month prior to 2003. As Table 3 shows, the average 

absolute value of daily net trader positions in 2006 even exceeds the corresponding figures for 

the entire year 2000, in the case of swap dealers and floor brokers and traders. 

(iii) Third, there are some exceptions to the overall increases in trader participation at 

longer maturities. One is commercial oil manufacturers (refiners, fabricators, etc.), a category 

that holds large net positions in short-term futures contracts but virtually none at long maturities. 

Another exception is small traders (those with individual positions small enough that they do not 

report their positions to the CFTC large-trader database), who account for approximately 12% of 

net positions in near-term contracts but for only 3-4% of net positions in long-term contracts. 

Fourth, the strongest market growth has taken place at contract maturities of less than 3 months 

and more than 3 years. By comparison, net trader positions in intermediate-maturity contracts 

have grown relatively less. 

(iv) Finally, most of the growth of net positions in long-term (>3 years) contracts has 

taken place since the end of 2003. 

In the next Section, we investigate whether this market growth in general, or some of its 

aspects, has had consequences for pricing and price discovery in the benchmark market for crude 

oil futures. 

V. Pricing 

 Price discovery and risk transfer are critical functions performed by futures markets. 

Garbade and Silber (1983) show theoretically that risk transfer and price discovery are 

interrelated, and that the ability of a market to simultaneously perform these functions depends 

on the strength of the linkage between the derivative markets and the markets underlying those 

 

21 Alhough, as discussed in Section IV.A, not always similar in direction. 
 16

derivatives. These authors illustrate that trading provides the linkage between cash and futures 

prices, leads to a closer correlation between the prices, to a more efficient transmission of 

information, and to improved hedging opportunities. A natural question is whether the growth in 

futures market activity that we documented in Section IV has had consequences for the pricing 

of crude oil futures and, consequently, the ability to better manage risk. Intuitively, price 

discovery should improve in markets as they become less thinly traded. In this Section, we focus 

on identifying changes in the relationships between the prices of futures contracts at different 

maturities. Specifically we focus on the prices of the near-month, 1-year and 2-year futures 

contracts.22 

A. Methodology 

 A large volume of research has attempted to evaluate the degree of interconnectivity 

between prices from different markets employing time-series techniques that are appropriate for 

non-stationary and co-integrated data. In particular, much work on applied co-integration 

analysis has relied on Johansen’s multivariate approach (Johansen, 1988, 1991; Johansen and 

Juselius, 1990). 

 Simply stated, assume an n-dimensional vector of non-stationary time series,Yt , (n = 3 

here – nearby, one-year and two year contract). Assuming all variables are non-stationary and the 

existence of co-integration, then the data generation process can be written (Johansen and 

Juselius, 1990) as an error-correction representation as follows: 

∑

−

=

∆ = Γ ∆ − + Π − +

1

1

1

k

i

Yt i Yt i Yt t ε . (1) 

 Equation (1) is a Vector AutoRegression (VAR) (in first differences) with the short run 

parameters being captured by the Γi matrix, and the inclusion of the lagged-level component, 

which is commonly known as the Error Correction Term. The combination of these two is 

known as the Error Correction Model (ECM). There are three cases of interest. First, if Π is full 

 

22 In this paper, we focus on nearby month, 1-year and 2-year futures because over this period of time monthly 

contracts are traded out 2 years enabling us to create rolling daily positions at these different maturities while at the 

same time ensure the one year interval between the maturities remain constant. 
 17

rank, then Yt is stationary in levels – and, indeed, a VAR in levels is an appropriate model. 

Second, if Π has zero rank, implying it contains no long run information and the appropriate 

model would be a VAR in first differences and third, if the rank of Π is a positive number r and 

is less than p there exist matrices β and α with dimensions p x r such that Π is a (3x3) 

coefficient matrix that may be factored as αβ'. The β matrix is a matrix of co-integrating 

parameters and the matrix α is a matrix of weights (commonly known as the speed of 

adjustment parameters) with which each co-integrating vector enters the n equations. Hypothesis 

tests can conveniently be conducted on these matrices. Assuming non stationary oil futures 

prices, we can examine the long run relationships between the contract maturities by determining 

the number of co-integrating vectors, r as follows: 

 H1(r): Π = ' αβ (2) 

The trace test (Johansen, 1991) is conducted to determine r. The null hypothesis for the trace 

test is that there are most r (0 ≤ r ≤ p) co-integrating vectors. 

 To examine the stability of the long and short run relationships between nearby and 

distant futures markets for crude oil in this paper, we apply recursive co-integration techniques 

(Hansen and Johansen, 1993, 1999). These techniques allow us to test for the level of cointegration between price series during our sample period. The recursive technique allows us to 

recover two ECM representations. In the “Z-representation,” all the parameters of the ECM ( β

and α ) are re-estimated during the recursions, while under the “R-representation” the short-run 

parameters (α ) are kept fixed to their full sample values and only the long run parameters ( β ) 

are re-estimated. 

 In this study, we define Yt as composed of three elements, which represent the nearby, 

one year and two year price series for crude oil futures. Under both of these representations, we 

perform the trace tests to visually inspect the time path of the statistics to identify any changes to 

the co-integrating relationships amongst the price series. From equation (1) the Trace test is 

calculated as 
 18

 Tracej = ˆ ln(1 ),

p

i

i j

T λ =

∑ − j = 1,..., p-1, (3) 

and each statistic is scaled by the 90% quantile of the trace distribution derived for the selected 

model. 

B. Estimation of the Error Correction Model 

 We use Tuesday settlement prices from January 4, 1999 through May 9, 2006, provided 

by Bridge CRB, for the near-month (or “Nearby”), one-year out and two-year out WTI crude oil 

futures contracts. The first year of data (52 weeks) is utilized to start the cointegration modeling 

(in particular, the recursive analysis) so that the price and position samples match the starting 

date for our position sample, i.e., January 4, 2000. 

Table 4 provides summary statistics of these three price series between January 2000 and 

May 2006. It is worth noting that the maximum of each of these price series are roughly seven 

times larger than the corresponding minimums which reflects the increase in oil prices during our 

sample period.23 

 In order to implement our Error Correction Model, we first check the order of integration 

of each of the price series within the futures maturity spectrum. As can be seen from Table 5, 

each series is integrated of the first order confirming that the analyses will be conducted on the 

differenced price series. We then estimate the ECM using the maximum likelihood technique 

outlined by Johansen and Juselius (1990). The lag length order was selected based on lag 

reduction statistics and 8 lags were deemed to be the optimal lag length.24 

Table 6 presents the decision rule based on the trace tests for the number of cointegrating vectors. We first fail to reject the null hypotheses on r ≤ 1. At p = 0.079 there 

 

23 The simple unconditional correlations between the three price series are greater than 0.95, which suggests a close 

link between the series. We also analyzed the correlation between spot price data and the three futures prices series. 

The unconditional correlation between spot prices and nearby, one year and two year contracts are 0.999, 0.98 and 

0.97. Consequently given that spot prices and nearby prices are effectively identical we infer that using spot instead 

of nearby prices would yield similar results. 

24 These results are excluded to conserve space but are available upon request. 
 19

appears to be some weak evidence of the existence of two co-integrating vectors. However, to 

keep the analysis tractable, we estimate our ECM with one co-integrating vector.25 We also test 

the individual elements of ' β against zero in the factorization αβ'= Π and investigate the 

possibility of weak exogeneity of each of the series (testing whether each element of the α vector 

is equal to zero) and the results are presented in the lower portion of Table 6. In particular, the 

middle panel explores the possibility that one of the three series is not in the co-integrating 

space. We reject the null that price i is not present in the co-integrating space for each series. 

With respect to the short-run adjustment toward the long run relationships,α , we also test for 

weak exogeneity on each market. For each market we test for whether or not it responds to 

perturbations in the co-integrating space. Inspection of the lower panel of Table 6 suggests that 

both the nearby and 1-year contracts are weakly exogenous and that the 2 year contract does all 

the adjusting to the long-run equilibrium. Implications of this finding are explored below. 

 In order to study the development of the level of cointegration between contracts of 

differing maturities we apply recursive cointegration techniques which also allows us to examine 

the stability and possible strengthening of the relationship over time. This technique has been 

employed in other studies (e.g., convergence across stock markets, interest rates in different 

countries etc) by Bremnes et al., 1997; Diamandis et al., 2000, Yang et al., 2004, but has yet to 

be been applied to price discovery in futures markets. Following Hansen and Johansen 

(1993,1999), here we conduct the rank constancy test based on what is known as the “Rrepresentation” whereby the short-run parameters captured in the Γi matrix are held fixed to their 

full sample values and only the long run parameters in equation (1) are re-estimated at each time 

interval. The mathematics behind the ‘R-representation’ are excluded to conserve space and 

interested readers are directed to Hansen and Johansen (1993, 1999). Recursive cointegration 

analysis is conducted on the single cointegrating vector tying the three time series together and 

 

25 Residuals from this estimated ECM seem to be well behaved. Specifically, Lagrangian Multiplier tests for first 

and second order autocorrelation using an auxiliary regression, as described by Godfrey (1988, Chapter 5) are 

conducted. We reject both orders of autocorrelation comfortably with p-values of 0.994 and 0.536 respectively. The 

test statistics for no conditional heteroskedasticity are rejected, but Rahbek et al. (2002) show that the rank test for 

cointegration is robust to moderate ARCH effects, so this appears not to be a problem in this case (these results are 

excluded to conserve space but are available upon request). 
 20

Figure 7 presents a time series plot of the normalized trace statistic (normalized by the 5% level 

of significance) for each week between January 2000 and May 2006. 

 As shown in Figure 7, results based on recursive cointegration are far more informative 

than the standard Johansen cointegration technique to the extent that they illustrate the change in 

the long run relationship between the series. As can be seen from the time series plot, the 3 

contract maturities were not in long run cointegration in January 2000 (the trace value was below 

1) but the series tended toward 1 (moving toward cointegration) in 2002 and 2003. On the 6th

July 2004 all series became cointegrated and have essentially remained cointegrated ever since. 

 In addition to the time path of the trace statistic we are also able to recover the speed of 

adjustment parameters (α ) for each of the series in the cointegrating vector. Each speed of 

adjustment parameter measures the degree to which each variable (contract maturity price series) 

responds to the deviation from the long-run equilibrium relationship. The ‘larger’ (in absolute 

value) the speed of adjustment, the faster the speed of adjustment from the previous period’s 

deviation from long run equilibrium. At the opposite end of the spectrum a small value implies 

that the price series in question is unresponsive to last period’s equilibrium error. A speed of 

adjustment equal to zero implies that a series is weakly exogenous. Thus the absence of Granger 

causality for cointegrated variables requires the additional condition that the speed of adjustment 

coefficient be equal to zero. As can be seen from Table 6, results suggest that both the nearby 

and one year contracts are weakly exogenous, and it is the two year contract that adjusts toward 

the long run equilibrium. Thus, the two year contract cannot cause changes in the nearby and 

one year contract as they do not adjust to perturbations. The opposite is not true. 

 The recursive estimates of the speed of adjustment parameters and the 5% confidence 

intervals for all three series are presented in Figure 8. As can be seen in the time series plots for 

the vast majority of the time the speed of adjustment parameters for the nearby and one year 

contract are statistically indistinguishable from zero. Interestingly, around the same time that the 

maturities became cointegrated (mid-2004), the second year maturity speed of adjustment 

parameter become statistically significantly different than zero. It has, for the most part, 

remained statistically different than zero since that time. 
 21

C. Explaining the Convergence between Price Series 

 We examine the growing convergence between crude oil futures contracts more 

rigorously in Tables 7 and 8. To this end, we use the trace statistic (plotted in Figure 7) as the 

dependent variable and use position data from the various categories of traders as explanatory 

variables. In this manner, our tests capture changes in the level of participation over time that can 

be tied to the concurrent increase in the trace statistic.26

 We first estimate simple regressions explaining the change in the level of cointegration 

using positions scaled by the mean (long plus short positions divided by the mean) for each 

category of trader independently in Table 7. We estimate several specifications, reporting the 

relation between each individual trader type and the cointegration Trace statistic independently. 

We report three specifications in Table 7 as follows: 

Spec 1: 5 Trace Near One Two t t =+ + α β ε ( Year+ Year)+

Spec 2: 1 4 Trace Near One Two t t =+ + α ββ ε ( ) ( Year+ Year)+

Spec 3: 12 3 Trace Near One Two t =+ + α ββ β ε ( ) ( Year)+ ( Year)+ t

 For example, in specification 1 we use the pooled activity in all three contracts (nearby, 

one-year and two-year) by trader category separately in a robust OLS regression to explain 

movements in the Trace statistic. As indicated in the lower portion of Table 7, swap dealer, 

hedge fund and non-reporting dealer positions appear to be significantly related to the 

strengthening of the cointegration level among the different contract maturities. Notably 

however, the explanatory power of swap dealer positions (with an adjusted R2

 of 0.21) greatly 

exceeds the explanatory power of all other trader category positions. Dealer/merchant, producer 

 

26 Other studies employing recursive cointegration techniques attempt to explain changes in the level of 

cointegration over time by commenting on changes (often abrupt) in trace values using information on actual 

(usually one-time) events. For instance, Phylaktis and Ravazzolo (2006) explain convergence in international stock 

markets using information on foreign ownership restrictions and Yang, Kolari and Sutanto (2004) attribute a sudden 

change in cointegration levels between stock markets to the global emerging markets crisis during 1997-1998. In a 

similar vein to our paper, Haigh (2000) examines the development of cointegration across time but fails to link 

trading volume in freight futures contracts to increased cointegration levels in pricing within freight markets. 
 22

and floor trader positions are generally unrelated to cointegration levels whereas manufacturer 

and others show some marginal contribution to cointegration over time. 

 Specification 2 parses out the incremental explanatory power of nearby positions from 

the analysis. As shown in the middle portion of Table 7, partially disaggregating positions by 

contract length uniformly increases the explanatory power of these regressions (the adjusted R2

for non-reporting traders increases by 0.43, for instance). Furthermore, positions in the nearby 

contract tend to be most strongly related to cointegration over time. Aggregate one- and twoyear commodity swap dealer positions continue to explain a large proportion of the price 

convergence. Aggregate one- and two-year positions are significant for most non-commercial 

traders (floor traders and hedge funds) as well. 

 Specification 3 (upper portion of Table 7) fully disaggregates the effects of nearby, oneand two-year contract positions across trader type. With these specifications, we continue to see 

an increase in the explanatory power of most regressions. As with Specification 2, we continue 

to see that positions in nearby contracts contribute significantly to cointegration for all trader 

categories except dealer/merchants. Significance of one- and two-year contract positions is less 

significant for most traders, except for producers and commodity swap dealers where all three 

(nearby, one- and two-year) positions remain strongly related to increased cointegration among 

these contracts over time. 

We also estimate models based on the positions of all trader categories concurrently in 

Table 8. Similar to the individual trader position regressions we estimate several specifications 

that attempt to explain the development of cointegration among nearby, one- and two-year 

contract prices. To illustrate, the equivalent pooled model for specification three used in the 

individual regression is as follows: 

 

Inclu

12 3 4

5 6 78

9 10 11 12

13

( ) ( Year)+ ( Year)+ ( )

( Year)+ ( Year)+ ( ) ( Year)+

( Year)+ ( ) ( Year)+ ( Year)

+( )

Trace AD Near AD One AD Two AM Near t

AM One AM Two AO Near AO One

AO Two AP Near AP One AP Two

AS Near

αβ β β β

β β ββ

β ββ β

β

=+ + +

+

+

14 15 16

17 18 19 20

21 22 23 24

( Year)+ ( Year)+ ( )

( Year)+ ( Year)+ ( ) ( Year)+

( Year)+ ( ) ( Year)+ ( Year)+ .t

AS One AS Two FBT Near

FBT One FBT Two MMT Near MMT One

MMT Two NRP Near NRP One NRP Two

βββ

β β ββ

β ββ β ε

+ +

+

+
 23

ding multiple trader categories within the same regression adds significant explanatory power, 

with the adjusted R2

 for these models ranging from 0.56 to 0.75. When nearby, one- and twoyear contracts are pooled, several trader group positions are significantly linked to the 

development of cointegration. Notably, commodity swap dealers have the greatest positive 

influence on cointegration. Only non-reporting traders share the positive relation in pooled data. 

When we disaggregate data, the influence of commodity swap dealer positions is strongly 

significant in one- and two- year positions, both individually and when pooled. Commodity 

swap dealer positions in the nearby contract is not significantly related to the development of 

cointegration over time. However, in disaggregated data, we also find that other trader positions 

are significantly related to cointegration. Notably, dealer/merchant positions in the two-year 

contract, hedge fund positions in the one-year contract and non-reporting trader positions in the 

nearby contract are also strongly related to the development of cointegration over time. While 

these other trader groups also help to explain the convergence in the price series, commodity 

swap dealer positions dominate in terms of explaining convergence, whether we focus on 

disaggregated or pooled regressions. 

 To summarize, we find that nearby contract prices (which are highly correlated with cash 

prices), one- and two-year futures prices have become more cointegrated over time primarily 

because commodity swap dealers have increased positions at one- and two-year horizons. This 

result holds when examining individual trader positions in isolation (Table 7) and when 

controlling for all other trader positions in the market (Table 8). As such, increased commodity 

swap dealer participation in crude oil futures markets over 2000-2006 has generated better price 

discovery in one- and two-year futures markets. The improved linkages among these contracts 

allow for better, more efficient risk management in the markets for crude oil. 

VI. Conclusions

 In this paper, we document major changes in the size and term structure of the benchmark 

U.S. market for crude oil futures since the turn of the millennium. In particular, we document the 

growth especially at the far end of the market. We show that, as recently as 2000, activity was 

heavily concentrated at the near end of the maturity spectrum. Since then, overall open interest 
 24

has more than doubled, with trader activity at the back end of the maturity spectrum increasing 

over twice as much as the market as a whole. The market growth in long-term (>3 years) 

positions generally started in 2004. An analysis of the composition of traders participating in the 

market shows that almost all large-trader categories (commodity swap dealers, hedge funds, 

commercial dealer/merchants, commercial producers) now carry aggregate net positions in longterm contracts comparable in magnitude to the size of their net positions in short-term (<3 

months) contracts prior to 2003. 

We document that, in the wake of this market growth, the prices of one-year and twoyear futures became co-integrated with the price of the near-month futures in 2004. Price 

discovery and risk transfer are critical functions performed by futures markets, and the ability of 

a market to simultaneously perform these functions depends on the strength of the linkage 

between the derivative and underlying markets. To explore this issue, using unique position 

level data we investigate, for the first known time, the question of which class of trader is 

responsible for the strengthening cointegration between nearby and distant futures contracts in 

crude oil. We find that the growth of activity by a class of traders, known as commodity swap 

dealers, explains much of this convergence. This class of traders, more than any other, by its 

trading activity allows other categories of traders to better manage risk with futures contracts 

several years into the future. The major changes we document have potentially significant 

implications for those interested in the effectiveness of hedges constructed with crude oil futures 

and for those interested in the quality of information contained in futures prices across the term 

structure. 
 25

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 29

Table 1: Open Interest in Crude Oil Futures and Options, 2002-2006

Non-Commercial (%) Commercial (%) Total Open 

 long short spread long short Interest 

2002 7.6 1.5 24.3 59.3 67.2 773,550 

2003 14.0 5.0 18.6 60.4 67.6 779,513 

2004 11.8 4.6 19.6 62.1 68.5 1,026,123 

2005 9.4 5.1 26.5 58.5 62.2 1,303,209 

2006 12.1 6.3 30.9 52.5 58.3 1,706,416 

Notes: Table 1 provides a snapshot of open interest changes since 2002. Open interest data are from the weekly 

Commitment of Traders Reports from July 16, 2002 through July 19, 2006. For each year, the snapshot is for the 

third week of July. We report the open interest for the sum of futures and adjusted options (i.e., total) positions of 

commercial and non-commercial traders. When the CFTC publishes its weekly Commitment of Traders Report, 

reporting traders are categorized into two broad groups: “Commercials” (left panel), who have declared an 

underlying hedging purpose, and “Non-commercials” (right panel), who have not. For each category, the long and 

short positions are reported as fractions of the overall open interest. For example, on the short [long] side of the 

773,500 open interest on Tuesday, July 16, July 2002, 67.2% [59.3%] of all positions were held by commercial 

traders and 1.5% [7.6%] were held by reporting non-commercial traders, with the rest split between 24.3% in spread 

positions (i.e., calendar spread positions constructed with long and short futures positions) by reporting noncommercial traders and 7% [8.8%] in outright short [long] positions held by non-reporting traders. 
 30

Table 2: Large trader categories

Table 2A: Commercial Traders 

CFTC Code CFTC Name Present in WTI futures & options markets 

18 Co-Operative 

AD Dealer/Merchant Y 

AM Manufacturer Y 

AO Agricultural/Natural Resources – Other Y (very small) 

AP Producer Y 

AS Commodity Swaps/Derivatives Dealer Y 

FA Arbitrageur or Broker/Dealer Y (merged into AS)

FB Non U.S. Commercial Bank 

FC U.S. Commercial Bank 

FD Endowment or Trust 

FE Mutual Fund 

FF Pension Fund 

FG Insurance Company 

FH Hedge Fund 

FM Mortgage Originator 

FO Financial – Other 

FP Managed Account or Pool 

FS Financial Swaps/Derivatives Dealer Y (merged into AS)

FT Corporate Treasurer 

LF Livestock Feeder 

LO Livestock – Other 

LS Livestock Slaughterer 

18 Co-Operative 

Table 2B: Non-commercial Traders 

CFTC Code CFTC Name Present in WTI futures & options markets 

HF Hedge Funds Y 

FBT Floor Broker /Trader Y 

FCM Futures Commission Merchant Y (very small)

IB Introducing Broker Y (very small)

NR NON-REPORTING TRADER Y 

Notes: Table 2 lists the trader sub-categories in the CFTC’s large-trader reporting system (LTRS). Bolded entries are 

those on which most of our analysis focuses. When the CFTC publishes its weekly Commitment of Traders Report, 

these various sub-categories are aggregated in two broad groups: “Commercials” (Table 2A), who have declared an 

underlying hedging purpose, and “Non-commercials” (Table 2B), who have not. In Table 2A, “Dealer/Merchant” 

(AD) includes wholesalers, exporter/importers, crude oil marketers, shippers, etc. “Manufacturer” (AM) includes 

refiners, fabricators, etc. “Agricultural / Natural Resources – Other” (AO) may include, for example, end users. 

“Commodity Swaps/Derivatives Dealers” (AS) aggregate all reporting “Swaps/Derivatives Dealers” (FS) and 

“Arbitrageurs or Broker Dealers” (FA), two categories that were merged in the CFTC’s internal reporting system partway through our sample period. In Table 2B, “Hedge Funds” (HF) aggregate all reporting Commodity Pool Operators 

(CPO), Commodity Trading Advisors (CTAs), “Associated Persons” (APs) controlling customer accounts as well as 

other “Managed Money” (MM) traders. “Floor Brokers & Traders” (FBT) aggregate all reporting floor brokers and 

floor traders. NR represents those traders that have not yet been categorized or do not fit any other category. “Nonreporting participants” (NR) are reporting trader who is not registered under the Commodity Exchange Act (CEA). 

Note: FH under the Commercial category includes hedge funds in financial contracts that are shown to be hedging. 

This category has very few participants and is not relevant to our study. 
Table 3: Absolute Value of Net Positions at ≠ Maturities: Changes Since 2000

Panel A: Commercial vs. Non-commercial Categories

<3 month 3-36 months >3 yea

2000 2006 growth 2000 2006 growth 2000 2006 

Commercial 106,027 268,974 154% 192,348 203,242 6% 14,820 42,3

 

Non-commercial 34,997 76,327 118% 39,390 100,500 155% 2,222 19,3

 

Total 141,025 345,301 145% 231,739 303,742 31% 17,042 61,6

 

Panel B: Traditional vs. Non-traditional Commercial Traders

<3 month 3-36 months >3 yea

2000 2006 growth 2000 2006 growth 2000 2006 

Other Commercial 10,624 100,493 846% 37,917 55,691 47% 2,517 14,6

 

Traditional 95,403 168,481 77% 154,431 147,551 -4% 12,302 27,7

 

Total 106,027 268,974 154% 192,348 203,242 6% 14,820 42,3

 

Panel C: Individual Trader Sub-categories

<3 month 3-36 months >3 yea

2000 2006 growth 2000 2006 growth 2000 2006 

Dealers (AD) 31,709 75,836 139% 69,957 68,589 -2% 3,664 12,8

 

Manufacturers (AM) 28,678 59,365 107% 25,219 23,451 -7% 3,926 2,3

 

Other Commercials (AO) 3,906 6,389 64% 5,262 6,396 22% - 6

 

Producers (AP) 28,862 26,891 -7% 53,337 49,115 -8% 4,707 11,8

 

Swap Dealers (AS) 10,624 100,493 846% 37,917 55,691 47% 2,517 14,6

 

Floor Brokers (FBT) 2,913 11,237 286% 20,077 31,785 58% 720 4,1

 

Hedge Funds (HF) 26,662 43,913 65% 16,361 55,889 242% 1,019 11,1

 

Non-Reporting (NRP) 5,422 21,177 291% 2,953 12,826 334% 482 4,0

 

Total 138,777 345,301 149% 231,082 303,742 31% 17,035 61,6

 

Notes: Table 3 shows the yearly averages (2000-2006) of the absolute values of the net futures and adjusted options positions commercial traders by contract maturity. “Commercial” traders include AD = Dealer/Merchant, AM = Manufacturer, AO = Agricultu

Other, AP = Producer, AS = Commodity Swaps / Derivatives Dealer (which includes FS = Financial Swaps / Derivatives DeBroker/Dealer). “Non-commercial” traders include FBT = Floor Broker / Floor Trader; HF = Hedge Fund; as well as, the reporte

subject to the reporting requirement and the positions of traders that have not yet been categorized or do not fit any other category. value of the daily net positions in each contract for each trader group, figures are summed up appropriately (e.g., for all commercial less than 3 months, etc.), and the annual averages are then computed. Table 3 is directly comparable to Figures 4-6 below. 
 32

Table 4: Descriptive statistics and correlation analysis on weekly prices

 Nearby 1-year contract 2-year contract

Mean 34.67 32.01 30.43

Median 29.99 25.46 23.70

Standard deviation 14.01 15.33 15.07

m3 0.96 1.35 1.45

m4 0.09 0.62 0.82

Minimum 11.37 12.92 14.06

Maximum 74.61 76.96 74.07

Unconditional Correlations 

 Nearby 1-year contract 2-year contract 

Nearby 1 

1-year contract 0.98 1 

2-year contract 0.97 1.00 1 

Notes: Table 3 provides summary statistics of the daily settlement price series for the near-month, 1- and 2-year out 

WTI sweet crude oil futures contracts between 1/4/2000 and 5/9/2006. In line with the analysis of trader positions, 

the prices used are those for Tuesday settlements. m3 and m4 represent sample skewness and kurtosis respectively. 

Table 5: Augmented Dickey-Fuller (ADF) tests for order of integration on futures prices

Test is on the estimated coefficient θ1from the following prototype model: 

∑=

∆ = + + ∆

K

k 1

Xt θ 0 θ 1Xt-1 β k Xt-k

Price Series K HO: I(1) vs. HA: I(0)

ADF 

HO: I(2) vs. HA: I(1)

ADF 

Nearby 0 -0.49 -21.59 

1-yr contract 1 1.98 -22.01 

2-yr contract 1 2.71 -22.50 

Notes: Critical values are taken from Fuller (1976). They are –2.57 (10%), –2.88* (5%) and –3.46 (1%). Therefore, 

based on these results are series are I(1). The optimal lag length (K) is based on the Schwarz Bayesian Criterion 

(1978). 
 33

Table 6. Cointegration Analysis of Prices

Trace tests on order of cointegrationa

λtrace test statistic HO: critical value(p-value)

45.21 r = 0 35.07 (0.002) 

22.82 r ≤ 1 20.16 (0.020) 

8.23 r ≤ 2 9.14 (0.076) 

 

Tests for exclusion from the cointegrating vectorb

HO: 2

χ (1) value(p value) 

 Nearby β N = 0 1.11 (0.291) 

1 yr contract β1 = 0 2.94 (0.087) 

2 yr contract β 2 = 0 2.99 (0.084) 

Tests for weak exogeneityb

HO: 2

χ (1) value (p value) 

 Nearby 

α I =0 0.02 (0.291) 

1 yr contract α B = 0 1.20 (0.273) 

2 yr contract α G = 0 3.09 (0.079) 

Notes: 

a Tests are on eigenvalues with the Π matrix. The λtrace statistic is - ∑= +

−

2

1

( ln(1 )),

i r

N λi where λi are 

ordered (largest to smallest) eigenvalues on Π. 

b

Tests are based on the following: T = N(ln(1-λR) – ln(1-λU), where λR is the eigenvalue 

calculated with the restriction and λU the eigenvalue calculated without the restriction. Based 

upon these results all 3 futures appear to be a part of the co-integrating relationship (at the p = 

0.08 level), and both the nearby and the 1 yr contract are weakly exogenous. 
 34

Table 7. Explaining the Trace Statistic with Position Data from Individual Categories

 

Constant Near 1-year 2-year 

1-year 

+ 2-year 

Near 

+ 1-year 

+ 2-year 2 R

Dealer/Merchants 30.49*** 0.694 -2.874*** 1.240*** 0.03 

Manufacturers 27.59*** 2.543** -1.568** 0.985*** 0.05 

Other Ag & Nat 

Resource 

32.30*** -2.89*** -0.24 0.38*** 0.07 

Producers 26.80*** 4.25*** -2.30*** 0.80*** 0.09 

Swap Dealers 17.92*** 5.68*** 7.14*** -1.17*** 0.40 

Floor 

Brokers/Traders 

22.87*** 5.30*** 0.79 0.60* 0.10 

Hedge Funds 23.61*** 5.08*** 1.10* -0.24 0.16 

Non-reporting 22.14*** 7.54*** 0.10 -0.21* 0.50 

 

Dealer/Merchants 28.77*** 0.527 0.126 0.00 

Manufacturers 26.65*** 2.65** 0.13 0.02 

Other Ag & Nat 

Resource 

32.12*** -2.99*** 0.21** 0.06 

Producers 25.51*** 4.17*** -0.06 0.04 

Swap Dealers 21.30*** 6.34*** 0.96*** 0.35 

Floor 

Brokers/Traders 

22.98*** 5.30*** 0.640*** 0.15 

Hedge Funds 23.91*** 5.20*** 0.22*** 0.16 

Non-reporting 22.19*** 7.59*** -0.11 0.50 

 

Dealer/Merchants 29.15*** 0.13 0.00 

Manufacturers 29.08*** 0.159* 0.01 

Other Ag & Nat 

Resource 

29.13*** 0.14* 0.00 

Producers 29.32*** 0.08 0.00 

Swap Dealers 24.45*** 1.70*** 0.21 

Floor 

Brokers/Traders 

26.57*** 1.00 0.06 

Hedge Funds 28.31*** 0.42*** 0.05 

Non-reporting 27.70*** 0.62*** 0.07 

Columns labeled “Near”, “1-year” and “2-year” contain the parameter estimates associated with nearby, one year 

and two futures positions respectively. “1-year + 2-year” is associated with the sum of one and two year positions 

and “Near + 1-year + 2-year” reflects the pooling of nearby, one year and two year positions. *, **, and *** 

represent significance at the 10%, 5% and 1% levels respectively. All regressions are run using Newey-West 

estimators.
 35

Table 8. Explaining the Trace Statistic with Position Data from Multiple Categories

 (1) (2) (3) (4) 

 Constant 31.61*** 29.96*** 29.12*** 25.50***

Near -6.08*** -4.45*** -3.85*** 

1-year -4.91*** 

2-year 2.71*** 

(1-year + 2-year) -0.21 

Dealer/Merchants 

(Near + 1-year + 2-year) -0.75*** 

Near 0.04 -0.82 0.23 

1-year -0.72** 

2-year -0.30 

(1-year + 2-year) -0.29** 

Manufacturers 

(Near + 1-year + 2-year) -0.45*** 

Near -1.49*** -0.79** -0.60* 

1-year -0.08 

2-year -0.44*** 

(1-year + 2-year) -0.29* 

Other Ag & Nat Resource 

(Near + 1-year + 2-year) -0.15 

Near -3.57*** -2.42** -2.67*** 

1-year 0.12 

2-year 0.01 

(1-year + 2-year) -0.69*** 

Producers 

(Near + 1-year + 2-year) -1.50*** 

Near 1.52 0.09 -1.33 

1-year 3.88*** 

2-year 3.02*** 

(1-year + 2-year) 3.19*** 

Commodity Swap Dealers 

(Near + 1-year + 2-year) 5.55*** 

Near -0.19 -0.40 -0.66 

1-year -0.07 

2-year -0.28 

(1-year + 2-year) -1.15*** 

Floor Brokers/Traders 

(Near + 1-year + 2-year) -1.52*** 

Near -2.73*** -2.44** -2.74*** 

1-year 0.47*** 

2-year -1.59*** 

(1-year + 2-year) -0.12 

Hedge Funds 

(Near + 1-year + 2-year) -0.413 

Near 10.45*** 9.79*** 10.98*** 

1-year 0.21 

2-year -0.96*** 

(1-year + 2-year) 0.10 

Non-reporting 

(Near + 1-year + 2-year) 0.59*** 

2 R 0.72 0.72 0.75 0.56 

Columns labeled “Near”, “1-year” and “2-year” contain the parameter estimates associated with nearby, one year 

and two futures positions respectively. “1-year + 2-year” is associated with the sum of one and two year positions 

and “Near + 1-year + 2-year” reflects the pooling of nearby, one year and two year positions. *, **, and *** 

represent significance at the 10%, 5% and 1% levels respectively. All regressions are run using Newey-West 

estimators. 
34 

Figure 1: Net Positions at ≠ Maturities: Snapshot of Changes Since 2000

Average Daily NET Crude Oil Futures Positions:

Non-commercial Traders

-10000

0

10000

20000

30000

40000

50000

60000

70000

< 3 mos.

3-6 mos.

6-9 mos.

9-12 mos.

1-2 yrs.

2-3 yrs.

3-4 yrs.

4-5 yrs.5-6 yrs.

>6 yrs.

2000

2001

2002

2003

2004

2005

2006

Average Daily NET Crude Oil Futures Positions:

All Commercial Traders

-70000

-60000

-50000

-40000

-30000

-20000

-10000

0

10000

< 3 mos.

3-6 mos.

6-9 mos.

9-12 mos.

1-2 yrs.

2-3 yrs.

3-4 yrs.

4-5 yrs.

5-6 yrs.

>6 yrs.

2000

2001

2002

2003

2004

2005

2006

Notes: Figure 1 shows the average annual net position of commercial and non-commercial traders by contract 

maturity. “Commercial traders” include AD = Dealer/Merchant, AM = Manufacturer, AO = Agricultural and 

Natural Resource - other, AP = Producer, AS = Commodity Swaps/Derivatives Dealer, (which includes FS = 

Financial Swaps / Derivatives Dealer FA = Arbitrageur or Broker/Dealer). “Non-commercial traders” include HF 

= Hedge Fund, FBT = Floor Broker/Floor Trader, , as well as the reported positions of traders not subject to the 

reporting requirement and the positions of traders that have not yet been categorized or do not fit any other category. 

The daily net positions in each contract for each reporting trader are summed up appropriately (e.g., for all 

commercial traders, for all positions less than 3 months, etc.), and the annual averages are then computed. Figure 1 

is directly comparable to Figure 4 below. 
Figure 2: Illustrating the Heterogeneity of the Commercial Trader Group

Average Daily NET Crude Oil Futures Positions:

Non-commercial Traders

-20000-10000

0

100002000030000400005000060000

< 3

mos.

3-6

mos.

6-9

mos.

9-12

mos.

1-2

yrs.

2-3

yrs.

3-4

yrs.

4-5

yrs.

5-6

yrs.

>6

yrs.

20002006

Average Daily NET Crude Oil Future

All Commercial Traders

-60000-50000-40000-30000-20000-10000

0

1000020000

< 3

mos.

3-6

mos.

6-9

mos.

9-12

mos.

1-2

yrs.

2-3

yrs.

3

yr

20002006

Average Daily Net Crude Futures + AO Positions:

Swap Dealers (incl. Commodity Index Traders)

-40000-20000

0

20000400006000080000

100000120000140000160000

< 3

mos.

3-6

mos.

6-9

mos.

9-12

mos.

1-2

yrs.

2-3

yrs.

3-4

yrs.

4-5

yrs.

5-6

yrs.

>6

yrs.

20002006

Average Daily Net Crude Futures 

Traditional Commercial Trade

-160000-140000-120000-100000

-80000-60000-40000-20000

0

2000040000

< 3

mos.

3-6

mos.

6-9

mos.

9-12

mos.

1-2

yrs.

2-3

yrs.

3-

yr

20002006

Notes: Figure 2 shows the average annual net futures + adjusted options (i.e., futures equivalent) positions of commercial and ncontract maturity. “Non-commercial traders” include mostly FBT = Floor Broker/Floor Trader and HF = Hedge Fund. “Commerci

AD = Dealer/Merchant, AM = Manufacturer, AP = Producer, and AS = Commodity Swaps/Derivatives Dealer, (which includes Derivatives Dealer FA = Arbitrageur or Broker/Dealer). The daily net positions in each contract for each reporting trader are summed 

all commercial traders, for all positions less than 3 months, etc.), and the annual averages are then computed. The bottom figures position of all commercial traders between the traditional commercial categories (commercial crude oil dealers, manufacturers, and pr

on the block”, i.e., the Commodity Swaps/Derivatives Dealer. 
Figure 3a: Net Positions of the Major Commercial Trader Types, 2000-2006

-80000-70000-60000-50000-40000-30000-20000-10000

0

10000200003000040000

2000-01

2001-01

2002-01

2003-01

2004-01

2005-01

2006-01

<3mos.4-5 yrs.5-6 yrs.>6 yrs.

Net Positions in Crude Oil Futures + Options Over Time--

Commercial Dealers (AD)

-100000

-90000-80000-70000-60000-50000-40000-30000-20000-10000

0

1000020000

2000-01

2001-01

2002-01

2003-01

2004-01

<3mos.4-5 yrs.5-6 yrs.>6 yrs.

Net Positions in Crude Oil Futures + 

Time--Commercial Manufacture

-60000-50000-40000-30000-20000-10000

0

100002000030000400005000060000

2000-01

2001-01 <3mos.

2002-01

2003-01

2004-01

2005-01

2006-01

4-5 yrs.5-6 yrs.>6 yrs.

Net Positions in Crude Oil Futures + Options Over Time -- 

Commercial Producers (AP)

-10000

0

100002000030000400005000060000700008000090000

100000110000

2000-01

2001-01

2002-01

2003-01

2004-01

<3mos.4-5 yrs.5-6 yrs.>6 yrs.

Net Positions in Crude Oil Futures + O

Time -- Commodity Swap Deale

Notes: Figure 3a shows the average monthly net futures + adjusted options (i.e., futures equivalent) positions, by contract maturity, 

commercial trader types: AD = Dealer/Merchant, AM = Manufacturer, AP = Producer, and AS = Commodity Swaps/Derivatives DeFinancial Swaps / Derivatives Dealer FA = Arbitrageur or Broker/Dealer). The daily net positions in each contract for each report

appropriately (e.g., for all contracts with less than 3 months until maturity, etc.), and the monthly averages are then plotted. The gra

easy comparisons of the relative sizes of the net positions held by different trader categories. Figure 3a is directly comparable to Figure
Figure 3b: Net Positions of the Major Non-Commercial Trader Types, 2000-2006

-40000-30000-20000-10000

0

100002000030000400005000060000700008000090000

100000

2000-01

2001-01

2002-01

2003-01

2004-01

2005-01

2006-01

<3mos.4-5 yrs.5-6 yrs.>6 yrs.

Net Positions in Crude Oil Futures + Options Over 

Time -- Hedge Funds (HF)

-40000-30000-20000-10000

0

100002000030000400005000060000700008000090000

100000

2000-01

2001-01

2002-01

2003-01

2004-01

<3mos.4-5 yrs.5-6 yrs.>6 yrs.

Net Positions in Crude Oil Futures + Optio

-- Floor Brokers & Traders (FB

Notes: Figure 3b shows the average monthly net futures + adjusted options (i.e., futures equivalent) positions, by contract maturity, 

non-commercial trader types: HF = Hedge Fund, FBT = Floor Broker/Floor Trader. The daily net positions in each contract for 

summed up appropriately (e.g., for all contracts with less than 3 months until maturity, etc.), and the monthly averages are then plottedallow for easy comparisons of the relative sizes of the net positions held by different trader categories. Figure 3b is directly comparbelow. 
 40 

Figure 4: Absolute Value of Net Positions at ≠ Maturities: Changes Since 2000

Average Daily Absolute Value of NET Crude Oil 

Futures + Options Positions: Commercial Traders

0

50000

100000

150000

200000

250000

< 3 mos.

3-6 mos.

6-9 mos.

9-12 mos.

1-2 yrs.

2-3 yrs.

3-4 yrs.

4-5 yrs.

5-6 yrs.

>6 yrs.

2000

2001

2002

2003

2004

2005

2006

Average Daily Absolute Value of NET Crude Oil 

Futures + Options Positions: Non-Comm'l Traders

0

50000

100000

150000

200000

250000

< 3 mos.

3-6 mos.

6-9 mos.

9-12 mos.

1-2 yrs.

2-3 yrs.

3-4 yrs.

4-5 yrs.

5-6 yrs.

>6 yrs.

2000

2001

2002

2003

2004

2005

2006

Notes: Figure 4 shows the yearly averages (2000-2006) of the absolute values of the net position of commercial and 

non-commercial traders by contract maturity. “Commercial” traders include AD = Dealer/Merchant, AM = 

Manufacturer, AO = Agricultural and Natural Resource - Other, AP = Producer, AS = Commodity Swaps / 

Derivatives Dealer (which includes FS = Financial Swaps / Derivatives Dealer FA = Arbitrageur or Broker/Dealer). 

“Non-commercial” traders include HF = Hedge Fund, FBT = Floor Broker / Floor Trader, , as well as the reported 

positions of traders not subject to the reporting requirement and the positions of traders that have not yet been 

categorized or do not fit any other category. After taking the absolute value of the daily net positions in each 

contract for each trader group, figures are summed up appropriately (e.g., for all commercial groups, or for all 

positions less than 3 months, etc.), and the annual averages are then computed. Figure 4 is directly comparable to 

Figure 1 above. 
 

Figure 5a: Magnitude of Short- vs. Long-term Net Positions of Commercial Trader Types, 20

0

20000400006000080000

100000120000

2000-01

2001-01

2002-01

2003-01

2004-01

2005-01

2006-01

<3mos.>3 yrs.1-2 yrs.

Absolute Value of Net Positions in Crude Oil Futures 

+ Options Over Time: Commercial Dealers (AD)

a1

0

20000400006000080000

100000120000

2000-01

2001-01

2002-01

2003-01

2004-01

<3mos.>3 yrs.1-2 yrs.

Absolute Value of Net Positions in Cru

+ Options Over Time: Commercial Pr

0

20000400006000080000

100000120000

2000-01

2001-01

2002-01

2003-01

2004-01

2005-01

2006-01

<3mos.>3 yrs.1-2 yrs.

Absolute Value of Net Positions in Crude Oil Futures 

+ Options Over Time: Commodity Swap Dealers (AS)

0

20000400006000080000

100000120000

2000-01

2001-01

2002-01

2003-01

2004-01

<3mos.>3 yrs.1-2 yrs.

Absolute Value of Net Positions in Crud

Options Over Time:Commercial Manuf

Notes: Figure 5a shows the average monthly magnitudes of the net futures + adjusted options (i.e., futures equivalent) positions, by cocommercial trader types: AD = Dealer/Merchant, AP = Producer, AM = Manufacturer,, and AS = Commodity Swaps/Derivatives DeFinancial Swaps / Derivatives Dealer FA = Arbitrageur or Broker/Dealer). After taking the absolute value of the daily net positions 

trader group, figures are summed up appropriately (e.g., for all positions less than 3 months, etc.), and the annual averages are then cscaled to allow for easy comparisons of the relative sizes of the net positions held by different trader categories. Figure 5a is directl

above and to Figure 6a below. 
 

Figure 5b: Magnitude of Short- vs. Long-term Net Positions of the Major Non-Commercial Trader T

0

20000400006000080000

100000120000

2000-01

2001-01

2002-01

2003-01

2004-01

2005-01

2006-01

<3mos.>3 yrs.1-2 yrs.

Absolute Value of Net Positions in Crude Oil Futures + 

Options Over Time: Hedge Funds (HF)

0

20000400006000080000

100000120000

2000-01

2001-01

2002-01

2003-01

2004-01

<3mos.>3 yrs.1-2 yrs.

Absolute Value of Net Positions in C

Options Over Time: Floor Bro

Notes: Figure 5b shows the average monthly magnitudes of the net futures + adjusted options (i.e., futures equivalent) positions, by comajor non-commercial trader categories: HF = Hedge Fund, and FBT = Floor Broker/Floor Trader. After taking the absolute value oeach contract for each trader group, figures are summed up appropriately (e.g., for all positions less than 3 months, etc.), and the 

computed. The graphs are scaled to allow for easy comparisons of the relative sizes of the net positions held by different trader catecomparable to Figure 3b above and to Figure 6b below. 
 

Figure 6a: Magnitude of Short- vs. Very Long-term Net Positions of Commercial Trader Types, 

0

20000400006000080000

100000120000

2000-01

2001-01

2002-01

2003-01

2004-01

2005-01

2006-01

<3mos.4-5 yrs.5-6 yrs.>6 yrs.

Absolute Value of Net Positions in Crude Oil Futures 

+ Options Over Time: Commercial Dealers (AD)

0

20000400006000080000

100000120000

2000-01

2001-01

2002-01

2003-01

2004-01

Absolute Value of Net Positions in 

Options Over Time: Commercia

0

20000400006000080000

100000120000

2000-01

2001-01

2002-01

2003-01

2004-01

2005-01

2006-01

<3mos.4-5 yrs.5-6 yrs.>6 yrs.

Absolute Value of Net Positions in Crude Oil Futures + 

Options Over Time: Commodity Swap Dealers (AS)

0

20000400006000080000

100000120000

2000-01

2001-01

2002-01

2003-01

2004-01

<3mos.4-5 yrs.5-6 yrs.>6 yrs.

Absolute Value of Net Positions in 

Options Over Time: Commercial M

Notes: Figure 6a shows the average monthly magnitudes of the net futures + adjusted options (i.e., futures equivalent) positions, by cocommercial trader types: AD = Dealer/Merchant, AP = Producer, AM = Manufacturer,, and AS = Commodity Swaps/Derivatives DeFinancial Swaps / Derivatives Dealer FA = Arbitrageur or Broker/Dealer).. After taking the absolute value of the daily net position

trader group, figures are summed up appropriately (e.g., for all positions less than 3 months, etc.), and the annual averages are then c

scaled to allow for easy comparisons of the relative sizes of the net positions held by different trader categories. Figure 6a is directland 5 above. 
 

Figure 6b: Magnitude of Short- vs. Very Long-term Net Positions of the Major Non-Commercial Trade

0

20000400006000080000

100000120000

2000-01

2001-01

2002-01

2003-01

2004-01

2005-01

2006-01

<3mos.4-5 yrs.5-6 yrs.>6 yrs.

Absolute Value of Net Positions in Crude Oil Futures + 

Options Over Time: Hedge funds (HF)

0

20000400006000080000

100000120000

2000-01

2001-01

2002-01

2003-01

2004-01

<3mos.4-5 yrs.5-6 yrs.>6 yrs.

Absolute Value of Net Positions in C

Options Over Time: Floor Brokers 

Notes: Figure 6b shows the average monthly magnitudes of the net futures + adjusted options (i.e., futures equivalent) positions, by comajor non-commercial trader categories: MMT = Managed Money Trader, and FBT = Floor Broker/Floor Trader. After taking the abs

positions in each contract for each trader group, figures are summed up appropriately (e.g., for all positions less than 3 months, etc.), a

then computed. The graphs are scaled to allow for easy comparisons of the relative sizes of the net positions held by different tradedirectly comparable to Figures 3b and 5b above. 
 

Figure 7. Recursively Calculated Trace Test Statistic Scaled by the 5% Critical value for Nearby, One & T

0

0.5

1

1.5

2

2.5

1/4/00 6/4/00 11/4/00 4/4/01 9/4/01 2/4/02 7/4/02 12/4/02 5/4/03 10/4/03 3/4/04 8/4/04 1/4/05 6/4/05 1

Date

Standardized Trace Statistic

The R-1 form of the trace statistic is represented by the light (red) line. The 5% critical value is represented by the solid black line. 
 

Figure 8. The Time Paths of the Speed of Adjustment Parameters (and 5% confidence inte

Nearby Futures Contract 

-3

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

1/4/2000 6/4/2000 11/4/2000 4/4/2001 9/4/2001 2/4/2002 7/4/2002 12/4/2002 5/4/2003 10/4/2003 3/4/2004 8/4/2004 1/4/2005 6/4/20

Date

Speed of Adjustment 

Parameter

One Year Futures Contract 

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1/4/2000 6/4/2000 11/4/2000 4/4/2001 9/4/2001 2/4/2002 7/4/2002 12/4/2002 5/4/2003 10/4/2003 3/4/2004 8/4/2004 1/4/2005 6/4/20

Date

Speed of Adjustment 

Parameter

Two Year Futures Contract 

-1.2

-1

-0.8-0.6-0.4-0.2

0

0.20.40.6

1/4/2000 6/4/2000 11/4/2000 4/4/2001 9/4/2001 2/4/2002 7/4/2002 12/4/2002 5/4/2003 10/4/2003 3/4/2004 8/4/2004 1/4/2005 6/4/20

Date

Speed of Adjustment 

Parameter