Electronic copy available at: http://ssrn.com/abstract=966692

December 4, 2008 

Fundamentals, Trader Activity and Derivative Pricing 

Bahattin 

Büyükşahin 

Michael S. 

Haigh 

Jeffrey H. 

Harris 

James A. 

Overdahl 

Michel A. 

Robe*

Abstract 

We identify and explain a structural change in the relation between 

crude oil futures prices across contract maturities. As recently as 2001, 

near- and long-dated futures were priced as though traded in segmented 

markets. In 2002, however, the prices of one-year futures started to move 

more in sync with the price of the nearby contract. Since mid-2004, the 

prices of both the one-year-out and the two-year-out futures have been 

cointegrated with the nearby price. We link this transformation to changes 

in fundamentals, as well as to sea changes in the maturity structure and 

trader composition of futures market activity. In particular, we utilize a 

unique dataset of individual trader positions in exchange-traded crude oil 

options and futures to show that increased market activity by commodity 

swap dealers, and by hedge funds and other financial traders, has helped 

link crude oil futures prices at different maturities. 

Keywords: Crude Oil Futures, Maturity, Segmentation, Cointegration, 

 Trading Activity, Fundamentals. 

JEL codes: G10, G13, N22 

* Büyükşahin: Economist, Commodity Futures Trading Commission (CFTC), 1155 21st Street NW, Washington, DC 

20485. Email: bbuyuksahin@cftc.gov. Haigh: Managing Director, Commodities Derivatives Trading, Société 

Générale Corporate and Investment Banking, 1221 Ave. of the Americas, New York, NY 10020. Harris: Chief 

Economist, CFTC and Professor of Finance, Alfred Lerner College of Business and Economics, University of 

Delaware, 320 Purnell Hall, Newark, DE 19716. Email: jharris@cftc.gov. Overdahl: Chief Economist, U.S. 

Securities and Exchange Commission (SEC), MS 9360, 100 F Street, NE, Washington, DC 20549. Email: 

overdahlj@sec.gov. Robe (corresponding author): Senior Economist (IPA), CFTC and Associate Professor of 

Finance, Kogod School of Business at American University, 4400 Massachusetts Avenue NW, Washington, DC 

20016. Tel: 202-885-1880. Email: mrobe@american.edu. We are grateful to Steve Cho for help with the data and for 

useful comments, and to Kirsten Soneson for research assistance. Thanks also to Hank Bessembinder, David Bessler, 

Celso Brunetti, Bo Collins, Jennifer Huang, Vince Kaminski, Andrei Kirilenko, Pat McCarthy, Jim Moser, John 

Parsons, David Reiffen, Steve Sharpe, Andy Smith, Rob Vigfusson and participants to the workshop on the Impact of 

Financial Markets on the Price of Oil (Vienna) and on Energy and Environmental Policy Research (Massachusetts 

Institute of Technology); to seminars at the Federal Reserve Board, the International Monetary Fund, the Universities 

of Connecticut, Richmond, and Victoria; and the 2008 de Groote—IIROC conference on Market Structure and 

Integrity (Toronto), for helpful comments on earlier drafts. This paper reflects the opinions of its authors only, and 

not those of the CFTC, the SEC, the Commissioners, or other staff upon either Commission. All remaining errors and 

omissions, if any, are the authors’ sole responsibility. 
Electronic copy available at: http://ssrn.com/abstract=966692

 1

I. Introduction.

Derivative markets have experienced unprecedented levels of activity in the past decade. In 

the case of commodities, part of this growth stems from financial investors’ desire for exposure to 

commodity prices. We utilize a unique dataset of trader positions in exchange-traded crude oil 

options and futures, to provide the first formal evidence of a transformation at the world’s largest 

futures market for a physical commodity.1

 In particular we show that, together with changes in the 

fundamentals of the underlying commodity, increased market activity by commodity swap dealers, 

and by hedge funds and other financial traders, has had a major impact on linking crude oil futures 

prices at different maturities. 

 Under standard no-arbitrage arguments, spot and futures commodity prices are linked by a 

net cost-of-carry factor that accounts for the time value of money, storage costs, and a possible 

convenience yield. In theory, futures prices along the term structure should likewise be related 

through similar cost-of-carry equations. We document that a stable long-run relation has existed 

since the early 1990s between the prices of futures contracts with less than a year to maturity.2

 The 

same is not true, however, for longer-maturity contracts. Until 2001, we find that contracts dated 

one year and beyond were priced as though traded in segmented markets. After 2001, the price of 

one-year futures began to move in sync with the nearby price and, since mid-2004, both one-year 

and two-year futures prices have become cointegrated with the nearby price. 

This development is economically significant. A critical issue, for many financial or 

commodity market participants, is whether a long-term relation exists between the prices of some 

financial instruments even though these prices may diverge in the short run (Kasa, 1992). Such 

long-run co-movements are precisely the ones that the cointegration analysis identifies (Engle and 

Granger, 1987; Johansen, 1988). One implication is that long-term hedging strategies should be 

more effective when they are based on cointegrated futures contracts, i.e., when nearby and 

backdated contracts are not segmented. Conversely, a lack of market integration across contract 

maturities could be deleterious to traders who might otherwise rely on its existence for price 

discovery or hedging purposes. 

To ascertain what caused the pricing changes, we compute a rolling measure (the trace 

statistic) to capture changes over time in the extent to which futures prices are cointegrated. We 

then identify and test the economic and financial variables that explain the evolution of this trace. 

 

1

 Total open interest in the New York Mercantile Exchange’s (NYMEX) West Texas Intermediate (WTI) crude oil 

contracts was a notional $322bn at the end of August, 2008. 

2

 Quan (1992) documents cointegration between spot and near-term futures (less than three month) in the 1980s. 
 2

One candidate explanation is that the fundamentals of crude oil changed, so that either the 

stochastic process driving the spot price (Bessembinder et al., 1995) or the time-series properties of 

the net cost-of-carry (Brenner and Kroner, 1995) mutated. Using a time series of real-sector 

variables, such as the non-Saudi spare crude oil production capacity and different proxies for the 

strength of world demand for industrial commodities, we identify breaks in late 2003 to early 2004 

that help explain the development of futures price cointegration over time. Market fundamentals do 

play a role in the emergence of cointegration. 

Visual inspection and Granger causality tests, however, suggest that changing fundamentals 

are not the sole cause of greater cointegration. Intuitively, cointegration should result from arbitrage 

activities that link futures prices at different maturities. In practice, arbitraging may be limited by 

financial or other market constraints. As noted in Başak and Croitoru (2006), the arrival of new 

types of traders who face fewer constraints can help alleviate price discrepancies and improve the 

transfer of risk between market participants. Still, even such less-constrained arbitrageurs may be 

unwilling to trade illiquid instruments. That is, a “thick” market” is needed for effective arbitrage 

strategies (e.g., Admati and Pfleiderer, 1988; Roll, Schwartz, and Subrahmanyam, 2007).3

 

To explore whether and how futures trading-related factors contribute to cointegration, we 

use non-public data from the U.S. Commodity Futures Trading Commission’s (CFTC) large trader 

reporting system that identifies daily individual trader positions at various contract maturities and 

classifies the traders according to their underlying business. As a result, we are able to analyze the 

positions of eight types of participants in the WTI crude oil market– four “traditional commercial” 

categories, including producers, manufacturers, and dealers or merchants; three “non-commercial” 

categories, including floor brokers and traders, hedge funds, and non-registered participants; and, 

“commodity swap dealers.”4

 The latter’s positions reflect over-the-counter swap transactions with 

commercial entities as well as with commodity index funds and other financial investors. 

 

3

 Interviews with major players in energy derivative markets are consistent with this intuition, and provide a clue as to 

the sequence of events involved. They suggest that the expectation of stronger fundamentals for industrial commodities 

(the anticipation of robust economic growth coupled with extraction capacity constraints) stoked investor interest in 

these assets, which led to an increase in commodity futures market participation by financial institutions and by the 

intermediaries trading with them. In particular, these interviews suggest that greater crude oil futures trading by 

commodity swap dealers amid a commodity-index investment boom, together with the growth of swap dealers’ 

positions in medium- and long-term contracts that started in 2003 and strengthened after oil prices became contangoed 

in 2004, were important factors behind the increased pricing convergence. 

4

 Reporting traders account on average for 94% of the total WTI open interest during our sample period. The CFTC 

provides public data on trader positions in weekly Commitment of Traders (COT) Reports. These reports, however, 

aggregate large traders into two broad, heterogeneous groups (Commercial vs. Noncommercial) and do not break down 

activity by contract maturity. On January 5, 2007 the CFTC began publishing COT—Supplemental reports that identify 

Commodity Index Traders specifically, but only for twelve agricultural commodities (not for energy contracts). 
 3

Futures and options open interest quintupled between 2000 and 2008. We show that this 

growth involved a considerable lengthening in the maturity structure, and a sea change in the trader 

composition, of market activity. More explicitly, we identify three trends in futures trading-related 

data that speak to the development of cointegration of prices across the futures term structure. One, 

open interest at maturities greater than one year grew nearly twice as fast as open interest at shorter 

maturities. Several categories of traders now carry much larger long-dated positions (one year or 

more) than they held in near-dated contracts (three months or less) in 2000. Two, swap dealers’ 

market share grew markedly during the key years of 2002 and 2003 amid the start of a commodityindex investment boom. Three, “traditional commercial” aggregate market share has halved since 

2000 as financial traders greatly expanded calendar-spread trading. Indeed, the market share of 

financial traders has more than doubled, from less than 20 percent of all open futures and futuresequivalent option positions in 2000 to more than 40 percent in 2008. 

 Armed with these stylized facts, we use the autoregressive distributed lag modeling 

approach to cointegration analysis in order to test whether there exists a long-term relation between 

the strength of price cointegration and the positions of different types of trader. We control for 

fundamentals that might affect cointegration: spare crude oil production capacity and two proxies of 

world demand for commodities (an index of non-exchange-traded commodity prices, and the cost of 

shipping dry freight in bulk). We also control for an exogenous liquidity change (the advent of 

electronic trading); for the general cost of liquidity (the slope of the term structure of near-term 

crude oil prices, which captures the cost of maintaining an exposure to crude oil prices by means of 

a long position in the nearby contract); and, for cross-sectional differences in liquidity (to allow for 

the fact that most longer-term trading involves contracts maturing in June or December). 

In this multivariate setting, we show that fundamentals help to explain the cointegration of 

futures prices across the term structure – fundamentals matter. Furthermore, we find that the futures 

market activities of some types of traders (but not others) also help explain cointegration – strong 

evidence that the composition of trading interests also matters. 

Specifically, we show that the strength of the cointegration between the nearby, one-year 

and two-year crude oil futures price series is linked to the market activities of financial traders, 

especially in one- and two-year contracts, as well as to the activity of commodity swap dealers in 

nearby contracts (activity that echoes in large part their commodity index swap business). By 

contrast, we find little evidence of a positive connection between cointegration and traditional 

commercial trading or commodity swap dealer activity in one- and two-year contracts (activity 
 4

likely to result from swap dealers’ commodity-index business). Together, our empirical results 

suggest that increased participation by financial traders, as well as by commodity swap dealers, 

enhance linkages among various futures prices. In this regard, our results illustrate how greater 

market activity by some types of traders can enhance market quality in commodity markets. 

 The remainder of the paper proceeds as follows. Section II summarizes our contribution to 

the literature. Section III documents changes in the relation between different-maturity crude oil 

futures. Section IV documents major changes in the maturity structure and the trader composition 

of the crude oil futures market since 2000. Section V develops testable hypotheses to explain the 

pricing changes. Section VI analyzes the impact of changes in crude fundamentals and in futuresmarket characteristics on WTI futures pricing. Section VII concludes. 

II. Related Work. 

 The present paper is related to several strands of the finance and economics literatures. In 

economics, it is part of a sizeable literature on the role of crude oil fundamentals on macroeconomic 

aggregates and financial variables.5

 In particular, Kilian and Park (2007) show that the response of 

U.S. stock returns to an oil price increase depends on whether the increase is the result of a demand 

or a supply shock in the crude oil space. Rather than assess the impact of commodity fundamentals 

on price levels in securities or derivatives markets, our analysis establishes that fundamentals are 

relevant in terms of the linkages between crude oil futures prices at different maturities. 

 In finance, our analysis adds to extant work on financial market integration and to a large 

body of research on the roles of different kinds of traders in financial markets. We also provide the 

first detailed characterization of hedging and speculative activity in the crude oil futures market, and 

the first study to document the development of a market for long-dated commodity futures. 

 We show that, although long-dated crude oil futures were priced as though segmented from 

near-dated contracts until 2003, these price series are now cointegrated. Our findings complement 

papers that document greater integration between international equity markets in the last two 

decades (e.g., Bekaert, Harvey and Lumsdaine, 2002; Carrieri, Errunza and Hogan, 2007). Unlike 

those papers, which test the equality of the prices of risk across geographic boundaries, our focus is 

on identifying cointegration across the maturity structure in a single market. In that sense, our 

paper is also related to studies that document cointegration across the Treasury yield curve (Hall, 

Anderson and Granger, 1992; Bradley and Lumpkin, 1992) or between spot and near-dated crude 

 

5

 See, e.g., Hamilton (2005), Kilian (2008a), and papers cited therein. 
 5

oil futures prices (Quan, 1992). Unlike those papers, we show that the relation between prices across 

near- and far-dated contract maturities has changed over time, and relate the extent of cointegration 

to changes in fundamentals as well as in the amount and composition of market activity. 

 In particular, we show that financial traders who may have little interest per se in the 

underlying commodity can add important dimensions toward integrating the related derivative 

market and making commodity markets more informationally efficient. Our empirical results are 

consistent with the notion that greater participation by less-constrained arbitrageurs helps to 

alleviate price discrepancies, as modeled by Başak and Croitoru (2006). Our results also provide 

evidence that the growth in swap dealers’ futures market participation helps explain the improving 

price discovery among nearby, one- and two-year futures contracts. To the extent that swap 

dealers’ increased activity in near-term contracts echoes an increase in commodity index investment 

flows, our findings support other previous empirical evidence that greater uninformed liquidity 

trading is related to the efficacy of arbitrage activities (Roll et al., 2007). 

 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 in recent years. 

Much of this scrutiny focuses on the concern that speculators, including hedge funds, may exert a 

destabilizing effect on financial markets, which could ultimately lead to higher trading costs.6

 In 

U.S. markets, Brunnermeier and Nagel (2004) find that hedge funds did not exert a correcting force 

on stock prices during the technology bubble. Haigh, Hranaiova and Overdahl (2007) find that 

hedge fund activity does not affect price levels in energy futures markets, yet hedge funds are very 

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

We assess the impact of different groups of traders, including hedge funds, on the linkages between 

market compartments – rather than on the absolute price levels in a single market.7

 Our results 

complement this extant research, as we find that hedge fund trading activity is beneficial in that it 

contributes to bringing in line the prices of commodity futures at different maturities. 

 Our research yields the first detailed depiction of activity on the world’s largest commodity 

futures market. A number of authors have used the CFTC’s publicly-available Commitment of 

 

6

 Chan, Getmansky, Haas and Lo (2006) 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 hedge funds had 

a significant market impact during the European Exchange Rate Mechanism crisis in the early 1990s. By contrast, 

Choe, Kho and Stulz. (1999), Fung, Hsieh and Tsatsaronis (2000), and Goetzmann, Brown and Park (2000) conclude 

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

7

 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. 
 6

Traders (COT) reports, mostly to shed light on speculative and hedging activity in futures markets.8

 

The COT data, however, are highly aggregated: they only differentiate between two broad types of 

traders (“commercial” vs. “non-commercial”). Our more detailed data allow us to separate the 

greatly increased activity of some kinds of traders (especially, financial traders and commodity 

swap dealers) from the lesser growth of futures market activity of other categories of participants 

(e.g., crude oil producers and manufacturers). These differences matter: to wit, we show that the 

activities of only some categories of traders help explain changes in market integration.9

 

 The public COT data, furthermore, do not break down trader activity at different maturities. 

Identifying the differential evolutions of the near- and far-ends of the market is a major contribution 

of the present study. To our knowledge, the only other papers with some formal evidence on the 

maturity structure of oil (or any commodity) markets precede the development of long-term futures 

(Neuberger, 1999 and Ederington and Lee, 2002).10 

 Indeed, although crude-oil futures with maturities of up to seven years have been available 

since 1997, the view that longer-term futures may be too illiquid to be useful for hedging purposes 

has remained a piece of conventional wisdom. Papers devising short-term hedging strategies for 

long-term price risk are still predicated on the notion that long-maturity contracts, if at all available, 

are highly illiquid – see, e.g., Veld-Merkoulova and de Roon (2003). We show that, since 2004, 

this conventional wisdom no longer applies in that (i) market activity in very-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 further-out contracts have become cointegrated. 

III. Changes in the WTI Crude Oil Market: Pricing. 

 The NYMEX first successfully introduced WTI light sweet crude oil futures in March, 1983. 

In the following ten years, there was little trading beyond the first few maturity months (Simon and 

Lautier, 2005). The NYMEX started listing December contracts with maturities up to three, seven 

 

8

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

(2000), de Roon, Nijman and Veld (2000), Wang (2003), Gorton, Hayashi and Rouwenhorst (2007), and Piazzesi and 

Swanson (2008). 

9

 Only a handful of other studies use disaggregated, non-public CFTC data. They are Ederington and Lee (2002), who 

analyze heating-oil NYMEX futures position from June 1993 to March 1997; Chang, Pinegar and Schachter (1997), 

whose dataset includes six futures markets from 1983 to 1990; and Haigh et al (2007), who analyze possible linkages 

between hedge fund activity and energy futures market volatility between August 2003 and August 2004. 

10 Many papers, in contrast, analyze the term structure of futures crude oil prices. See e.g. Litzenberger and Rabinowitz’ 

(1995) investigation of backwardation; Routledge, Seppi and Spatt’s (2000) model and numerical analysis of forward 

pricing; and Lautier’s (2005) study of pricing determinants for short-, medium- and long-dated crude oil futures. 
 7

and nine years in 1990, 1997 and 2007, respectively. Since March 2006, all maturity months up to 

five years (not just June or December contracts) have been listed for trading on the exchange. 

A stable long-run relation has existed since the 1980s between crude oil’s spot, nearby and 

three-month futures prices (Quan, 1992). In this Section, we use a recursive cointegration analysis 

to show that the same is not true for the prices of longer-dated futures. 

Our main focus is on one- and two-year-out futures (two years is the outermost maturity for 

which a long enough continuous time series exists of weekly crude oil futures settlement prices). 

To provide a context for the extent to which the nearby, one-year and two-year-out futures prices 

are cointegrated, we also provide cointegration results for the nearby and next two nearby months; 

and, for the prices of the nearby, six-month, and nine-month futures. 

We establish that a stable long-run relation has existed since the early 1990s between the 

prices of futures contracts with less than a year to maturity.11 Contracts dated one year and beyond, 

however, started only more recently to move in sync with the nearby price. It is only since mid2004 that one-year and two-year futures prices have been cointegrated with the nearby price. 

A. Data 

We use NYMEX Tuesday settlement prices from March 1989 to August 2008 for the 

nearby, next two nearby months, six-month, nine-month, and one-year contracts; and, from 

September 1995 to August 2008, for the two-year contract. March 1989 (September 1995) is the 

earliest date from which a continuous daily price series can be created for the one-year (two-year) 

futures. This continuity is necessary to create rolling daily positions at different maturities while 

ensuring that the intervals between these maturities remain constant. 

A point of terminology is in order at this stage. Very few WTI crude oil futures traders take 

delivery. As a result, open interest in the prompt-month WTI futures drops precipitously, as that 

contract approaches its expiration date and traders who wish to retain their crude oil exposures roll 

their positions into the next-month contract. At the same time, arbitrage during the last days of a 

contract's life becomes more difficult because it entails carrying a cash market position – which may 

“disconnect” the prompt contract’s price from other futures prices and make the nearby price more 

volatile than the farther out months. Because our interest is in the most liquid short-dated contract, 

we therefore label “nearby contract” the short-dated futures contract with the highest open interest. 

 

11 In the 1980s, even the prices of six- and nine-month futures were not cointegrated with the nearby price (Quan, 1992). 
 8

In practice, this definition means that the “nearby price” typically stands for the price of the promptmonth contract through the seventh or eighth business days of the expiry month, and the price of 

next-month contract for the remainder of that month.12 

Figure 1 and Table 1 provide information on the nearby, one-year and two-year price series 

from July 2000 to August 2008. Two facts emerge. One, the maximum of each price series during 

the sample period is about seven times larger than the corresponding minimum, reflecting major 

price increases in the second half of our sample period. Two, the unconditional correlations 

between the returns on the three series are also much higher in the second half of the sample period. 

B. Cointegration Analysis 

 Economic theory generally deals with equilibrium relationships. Empirical econometric 

studies attempt to evaluate such relationships by summarizing economic time series using statistical 

analysis. Applying standard inference procedures in a dynamic time series model, however, requires 

that the various variables be stationary, since the majority of economic theory is built upon the 

assumption of stationarity. Therefore, before assessing pricing relations in the crude oil market, it is 

useful to determine the orders of integration for the variables considered. For this purpose, we 

perform stationarity tests employing the Augmented Dickey Fuller (ADF) unit root tests. 

 Table 2 shows that the nearby, one- and two-year futures price series are all nonstationary 

but become stationary after taking first differences, i.e., they are all I(1) or integrated of order one. 

Thus, we cannot rely on inferences based on simple OLS regressions involving these variables.13 

 The use of non-stationary variables does not necessarily result in invalid estimators. An 

important exception arises when two or more I (1) variables are cointegrated (Engle and Granger, 

1987); that is, there exists a particular linear combination of these non-stationary variables which is 

stationary. For example, if Yt and Xt are both I(1) and there exists a β such that Zt=Yt-βXt is I(0), 

then Yt and Xt are said to be cointegrated, with β being called the cointegrating parameter. 

Johansen (1988) proposes and implements a unified vector autoregressive system approach 

for testing cointegration. He derives the maximum likelihood estimator of the space of cointegration 

 

12 Robustness analyses using calendar dates (rather than open interest) to time the roll yield qualitatively similar results. 13 OLS estimators would have sampling distributions that are very different from those derived under the assumption of 

stationarity. In particular, if the variables are non-stationary and not cointegrated, then the OLS estimator does not 

converge in probability as the sample size increases; the t- and F- statistics do not have well defined asymptotic 

distributions; and, the Durbin-Watson statistic converges to zero. Put differently, OLS regression results might be 

spurious in that, even if there is no clear relation between the variables, we could still find a statistically significant 

relationship due to the fact that they share a trend. 
 9

vectors and the likelihood ratio test of the hypothesis that it has a given number of dimensions. The 

procedure involves the following stages: 

• Model checking, determination of lag length; 

• Determination of cointegration rank based on trace statistics; 

• Estimation of the cointegration space. 

The first step of the model building involves the choice of lag order. The most common 

procedure is to estimate a vector autoregression using the undifferenced data, and to then use one of 

several information criteria to select the number of lag lengths. We use the Schwarz information 

criterion (SC) to determine the optimal lag. The optimal number of lag is 2 in our case. 

After selecting the lag length, the Johansen procedure estimates a vector error correction 

model (VECM) to determine the number of cointegrating vectors. According to Johansen (1988), a 

general polynomial distributed lag process, t x , involving up to k lags, can be written as: 

t t k t k ut x = Π1 x −1 + ... + Π x − + (1)

 

where t x is a vector of n variables of interest, Πi is an (n× n) matrix of parameters, and u is n -

dimensional Gaussian independently distributed random variables with mean zero and variance 

matrix Λ. This equation can be reformulated into VECM form: 

t t k t k t k ut Δx = (2) Γ1Δx −1 + ... + Γ −1Δx − +1 + Θx − +

where j

k Γi = −Σ j=i+1Π , ( 1 i =1,2,..., k − ), and = ( ) =1 I i

k Θ Σi Π − . This way of specifying the system 

contains information on both the short- and long-run adjustments to changes in t x , via the estimates 

of Γi

ˆ and Θˆ , respectively. Assuming that t x is (1) I , while r linear combinations of t x are 

stationary, we can write 

Θ = αβ′, (3) 

where α is the vector of adjustment coefficients; β is the cointegrating vector; and both are ( n× r ) 

matrices. The approach of Johansen (1988) is based on the estimation of system (2) by maximum 

likelihood, while imposing the restriction in (3) for a given value of r . Johansen demonstrates that 

β can be estimated by regressing ΔXt and Xt−k on the lagged differences. 
 10

The next step in the Johansen approach involves testing the hypothesis about the rank of the 

long run matrix Θ , or equivalently the number of columns in β . The likelihood ratio test for the 

determination of the rank r is discussed in Johansen (1992). In general, tests of the hypothesis that 

r ≤ q use the likelihood ratio test statistics: 

 ) ˆ ( ) = = 1 (1 j

k

trace j q λ q −TΣ + log − λ (4) 

This test is called the trace test. It checks whether the smallest k − q eigenvalues are significantly 

different from zero. 

Most of the existing Monte Carlo studies on the Johansen methodology point out that the 

dimension of the data series for a given sample size may pose particular problems, because the 

number of parameters of the underlying VAR models grows very large as the dimension increases. 

Likewise, difficulties often arise when a given lag length of the system is either over or under 

parameterized. Reimers (1992) argues that, for small samples, the Johansen procedure over-rejects 

when the null is true. To correct this bias, Reimers suggests an adjustment in the degrees of 

freedom in the trace statistic by replacing T by T − nk for small samples. The corresponding 

degrees-of-freedom-adjusted trace can be written as: 

 ) ˆ ( ) = ( ) = 1 (1 j

k

j q

a

trace λ q − T − nk Σ + log − λ (5) 

In this study, we define Yt as composed of three elements. They represent the nearby, oneyear-out, and two-year-out price series for WTI crude oil futures contracts. Table 3 (discussed in 

the next sub-section) reports the Johansen trace test for the number of cointegrating relations, i.e., 

the cointegration rank. The VAR for the Johansen procedure has 2 lags of each variable, thereby 

allowing for one month of growth rates of the variables. The choice of the cointegration rank is 

very important in the Johansen methodology because the rest of the analysis is affected by this 

choice. We use the likelihood ratio test (i.e., the trace test) to determine the cointegration rank. The 

latter divides the data into r relations towards which the process is adjusting and p-r relations which 

are pushing the process. The first set of relations can be interpreted as equilibrium errors, and the 

second set can be viewed as the common driving factor in the system. If, for example, r=0 (no 

cointegration), then we have “p unit roots”, i.e., common driving trends in the system. 

 Panel A in Table 3 presents cointegration rank results for the whole sample period. Panel A 

should be read from top to bottom. We start by testing the hypothesis that all roots are unit roots 
 11

(r=0, p-r=3), which we clearly reject. Next, we test for two unit roots (r=1, p-r=2); this is also 

rejected by the corrected test statistics at any standard level of significance. However, when we 

move to the case of one unit root, we cannot reject this null hypothesis using P-value at any given 

standard level of significance. Therefore, the rank test statistics implies that there is at most one 

cointegrating relation at standard levels of significance. 

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). The results are presented in the lower portion of Table 3. In 

particular, Panel B explores the possibility that one of the three series is not in the cointegrating 

space. While the exclusions of one-year- and two-year-out contracts from the cointegration relation 

can be accepted at any standard level of significance, the economic importance of these prices 

requires that they be kept in the analysis. With respect to the short-run adjustment toward the long 

run relations,α , we also test for weak exogeneity on each market. For each futures maturity, we 

test for whether or not that futures price responds to perturbations in the cointegrating space. 

Inspection of Panel C in Table 3 suggests that the nearby contract is weakly exogenous and that the 

1 year and 2 year contracts do all the adjusting to the long-run equilibrium. 

C. Dynamics of the Relation between Nearby and Long-dated Futures Prices 

 In order to study the development of the level of cointegration among contracts of differing 

maturities, we examine the dynamics and extent of relationship (if any) between the three prices 

using the recursive cointegration method outlined in Hansen and Johansen (1993). This technique 

has been employed in many studies of convergence across stock markets or interest rates in 

different countries.14 

 The recursive cointegration technique lets us to test for changes in the level of cointegration 

among prices during our sample period. It allows us to recover two error-correction-model (ECM) 

representations. In the “Z-representation,” all the parameters of the ECM ( β and α ) are reestimated 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. 

We recover the R-representation, since our focus is on changes in the long-run relation between the 

different price series. 

 

14 See, e.g., Bremnes, Gjerde and Saettem (1997), Bessler & Yang (2003), and Wang, Yang and Li (2007). 
 12

The logic behind the recursive cointegration technique is similar to Johansen’s (1988) 

multivariate cointegration approach. Instead of using all of the observations, we start with an initial 

sample period from t0 to tj (for some choice of j) and calculate the trace statistics for this subsample. Then, we increase the sample size by 1 from t0 to tj+1 (i.e., we add one more week in each 

step) and calculate the relevant trace statistics for this sample period. This process continues until 

we exhaust all the observations. In the final stage, we perform the cointegration analysis for the full 

sample and calculate the trace statistics. Of course, the trace statistic calculated in the final stage 

equals the standard static trace statistic calculated with the Johansen (1991) method. The recursive 

method, however, is far more informative as it allows us to see the dynamics of the trace statistic. 

 Figure 2 presents a time series plot of the normalized trace statistic for each week from 1992 

onwards. We use a three-year period for the initialization; hence, since daily price data are available 

for the 1-year (2-year) contract from 1989 (1995) onwards, the results start in 1992 (1998) for tests 

involving maturities up to one year (two years). 

 We re-scale all trace statistics in Figure 2 by the 95% quantile of the trace distribution derived 

for the selected model without exogenous variables or dummies (Johansen and Juselius, 1992). A 

normalized trace statistic above 1 suggests the rejection of the null hypothesis of no cointegration at 

the 5% level of significance. In addition, to see whether there exists a cointegrating vector among our 

variables, the slope of re-scaled trace statistic determines the direction of co-movements between our 

variables. An upward slope indicates rising co-movement, while a downward slope for the trace 

statistic reveals declining co-movement between our variables. 

 The top curve on the graph (dark blue) shows the scaled trace statistic for the “Nearby”, 

“Nearby + 1 month” and “Nearby + 2 months” contracts. For these near-term price series, there is 

strong evidence of a cointegrating vector throughout the sample period. The next highest curve 

(drawn in green) shows the scaled trace for the “Nearby”, “Nearby + 6 months” and “Nearby + 9 

months” contracts. For these three price series, there is statistical evidence of a cointegrating vector 

from 1992 onward, and of a strengthening of the cointegration after mid-1998. 

 The third curve starting in 1992 (drawn in red, starting close to 0.3) shows the scaled trace 

for the “Nearby” and “Nearby + 12 months” (i.e., one-year-out) contracts. These two price series 

are indubitably cointegrated after July 2003, with the statistical strength of the cointegration 

increasing greatly after May 2004. Finally, the black curve (starting in 1995) shows the scaled trace 

statistic for the “Nearby”, “Nearby+12 months”, and “Nearby+24 months” contracts. Whereas 

these three contracts were not in long-run cointegration before 2001 (the trace value is well below 
 13

1), the trace increased toward 1 (moving toward cointegration) in 2002 and 2003. By mid-2004, the 

series were cointegrated. They have remained cointegrated ever since. 

IV. Changes in the Crude Oil Market: Growth, Trader Composition, and Maturity Structure. 

 In Section III, we identify significant changes since 2000 in the relation between crude oil 

futures prices. In this Section, we identify major changes in the makeup of the crude oil futures 

market. WTI futures and options open interest quintupled between 2000 and 2008. We show that 

this growth involved a considerable lengthening in the maturity structure, and a sea change in the 

trader composition, of market activity. 

 We do so by utilizing non-public CFTC data on open trader positions in exchange-traded 

commodity futures and options on futures. Section IV.A describes our dataset, and contrasts it to 

the less-detailed information that is publicly available. Section IV.B summarizes the overall market 

growth. Section IV.C identifies which types of traders contributed the most to this growth. Section 

IV.D contrasts activity patterns and growth at short, medium, long and very long maturities. 

A. Data Sources 

We construct a database of daily position for NYMEX light sweet crude oil (WTI) futures 

and options-on-futures contracts from the first week of July, 2000 to the last week of August, 2008. 

The raw position data we utilize and the trader classifications on which we rely originate from the 

CFTC’s Large Trader Reporting System (LTRS). 

Specifically, to help it fulfill its mission of detecting and deterring 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. The CFTC also collects 

information from all large traders about the purposes of their NYMEX positions, and classifies 

these traders according to their underlying businesses. The position 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, this threshold was 350 contracts in our sample period. Many smaller positions 

are also voluntarily reported to the CFTC and are included in the database. As a result, our dataset 

contains more than 90% of all WTI positions. 

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

here we focus on the Tuesday reports. The weekly frequency matches the frequency at which we 
 14

sample prices for the analyses in Sections III and VI. Note that 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 directly comparable with 

those of numerous extant studies that rely on COT data.15 

1. Public Data: “Commercial” vs. “Non-Commercial” Traders

 Every Friday, the CFTC publishes a weekly crude oil COT report that contains the overall 

open-interest figure. This report does not break down open interest figures by contract maturity. It 

does, however, provide some additional information on different traders’ positions by separating the 

reporting traders between one of two broad categories – “Commercial” and “Non-commercial.”16 

 The CFTC classifies all of a trader's reported futures and options positions in a given 

commodity as “Commercial” if the trader uses futures contracts in that particular commodity for 

hedging as defined in CFTC 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 accurately 

and consistently, the CFTC 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. 

 Table 4 illustrates the level of disaggregation contained in publicly-available weekly COT 

reports of the WTI futures market. For the third week of July from 2000 to 2008, this table shows 

the open interest in WTI light sweet crude oil futures and futures-equivalent (delta-adjusted) options 

positions for Commercial (right panel) and Non-commercial (left panel) traders. For each category 

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

 Take, for example, the week in 2008 when crude oil prices reached their highest level ever. 

Panel A shows that, on July 18, 2008, on the short [long] side of the 1,344,411 futures open interest, 

51.1% [49.8%] of all positions were held by commercial traders and 14% [15.6%] 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 data. A second difference is COT frequency, 

which is less than weekly in studies using pre-2000 data. Starting in 1962, COT data were compiled on an end-ofmonth 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 positions of non-reporting traders, which include speculators, proprietary traders 

and smaller traders. This category accounts for the difference between total open interest in Table 4 and the aggregate 

open positions of all reporting (i.e., large) traders in Table 5. 
 15

reporting non-commercial traders, with the rest split between 27.5% in spread positions (i.e. 

calendar spread positions constructed with both long and short futures positions) by reporting noncommercial traders and 8.4% [7.1%] in short [long] positions held by non-reporting traders. 

2. Proprietary Data: The Large Trader Reporting System (LTRS) Data

 Whereas the public data only identify “Commercial” vs. “Non-commercial” categories of 

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

their respective components, and identify traders’ positions at all available contract maturities. That 

is, each reporting trader is classified into one of twenty-eight possible trader groups in the CFTC’s 

large-trader reporting system (LTRS). Appendix 1 provides lists of the “Commercial” and “Noncommercial” sub-categories, highlighting (in bold) the sub-categories that are the most active in the 

crude oil market. 

 The four main commercial sub-categories are (i) “Dealer and Merchant”, i.e., wholesalers, 

exporters and importers, marketers, etc.; (ii) “Manufacturers”, i.e., refiners, fabricators, etc; (iii) 

“Producers”, a self-explanatory grouping; (iv) “Commodity Swap Dealers”, gathering all reporting 

swap dealers and arbitrageurs/broker-dealers.17 These categories typically make up more than 95% 

of the commercial open interest in our 2000-2008 sample, and close to 99% in the last five years. 

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

to as “traditional” hedgers. By contrast, the swap dealer sub-category (whose activity we shall see 

has grown significantly since 2000) also includes the positions of non-traditional hedgers, including 

“entities whose trading predominantly reflects hedging of over-the-counter 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). 

 The three most active non-commercial sub-categories are (i) “Floor Brokers and Traders”; 

(ii) “Hedge Funds”, which comprise all reporting commodity pool operators, commodity trading 

advisors, “associated persons” controlling customer accounts as well as other “managed money” 

traders;18 (iii) “Non-registered participants” (NRP). The latter category, whose importance we shall 

see has increased substantially since 2000, mostly comprises financial traders whose positions are 

large enough to warrant reporting to the CFTC but who are not registered as managed money 

 

17 The CFTC merged the previously separate financial swap dealers and arbitrageurs/broker-dealer sub-categories with 

commodity swap dealers partway through our sample period. Haigh, Hranaiova and Overdahl (2007), who identify 27 

crude swap dealers from August 2003 – August 2004, find only 1 arbitrageur/broker-dealer and 1 financial swap dealer. 

18 See Appendix 2 for a more detailed discussion of the term “hedge funds” in the context of commodity futures markets. 
 16

traders or floor brokers and traders under the Commodity Exchange Act. NRPs also include some 

smaller non-commercial traders who do not have a reporting obligation but whose positions are 

nevertheless reported to the CFTC. During the sample period, these three categories made up about 

90% of the total non-commercial open interest (including non-reporting traders). 

B. Overall Market Growth 

 Table 4 and Figure 3 use the publicly available COTR information to illustrate the growth in 

crude-oil futures and option activity on the NYMEX. 

 Panel A in Table 4 shows that futures open interest more than tripled between July 2000 and 

July 2007, to more than 1.5 million contracts, but then fell somewhat (to 1.34 million contracts) in 

July 2008. Panel B shows that growth in option trading was even stronger. Compared to July 2000, 

the total number of open futures and futures-equivalent-options positions quintupled to almost 3 

million contracts in July 2008. The notional equivalent is more than $405 billion (each contract is 

for 1,000 barrels of oil, or approximately $140,000 at mid-July 2008 prices). 

 Table 4 highlights a period of moderate futures open-interest growth between July 2000 and 

July 2003, followed by a period of robust growth through 2007. The blue line in Figure 3 shows a 

similar pattern for the number of individual crude oil futures traders reporting their positions to the 

CFTC. This number grew considerably starting in mid-2003, to almost 350 in July 2007 (from 

about 150 in July 2000). It then leveled off, and fell to below 300 during the Summer of 2008.19

C. Trader Composition of Market Activity 

 Table 4 shows that the composition of the open interest also changed substantially during 

our sample period. Most notably, the third column in the table points out the increased importance 

of non-commercial calendar spread positions. The market share of spread traders more than tripled 

during the sample period, to almost 27% of the futures open interest and over 40% of all open 

futures and futures-equivalent option positions. 

 

19 Our method of computing open interest at the trader level presents the disadvantage of possibly overestimating the 

importance of spread traders or of trader categories whose “members” are heterogeneous in the direction (long or short) 

of their futures positions. In turn, this method may underestimate the role of more directional traders (such as crude oil 

swap dealers, who as a whole are massively long in near-term contracts). Still, this definition of open interest at the 

trader and sub-category level is the only one that yields the correct total open interest figures when summing up the 

positions of all traders across a given contract maturity. Furthermore, we ran some robustness checks using the absolute 

value of traders’ net positions (rather than their open interest) and obtained mostly similar regression results. 
 17

 While Table 4 shows that the overall WTI market growth was due in part to an increase in 

spread trading, the black line in Figure 3 shows in turn that the growth of spread activity partly 

reflects an increase in the number of large non-commercial spread traders. Notably, from 2001 

through 2003, the number of this kind of futures traders grew proportionally more than the total 

number of traders did. Precisely, the number of spread traders doubled to almost 50 between May 

2001 and January 2004, and then proceeded to triple again (to almost 150 by mid-2007). 

 The trader-level LTRS data allow us to further break down the market changes by specific 

trader type and by maturity. For every crude oil futures trader and each contract delivery month, we 

compute the trader’s Tuesday open interest in that contract as the average of that trader’s long and 

short futures positions. We then sum up the individual open interests of all reporting traders in a 

given category (e.g., all producers, all hedge funds, etc.) for all near-term (< 3 months), mediumterm (3 to 12 months), long-term (1 to 3 years), and very-long-term (3+ years) contracts. 

Table 5 provides annual averages and growth rates, by maturity, of these open interest 

figures for nine categories of large traders. To keep Table 5 tractable, we focus on three years: 

2000, 2004, and 2008. Figure 4 plots the weekly market shares of seven categories of traders from 

July 2000 through August 2008. Figure 5 provides further information for seven of the nine trader 

types. Figure 5a plots the open interest, by maturity, for each of the four most active commercial 

trader categories: producers, manufacturers, dealers/merchants, and commodity swap dealers. 

Figure 5b provides similar plots for the three most active non-commercial types: hedge funds, floor 

brokers/traders, and non-registered market participants. The scale is the same throughout Figure 5, 

to easily compare the relative magnitudes of the open positions held by different trader categories. 

Table 5, and Figures 4 and 5, allow us to make several observations. 

(i) Although “traditional commercial” traders now typically hold larger aggregate net

positions (whether long or short) than in 2000,20 their aggregate share of the futures open interest 

has halved since 2000 (from 42% to less than 20%). 

(ii) In the same period, the open futures positions held by financial traders (hedge funds and 

non-registered participants) grew by an order of magnitude – from about 45,000 contracts in the 

second half of 2000, to more than half a million futures in the first eight months of 2008. As a 

result, the market share of financial traders has more than doubled, from less than 20% of all open 

futures and futures-equivalent option positions in 2000 to more than 40% in 2008. 

 

20 For brevity, we include a single figure on net positions (Figure 8, for swap dealers, which is discussed in Section 

IV.D). Figures on the net positions of other trader types are available from the authors upon request. 
 18

(iii) Swap dealers’ market share grew markedly during the key years of 2002 and 2003. 

Overall, though, commodity swap dealers’ market share has been relatively stable, mostly 

fluctuating around 35% of the open interest. In a fast-growing market, of course, this fact implies 

that swap dealers’ activity increased substantially in absolute terms. Part of this considerable growth 

reflects the direct and indirect influx of commodity index money in futures markets (CFTC, 2008). 

D. Market Growth at Different Contract Maturities 

 Figure 6, which uses the LTRS data, complements Table 5 and Figure 5 by providing annual 

open interest averages for all reporting (i.e., large) traders by contract maturity. Table 5 and Figures 

5 and 6 reveal a sea change in the maturity structure of the crude oil futures market. 

(iv) While the market as a whole has grown, growth has been stronger at the back end of the 

maturity curve. For futures only as well as for futures and options combined, Table 5 shows that 

the open interest in longer-dated contracts (one year or more) was greater, in the first eight months 

of 2008, than the open interest in contracts with less than a year to maturity used to be in 2000. 

(v) Table 5 links this transformation to the fact that several categories of traders now carry 

larger (commercial dealers) or much larger (commodity swap dealers, hedge funds, non-registered 

participants) open positions in long-dated contracts (one year or more) than they used to hold in 

near-term contracts (three months or less) in 2000. 

(vi) Among traditional commercial traders categories, crude oil producers have been almost 

absent from the long-term market. Manufacturers, in contrast, had a strong presence until 2005, and 

crude oil dealers and merchants retain a strong presence to this day.21 

(vii) Much, but not all, of the growth of net positions in long-dated contracts took place after 

2003. Figure 5a shows that swap dealers’ open positions in such contracts started to grow in mid2003. In contrast, Figure 5b shows that the open positions of other major players in these contracts 

did not start to rise substantially until 2004 (hedge funds) or 2005 (non-registered participants). 

(viii) The evidence that swap dealers were first to move into longer-dated contracts is in line 

with our interviews of some of the most senior participants in the U.S. energy futures markets. As 

indicated also by these interviews, the growth of swap dealers’ backdated positions accelerated in 

 

21 Small traders (those with individual positions small enough that they do not report their positions to the CFTC largetrader database) account for approximately 12% of net positions in near-term contracts, but make up only 3-4% of net 

positions in long-term contracts. 
 19

the second half of 2004 – at the time when the WTI futures market contangoed, after a long period 

of backwardation (see Figure 7).22 

(ix) Among all trader categories, commodity swap dealers’ positions in short- vs. long-dated 

contracts exhibit the most striking differences. Of particular interest is Figure 8 which shows that 

swap dealers’ positions in near-dated contracts (< 3 months) are net long (likely reflecting a greater 

propensity of commodity investment flows to be directed to short-term contracts), whereas their 

further-out positions are mostly spread positions and are often net short. Put differently, Figure 8 

suggests that commodity swap dealers’ short-term and long-term positions reflect different trading 

motives. Figure 5a provides further evidence in support of this conjecture. In April 2007, swap 

dealers’ open positions in shorter-term futures dropped by about 40%; they have remained relatively 

level ever since. Since mid-2007, in contrast, swap dealers have taken large and growing positions 

in long-term contracts (1 to 3 years). 

In the next two Sections, we investigate whether this considerable market growth – or the 

fundamentals behind this growth – helped strengthen linkages between different-maturity futures. 

V. Explaining the Emergence of Price Cointegration: Variables and Testable Hypotheses. 

Using the trace statistic from a recursive cointegration analysis to measure changes over 

time in the extent to which the nearby, one-year and two-year futures prices are cointegrated, 

Section III identifies the emergence of a stable long-term relation between these three price series. 

In this Section, we develop testable hypotheses regarding possible explanations for this change in 

pricing structure. We consider real-sector variables (V.A) and metrics of market activity (V.B). 

A. Real Variables 

 In theory, the prices of crude oil for spot vs. future delivery should be related by a cost-ofcarry factor accounting for the time value of money, storage costs and possibly a convenience yield. 

Prices further along the futures maturity curve should be related to one another through similar costof-carry equations. One possible explanation for the pricing changes we document in Section III, is 

that the crude oil fundamentals evolved – affecting the time-series properties of the spot price for 

crude oil (Bessembinder et al., 1995) or of the net cost-of-carry for long-dated oil futures contracts 

 

22 When the term structure of futures prices is in contango (upward sloping), contracts expiring further out in time cost 

more. Hence, rolling a nearby position into the next-nearest-dated contracts is costly (see, e.g., Erb and Harvey, 2006). 
 20

(Brenner and Kroner, 1995). To assess whether changes in fundamentals led to price cointegration, 

we consider two real-sector variables relevant to the pricing of crude oil: (1) the strength of world 

demand for industrial commodities; (2) capacity constraints affecting crude oil production. 

1. Commodity-Demand Shock 

 In a recent article, Kilian (2008b) proposes a novel measure of global real economic activity 

predicated on the intuition that “increases in freight rates may be used as indicators of (...) demand 

shifts in global industrial commodity markets.” Specifically, Kilian constructs a global index of 

single-voyage freight rates for bulk dry cargoes including grain, oilseeds, coal, iron ore, fertilizer 

and scrap metal. This index, which accounts for the existence of “different fixed effects for different 

routes, commodities and ship sizes,” can be computed as far back as January 1968. It is deflated 

with the U.S. consumer price index (CPI), and linearly detrended to remove the impact of the 

“secular decrease in the cost of shipping dry cargo over the last forty years.” A change in the time 

series properties of freight-rates is apparent starting in the spring of 2003, after which shipping costs 

rise substantially (Kilian, 2008b: Figure 1). The 2003-2008 period stands in sharp contrast to the 

previous two decades (which did not witness periods of sustained freight rate increase). 

 Similarly, Korniotis (2008) finds that exchange-traded as well as non-traded commodities 

experienced statistically significantly higher quarterly growth rates from 2004 to 2008 than from 

1998 to 2003. He shows that “this structural change in the growth rate levels is both economically 

and statistically significant.” The structural break finding is robust, in that it extends to agricultural 

products and in that there is no statistically significant price-appreciation gap between exchangetraded and non-traded commodities. 

 For our empirical analysis, we build on these observations and construct a weekly inflationadjusted spot price series for an equally weighted basket of non-exchange-traded commodities.23 

By focusing on non-exchange-traded commodities, we ensure that the fluctuations of the basket’s 

price do not stem from changes in the activities of financial institutions in commodity futures 

markets. Given a desire for an index that is diversified across commodity types, but also facing 

limitations in the availability of spot price data, our basket comprises seven commodities: rice, coal, 

 

23 The Kilian index exists at a monthly frequency. This is one reason why we focus on the inflation-adjusted price of a 

basket of commodities to identify demand shock common to all commodities, as that price series can be constructed at 

the same weekly frequency as the trace statistic. We obtain qualitatively similar results with the nominal price series, as 

well as with weekly cubic interpolations of the Kilian shipping cost index series. 
 21

manganese, rhodium, cadmium, cobalt and tungsten.24 

 Figure 9 plots our index from January 1997 to August 2008. In line with the monthly Kilian 

(2008b) freight-rate index and both Korniotis (2008) quarterly commodity price indices (traded and 

non-traded), this graph supports the notion that, starting in 2003 and more strongly after 2004, a 

demand shock pushed upward the prices of most commodities. A large change in the growth rate of 

the index is visible, with sustained growth and few price decreases from 2003 to August 2008. 

 In order to link these structural breaks to cointegration, recall that Bessembinder et al.

(1995) document how distant oil prices used to be not very responsive to changes in near prices. 

Assuming efficiency, they attributed this finding to mean-reversion in spot prices. In this vein, one 

possible reason why distant-delivery prices became cointegrated with near-delivery prices is that, 

amid a structural break in commodity prices, spot crude oil prices no longer are mean-reverting but 

now are closer to following a random walk. These observations lead to our first testable 

hypothesis:25 

Hypothesis 1: Higher commodity prices are associated with greater price cointegration across the 

maturity structure. 

2. Crude Oil Fundamentals 

In addition to a demand shock common to all commodities, we explore whether a change in 

the stochastic processes driving crude oil’s price or its cost-of-carry could have originated from a 

shock specific to the crude oil market. Indeed, recent research indicates that, between 2003 and 

2008, the confluence of robust economic growth worldwide and of capacity constraints in crude oil 

extraction was responsible for a considerable increase in oil prices (Hamilton, 2008).26 

Using data from the Energy Information Administration (US Department of Energy), we 

plot in Figure 10 the spot price of WTI crude oil vs. the total spare crude oil production capacity 

outside of Saudi Arabia. We focus on the non-Saudi spare production capacity, because the clearest 

 

24 Although futures exist on rough rice and Appalachian coal, the CFTC’s Commitments of Traders Reports show that 

the open interest and number of traders in these contracts are small (both in absolute terms and compared to other major 

commodities). Our basket adds coal to the basket of non-exchange-traded agricultural and industrial commodities 

constructed by Korniotis (2008). To abstract from absolute price-level differences across commodities, we construct the 

index by compounding an equally-weighted average of individual commodity returns, and deflating the resulting series.

Spot price data are from Bloomberg; deflators, from the US Bureau of Labor Statistics. 

25 Testing this hypothesis directly would require a very long time series of quality spot price data. Routledge, Spatt and 

Seppi (2000), however, note that spot data in 1992-1996 are not available. 

26 See also U.S. Interagency Task Force on Commodity Markets, 2008. Interim Report on Crude Oil. Washington D.C. 
 22

evidence of a major change in oil market fundamentals is evident in this variable (as opposed to 

world oil consumption, Saudi surplus oil production capacity, OECD stocks of crude oil, etc.). 

Figure 10 highlights a major change in the physical crude oil market. From January 1995 to 

February 2004, when spare capacity was relatively plentiful, prices fluctuated around $29. From 

March 2004 to August 2008, in contrast, non-Saudi spare capacity was close to zero and spot oil 

prices ranged between $27 and $142. Our second testable hypothesis is thus: 

Hypothesis 2: Lower non-Saudi oil spare production capacity is associated with increased price 

cointegration across crude oil futures maturities. 

B. Trader Composition of the Crude oil Futures and Option Market 

 Price discovery and risk transfer are critical functions of 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 derivatives. These authors show 

that trading provides the linkage between cash and futures prices and leads to a closer correlation 

between prices, a more efficient transmission of information, and improved hedging opportunities. 

 Our second set of hypotheses recognizes the fact that market linkages are unlikely to take 

hold without participants keen to exploit apparent pricing aberrations. That is, price discovery 

should improve in a market as it becomes less thinly traded. Between near- and long-dated futures 

prices, some of the necessary trading activity could consist of cross-maturity arbitrage (or spread) 

trades or, alternatively, of speculative directional trades (i.e., trades that involve either long or short 

positions but not both). In either case, it should matter what kinds of traders try to take advantage 

of perceived price discrepancies across the maturity structures. If too many market participants are 

perceived as possibly acting on the basis on superior information, then would-be arbitrageurs may 

elect not to trade against them. If so, then a change in market conditions that begets an 

“exogenous” increase in non-discretionary liquidity trading (or the arrival of a new kind of liquidity 

trader) could provide the “thick market” needed for arbitraging and other discretionary trading 

(Admati and Pfleiderer, 1988) and act as a catalyst for changes in pricing relations. 

 Commodity index investors exhibit the very profile of uninformed traders. To the extent 

that a large fraction of the commodity index money is reflected in the nearby crude oil futures 

positions of commodity swap dealers – see Sections IV.C (iii) and IV.D (ix) – we surmise that: 
 23

Hypothesis 3: The cointegration between short-term and long-term futures crude oil futures prices 

is positively related to the magnitude of commodity swap dealer positions in nearby 

futures (but not necessarily in longer-term contracts). 

 Once liquidity trading has grown sufficiently, of course, it becomes easier for speculators 

and financial arbitrageurs to enter the market, as they face less risk that the counterparties to their 

trades would be better informed. Hence, one would expect that an increase in the market share of 

traders who are eager to exploit apparent pricing discrepancies, yet may not be trading on the basis 

of private information, should enhance price cointegration across contract maturities. The kinds of 

financially-motivated traders least likely to have private information in commodity markets are 

hedge funds and other financial traders. Further, to the extent that these two types of traders face 

fewer financial constraints than more traditional trader categories, their arrival should help alleviate 

price discrepancies and improve the transfer of risk between market participants (as in Başak and 

Croitoru, 2006). These observations yield our fourth testable hypothesis: 

Hypothesis 4: Greater market participation by hedge funds and non-registered participants 

(especially in long-dated contracts) leads to increased cointegration between shortterm and long-term futures crude oil futures prices. 

 Lastly, our interviews of market participants suggest that, whereas traders wishing to gain 

exposure to crude oil initially took long positions in nearby futures contracts, changes in the term 

structure of futures prices later made this strategy less attractive. Precisely, it became costly to roll 

forward long positions in the nearby futures after the crude oil market became “contangoed,” i.e., 

after the term structure of crude oil futures prices became upward-sloping in mid-2004 (see Figure 

7). Intuitively, traders who previously had been rolling nearby positions started to have incentives 

to move further along the futures maturity curve. These observations beget our final hypothesis: 

Hypothesis 5: Greater price cointegration between near- and long-dated futures is positively 

associated with the slope of the near term structure of crude oil futures prices. 

 
 24

VI. Explaining the Pricing Changes: Empirical Analysis. 

In this Section, we test our five hypotheses by looking for statistical evidence of a long-term 

relation between the strength of cointegration across contract maturities, and crude oil fundamentals 

as well as the futures positions of different traders at some or all maturities. Section VI.A describes 

our methodology. Section VI.B describes our explanatory variables. Sections VI.C and VI.D, 

respectively, summarize and discuss our findings. 

A. Methodology 

 Before estimating the effect of trader participation on the strength of cointegration between 

nearby and further out contract prices, we first check the order of integration of each variable using 

Augmented Dickey Fuller Tests. Table 6 presents the unit root tests for the variables entering in our 

estimation equation. Most of the position variables are I(0), but the trace statistics and the real 

variables are I(1). Since some of the variables are nonstationary, we use a cointegration analysis to 

test whether there is a long term relation between the position data and trace statistics. 

The Engle and Granger (1987) and Johansen (1988, 1991) cointegration techniques require 

that all the variables in the estimation equation be integrated of the same order. Here, some of the 

variables are I(0) while others are I(1). Pesaran and Shin (1999) show that the autoregressive 

distributed lag (ARDL) model can be used to test the existence of a long-run relationship between 

underlying variables and to provide consistent, unbiased estimator of long-run parameters in the 

presence of I(0) and I(1) variables in the estimation. The ARDL estimation procedure reduces the 

bias in the long run parameter “ β ” in finite samples, and ensures that it has a normal distribution 

irrespective of whether the underlying regressors are I(0) or I(1). 

 We start with the problem of estimation and hypothesis testing in the context of the 

following ARDL(p,q) model: 

 (6) 

where y is a t ×1vector of dependent variable, x is a t × k vector of regressors, and stands for a 

t × s vector of deterministic variables such as an intercept, seasonal dummies, time trends, or 
 25

exogenous variables with fixed lags.27 In vector notation, Equation (6) is: 

where is the polynomial lag operator ; is the polynomial lag 

operator , and L represents the usual lag operator ( ). 

The estimate of the long run parameters can then be obtained by first estimating the parameters of 

the ARDL model by OLS and then solving the estimated version of (6) for the cointegrating 

relationship by 

where gives us the long-run response of to a unit change in and, similarly, represents the 

long run response of to a unit change in the deterministic exogenous variable . 

 In practice, we obtain the standard errors of the long run coefficients using “Bewley 

regressions.” Bewley's (1979) approach involves the estimation of the following regression 

by the instrumental variable method, using as 

instruments. Pesaran and Shin (1999) show that the instrumental variable estimators of and 

obtained using the Bewley (1979) method are numerically identical to the OLS estimators of and 

 based on the ARDL model (the latter alone, of course, provides an ECM representation when the 

variables under study are cointegrated). 

 When estimating the long-run relationship, one of the most important issues is the choice of 

the order of the distributed lag function on and the explanatory variables . We carry out the 

two-step ARDL estimation approach proposed by Pesaran and Shin (1999). First, the lag orders of 

p and q must be selected using some information criterion. Based on Monte Carlo experiments, 

Pesaran and Shin (1999) argue that the Schwarz criterion performs better than other criteria. This 

 

27 The error term is assumed to be serially uncorrelated.
 26

criterion suggests optimal lag lengths p=1 and q=1 in our case. Second, we estimate the long run 

coefficients and their standard errors using the ARDL(1,1) specification. 

B. Variables 

We use the trace statistic for the nearby, 1-year and 2-year prices (see Figure 2) as the 

dependent variable. As explanatory variables, we use proxies of the crude oil market fundamentals, 

data on the open positions of specific kinds of trader, and controls for overall market activity and 

exogenous liquidity shocks. 

In line with Hypotheses 1 and 2, we use two variables for market fundamentals: a weekly 

index of non-exchange-traded commodity prices (LPI), and non-Saudi spare crude oil production 

capacity. Because the spare capacity data are available only monthly, we use cubic interpolations to 

generate a weekly time series (SPARE) from the end-of-month EIA estimates. 

To match the way we compute the trace statistic, we focus on the open interest of each 

trader type (all manufacturers, all producers, etc.) in three contracts: nearby, one-year-out, and twoyear-out futures. In this manner, our regressions capture changes over time in specific levels of 

participation that could be tied to the concurrent increase in the trace statistic.28

We use two specifications. In one set of regressions (denoted “Model 1” in Tables 7-9), we 

use market shares for each trader type – that is, we scale the open positions of each trader category 

in the relevant contract(s) by the open interest of all traders in all three contracts (nearby, one- and 

two-years). Once we control for the overall open interest (as discussed in the next sub-section), this 

first specification allows us to assess whether the composition of that open interest matters. In the 

second set of regressions (denoted “Model 2” in Tables 7-9), our trader participation variables 

measure the absolute figure of the relevant open interest. 

We include the slope of the term structure of near-term crude oil prices (SLOPE), which 

measures the cost of maintaining an exposure to crude oil prices by means of a long position in the 

nearby WTI futures. As discussed in Hypothesis 5, this variable captures incentives of long-only 

traders (e.g., commodity index traders) to move positions further along the maturity structure. 

We use either of two explanatory variables to capture the overall trading activity: the total 

open interest across all traders and all contract maturities (TOI), and the Tuesday trading volume in 

 

28 Several studies employing recursive cointegration techniques (e.g. Haigh, 2000) attempt to explain changes in the 

level of cointegration over time, by informally relating (often abrupt) changes in trace values using information to actual 

(usually one-time) events. 
 27

the nearby, one-year and two-year-out futures (FVOL). The first variable captures the overall 

market growth. The second variable captures the reality that trading volume in the relevant 

contracts may not be a constant proportion of the open interest in these contracts. In practice, we 

find that TOI and FVOL are cointegrated, so we use only one of these two variables at a time. We 

obtain qualitatively similar results with TOI or FVOL; hence, we report results for TOI only. 

NYMEX crude oil trading volume grew massively (relative to open interest) after the start 

of electronic trading in September 2006. We use a dummy variable (ETRADE) to capture this fact: 

we set it equal to 1 starting with the first Tuesday of September 2006, and 0 prior to that date. 

Lastly, a substantial portion of the long-dated crude oil market activity involves contracts 

maturing in June or December. We use two liquidity dummies to capture this fact, which we set 

equal to 1 when the nearby, one-year-out and two-year-out contracts mature in June (JUN) or 

December (DEC) and to 0 otherwise. 

C. Results 

 Tables 7, 8 and 9 present our ARDL regression results. We use positions for each category 

of trader independently in Table 7, and jointly in Tables 8 and 9. In Tables 7 and 8, the real-sector 

variable is the non-traded industrial commodity price index (LPI); in Table 9, the proxy for crude 

oil fundamentals is the non-Saudi spare capacity (SPARE). In all three tables, we run two models: 

in the first model, the explanatory variable is the market share of a trader category; in the second 

model, we use instead the number of open contracts itself. 

 In Tables 8 and 9, we report the results for one- and two-year open interests combined, with 

nearby open interest as a separate variable. We report three specifications in Table 7: 

Panel A: nearby, one- and two- year open interests combined; 

Panel B: one- and two-year open interests combined, nearby open interest separate; 

Panel C: three separate variables for the nearby, one- and two-year open interests. 

The industrial-commodity price index variable (LPI) is often significant (with the expected 

positive sign) in Table 8, which supports the first testable hypothesis delineated in Section V.A. 

Together with the slope of the term structure of crude oil prices, the spare crude oil production 

capacity variable (SPARE) is also highly statistically significant (with the expected negative sign) 

in all the regressions of Table 9, which supports our second hypothesis. In short, changes in market 

fundamentals help explain the greater co-integration. 
 28

In both Tables 8 and 9, the coefficient on SLOPE (the slope of the nearby term structure of 

crude oil prices) is positive and almost always statistically significant. Notably, SLOPE is highly 

significant in all the regressions that use SPARE as the proxy for fundamentals. If we interpret an 

upward-sloping term structure of crude oil futures prices as an indication of a forthcoming supply 

tightening, then this finding suggests that, in a crude oil market where it takes years to develop new 

production capacity, a dearth of spare capacity brings prices in line at near and long horizons. 

The coefficient on SLOPE is positive, i.e., contango in the near-term is associated with 

higher cointegration across short- and long-dated contracts. This fact suggests, as Hypothesis 5

conjectures, that the transmission mechanism from changes in fundamentals to price cointegration 

across the term structure involves market activity by traders rolling short-term futures positions. 

Consistent with the interpretation that trader activity matters, we find that overall market 

activity (TOI) help explain the trace statistic – and, critically, that, after controlling for changes in 

the overall open interest, the composition of that open interest matters in the manners outlined in 

Hypotheses 3 and 4. 

Specifically, our regressions show that the strength of the cointegration between the nearby, 

one-year and two-year crude oil futures price series is linked to the market activities of financial 

traders, especially in one- and two-year contracts (Tables 7.1 and 7.2, Panels B and C; Table 8, 

Panel A.2), as well as to the activity of commodity swap dealers in nearby contracts – activity that 

reflects in large part their commodity index swap business (Table 7.1, Panel A; Table 8, Panel B.2; 

Table 9, Panel B.2). By contrast, we find little evidence of a positive connection between the 

strengthening of cointegration and the activities of traditional commercial traders or the activity of 

swap dealers in one-year and two-year contracts (which is, arguably, less likely to result from swap 

dealers’ commodity-index business). 

D. Discussion and Possible Caveats 

 Our analysis indicates that changes in fundamentals, coupled with changes in the market 

activities of three specific categories of traders, help explain the strengthening of linkages between 

nearby and far-out crude oil futures prices. In this regard, our results illustrate how increased 

participation by some types of traders can enhance price linkages in commodity markets. 

 One might worry that relation need not imply causation. A battery of univariate Granger 

causality tests, however, confirm that the direction of causality runs from fundamentals and market 

activity to cointegration, and not the reverse. With this potential concern laid to rest, we now round 
 29

out this Section by discussing our proxies for fundamentals, and the relation between traders’ endof-day positions and their futures market activity. 

To test Hypothesis 1, we use a weekly index of non-exchange-traded commodity prices (the 

LPI variable). Our results are robust to using another proxy of the world demand for commodities. 

Specifically, when replacing LPI by SHIP (i.e., by weekly interpolations of the monthly Kilian 

(2008b) shipping cost index), we find that the SHIP variable is usually statistically significant in the 

same regressions as LPI, and that the same combinations of other explanatory variables typically 

remain significant (with the same signs). 

One possible worry stems from the observation that both the LPI and SHIP variables partly 

reflect the price of crude oil, since the latter is a key component of the production and transportation 

costs of commodities. To wit, at typical 2005 (mid-2008) charter rates, the bunker fuel used to 

propel ships accounted for approximately one third (one half) of dry-cargo shipping costs. More 

formally, a co-integration analysis of SHIP and LPI shows that these two variables are cointegrated 

with the inflation-adjusted price of crude oil in the last ten years. 

Visual inspections of the SHIP and crude oil price series, however, suggest that the shipping 

cost increases predated, by more than six months, the start of a sustained increase in crude oil 

prices. Furthermore, the prices of oil and the non-oil components of SHIP and LPI should move 

broadly in sync, if all three are driven by a demand shock common to all commodity markets. 

Lastly, to alleviate any residual concern about regressing the trace statistic (which measures 

linkages between various crude oil prices) on two non-oil price measures that are cointegrated with 

the price of crude oil, we note two things. First, the third variable we use to capture fundamentals, 

SPARE, is a quantity measure – yet it is also cointegrated with the crude oil price series. Second, 

the statistical significance of fundamentals is even stronger when we replace the combination of LPI 

and SLOPE or of SHIP and SLOPE in our regressions with SLOPE and SPARE. Put differently, 

our analysis of the link between fundamentals and cointegration is not subject to the concern of 

regressing “prices on prices.” 

 Our regressions show that the “market activities” of specific groups of traders contributed to 

the 2003-2004 emergence of price cointegration across the maturity curve in the crude oil futures 

market. This result entails several possible caveats. 

First, all traders grouped in a given LTR category may not share similar motives for trading. 

In the “commodity swap dealers” category, for example, not all traders partake in the commodity 

index (CIT) business – and far from all of the activity of those dealers that are involved in the CIT 
 30

business is related to index trading (CFTC, 2008). The “non-registered participants” and “hedge 

funds” categories likely are also heterogeneous. The CFTC data made available for this study do 

not allow us to improve much on the LTR classification. If anything, however, within-category 

heterogeneity should bias against finding a statistically significant relations between the activities of 

specific trader categories and the strength of cointegration. It is thus all the more relevant that we 

do find such relations; that they are statistically significant for the predicted categories; and, that the 

regression coefficients have the expected sign. 

A second possible caveat to our results is that we measure traders’ activities by their end-ofday open positions – as opposed to volume of trading. We use position data not because the CFTC 

databases links a trader’s main-line-of-business information to his end-of-day position data but not 

to his within-day trade and quote activity. Rather, we do so because the cointegration techniques 

used in Section III assess co-movements between prices over long periods of time – and, similarly, 

a trader’s end-of-day position in a given contract summarizes the extent of his trading in that 

contract over a long period (namely, the entire life of the contract). Still, to the extent that a trader’s 

futures positions on the NYMEX may reflect over-the-counter (OTC) transactions cleared on 

NYMEX’s ClearPort facility, it is nevertheless important to confirm that our measure of market 

activity moves in long-term sync with the related trading activity on the Nymex. Consistent the 

intuition that the same underlying factor drives futures open interest and trading volume in the 

sample period, Johansen cointegration analyses show that the volume and open interest series are 

indeed cointegrated overall, as well as for the nearby, one-year and two-year series. 

VII. Conclusions. 

 We document that, in the wake of strong growth in the market for exchange-traded crude oil 

derivatives, the prices of one-year and two-year futures became cointegrated with the price of the 

near-month futures in 2004. Using data on crude oil fundamentals and a unique dataset of traderlevel futures-market position data, we investigate two questions. One, can changes in fundamentals 

help explain the strengthening cointegration among nearby and distant futures contracts in crude 

oil? Two, which categories of traders (if any) are associated with the emergence of cointegration? 

 We find that fundamentals contributed to this pricing development. We also find that an 

increase in commodity swap dealer positions in the nearby contract, as well as increases in the 
 31

market activity of other traders (in particular, hedge funds’ and other financial traders’ activities in 

long-dated contracts) helps further explain the cointegration of prices in this markets. 

 The 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. 

 As the market for crude oil futures further develops, with greater participation at horizons 

beyond two years, one might anticipate similar linkages emerging at the longer end of the futures 

spectrum. At the same time, amid a recent fall in demand for crude oil (and a concomitant increase 

in spare production capacity) and an unprecedented credit crisis (which might affect the ease with 

which financial traders can carry out commodity arbitrage strategies), a natural question is whether 

the links between short, medium and long-term prices could become weaker. Time will tell. This 

question, therefore, we leave for further research. 
 32

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 35

Appendix 1: Large trader categories

Panel A: 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 

Panel B: Non-commercial Traders 

CFTC Code CFTC Name Present in WTI futures & options markets 

HF Hedge Fund Y 

FBT Floor Broker /Trader Y 

FCM Futures Commission Merchant Y (very small)

IB Introducing Broker Y (very small)

NRP Non-Registered Participant Y 

Notes: Appendix 1 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” (Panel A), who have declared an 

underlying hedging purpose, and “Non-commercials” (Panel B), who have not. In Panel A, “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 Dealer” (AS) aggregates all reporting “Swaps/Derivatives Dealers” (FS) and “Arbitrageurs or 

Broker Dealers” (FA), two categories that were merged in the CFTC’s internal reporting system part-way through our 

2000-2008 sample period. In Panel B, “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. Note that hedge funds involved in financial contracts that are shown to be hedging 

would be included in the Commercial category FH. “Floor Broker / Trader” (FBT) aggregates all reporting floor 

brokers and floor traders. “Non-registered participants” (NRP) are non-commercial traders not registered under the 

Commodity Exchange Act (CEA). This category, which has grown significantly since 2000, mostly comprises financial 

traders with positions large enough to warrant reporting to the CFTC, it also includes smaller traders who do not have 

such a reporting obligation but whose positions are nevertheless reported. 
 36

Appendix 2: Defining Hedge Funds.

“Hedge fund” activity in energy derivatives markets has been the subject of intense scrutiny 

in recent years by academic researchers, market participants, policy makers, and the media. Yet, 

there is no accepted definition of a “hedge fund” in futures markets, and there is nothing in the 

statutes governing futures trading that defines a hedge fund. Furthermore, there is nothing that 

requires hedge funds to be categorized in the CFTC’s Large Traders Reporting System (LTRS). 

Still, many hedge fund complexes are either advised or operated by CFTC-registered 

commodity pool operators (CPOs) or Commodity Trading Advisors (CTAs) and associated persons 

(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) that they are 

representing.29 

It is clear that many of the large CTAs, CPOs, and APs are considered to be hedge funds and 

hedge fund operators. Consequently, we conform to the academic literature and 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 identified other participants 

who were not registered in any of these three categories but were known to be managing money –

these are also included in the hedge fund category (see bottom of Appendix 1). 

 

29 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, 

except that it invests pooled money in the futures and options markets. Like its securities counterparts, a commodity 

pool operator (CPO) might invest in financial markets or commodity markets. Unlike mutual funds, however, 

commodity pools may be either long or short derivative contracts. A CPO’s principal objective is to provide smaller 

investors the opportunity to invest in futures and options markets with greater diversification with professional trade 

management. The CPO solicits funds from others for investing in futures and options on futures. The commoditytrading advisor (CTA) manages the accounts and is the equivalent of an advisor in the securities world. 
Table 1: Descriptive Statistics and Correlation Analysis on Weekly Crude Oil (WTI) Futures Pric

Panel A: Tuesday settlement prices

July 2000 to August 2008 July 2000 to June 2004 July 200

Nearby 1-year 2-year Nearby 1-year 2-year Nearby 

Maximum 140.97 141.74 139.47 42.33 36.91 34.17 140.97 Minimum 18.46 19.98 20.33 18.46 19.98 20.33 39.54 

Panel B: Sample moments, Tuesday to Tuesday returns

July 2000 to August 2008 July 2000 to June 2004 July 200

Nearby 1-year 2-year Nearby 1-year 2-year Nearby 

Mean 0.30% 0.36% 0.39% 0.05% 0.13% 0.17% 0.54% Median 0.85% 0.63% 0.43% 0.87% 0.40% 0.28% 0.83% Maximum 10.61% 8.34% 9.01% 9.48% 6.51% 5.78% 10.61% Minimum -25.30% -13.16% -10.29% -25.30% -13.16% -10.29% -16.97% Std deviation 4.68% 3.09% 2.76% 5.13% 2.69% 2.16% 4.22% 

Panel C: Simple Correlations, Tuesday to Tuesday Returns

July 2000 to August 2008 July 2000 to June 2004 July 200

Nearby 1-year 2-year Nearby 1-year 2-year Nearby 

Nearby 1 1 1

1-year 0.85 1 0.84 1 0.89 2-year 0.70 0.96 1 0.65 0.92 1 0.80 

Notes: Table 1 provides summary information computed from the Tuesday settlement price series for the near-month, 1-year out (neayear out (nearby + 24 months) WTI sweet crude oil futures contracts between July 3, 2000 and August 28, 2008. We use Tuesday figures. If Tuesday prices are not available (i.e., if markets are closed on a given Tuesday), then we use the next Wednesday price ininstances when the market is also closed that Wednesday, the previous Monday settlement price. 
Table 2: Augmented Dickey-Fuller (ADF) tests for Futures Prices

Price Series K HO: I(1) vs. H1: I(0) HO: I(2) vs. H1: I(1)

Nearby 3 -1.7317 -17.3825 

1-yr contract 3 -0.2294 -9.5076 

2-yr contract 1 -1.0262 -28.5536 

Notes: The critical values are from Fuller (1976): –3.12 (10%), –3.41* (5%) and –3.96 (1%). Hence, based on these 

results, all three price series are I(1). The optimal lag length K is based on the Akaike Information Criterion (AIC). 

Table 3. Cointegration Analysis of Prices

Panel A: Trace tests on order of cointegrationa

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

50.93 r = 0 34.10 (0.000) 

22.91 r ≤ 1 19.87(0.020) 

4.72 r ≤ 2 8.47 (0.323) 

 

Panel B: Tests for exclusion from the cointegrating vectorb

HO: 2

χ (1) value(p value) 

Nearby β N = 0 3.115 (0.078) 

1 yr contract β1 = 0 0.969 (0.325) 

2 yr contract β 2 = 0 0.468 (0.494) 

Panel C: Tests for weak exogeneityc

HO: 2

χ (1) value (p value) 

Nearby αN =0 0.198 (0.656) 

1 yr contract α1 = 0 6.63 (0.01) 

2 yr contract α2 = 0 6.08 (0.01) 

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, the exclusions of 1 year out and 2 year out contracts are accepted at 10 

percent level of significance. However, given the economic significance of these contracts, we 

keep them in our cointegration relation. 

c

Weak exogeneity tests suggest that the nearby contract price is a weakly exogenous variable that 

influences the long-run path of other variables in the system – in this case, the other two prices – 

but is not influenced by these other variables. 
Table 4: Crude Oil Futures and Options: Open Interest Snapshots, 2000 –2008

Panel A: Crude Oil Futures, Third Week of July

 Non-Commercial (%) Commercial (%) Open 

 long short spread long short Interest 

2000 10.2 6.0 6.0 74.4 76.9 456,717

2001 3.6 13.7 9.1 80.5 65.8 453,943

2002 13.1 5.9 18.1 56.4 66.7 486,180

2003 19.8 12.7 8.4 62.7 67.5 536,017

2004 16.1 10.9 8.4 67.3 71.5 696,850

2005 13.2 10.9 16.7 62.1 63.6 822,636

2006 17.7 12.4 21.1 54.9 60.1 1,086,762

2007 16.1 8.9 20.3 58.5 65.3 1,549,425

2008 15.6 14.0 27.5 49.8 51.1 1,344,411

Panel B: Crude Oil Futures and Futures-equivalent Options, Third Week of July 

 Non -Commercial (%) Commercial (%) Open 

 long short spread long short Interest 

2000 7.8 2.9 11.0 73.4 75.9 607,156 

2001 3.1 8.0 13.7 77.3 69.0 608,391 

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 

2007 10.9 4.5 26.7 58.8 64.9 2,453,667 

2008 8.0 5.1 42.7 45.6 48.4 2,974,130 

Notes: Table 4 provides a snapshot of open interest changes since 2000. Open interest data for futures and futuresequivalent (delta-adjusted) option positions are from the weekly Commitment of Traders Reports from July 2000 

through July 2008. For each year, the snapshot is for the third week of July (e.g. July 17, 2007). We report open 

interest figures for futures only and for total positions (i.e., the sum of futures and futures-equivalent options 

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” (right panel), who have 

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

long and short positions are reported as fractions of the overall open interest. In Table 4B, 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) held by 

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

 
Table 5: Open Interest by Maturity and Trader Category – Annual Averages

Futures Only Futures & Futures-equivalent Options

Panel A: 2000

0-3 months 3-12 months 1-3 years 3+ years Total 0-3 months 3-12 months 1-3 years 3+ years 

Manufacturers 30,361 13,185 7,858 2,479 53,883 37,601 16,720 7,985 2,47

 

Other Commercials 3,292 1,664 120 - 5,076 3,540 1,810 120 - 

Producers 11,131 6,733 2,413 899 21,176 15,575 9,947 2,494 89

 

Dealers/Merchants 49,401 37,665 20,068 5,173 112,307 58,100 42,351 20,691 5,17

 

Commodity Swap Dealers 41,155 42,206 40,682 9,090 133,132 68,684 77,563 53,300 9,09

 

Unclassified Commercials 11,715 4,642 1,688 72 18,117 16,267 6,018 1,997 7

 

Non-Registered 9,590 3,602 654 736 14,581 21,891 10,418 728 73

 

Floor Brokers/Traders 6,320 12,250 6,432 443 25,445 22,695 23,083 7,819 44

 

Hedge Funds 16,542 7,228 6,340 595 30,705 18,072 8,244 7,077 59

 

Total 179,505 129,175 86,256 19,487 414,423 196,155 262,425 102,210 19,48Commercial 147,053 106,096 72,830 17,713 343,692 199,767 154,409 86,588 17,7

 

Non-Commercial 32,452 23,080 13,426 1,774 70,731 62,658 41,745 15,623 1,77

 

NonComm + Swap Dealers 73,606 65,286 54,107 10,864 203,863 131,342 119,308 68,923 10,86

 

Panel B: 2004

0-3 months 3-12 months 1-3 years 3+ years Total Diff vs.2000 0-3 months 3-12 months 1-3 years 3+ years 

Manufacturers 27,266 9,533 10,932 1,273 49,004 -9% 34,543 11,756 11,074 1,27

 

Other Commercials 2,031 328 - 1 2,360 -54% 2,935 909 36 

 

Producers 11,119 2,897 486 1 14,503 -32% 12,463 3,190 501 

 

Dealers/Merchants 78,364 55,359 25,046 13,683 172,452 54% 99,832 75,068 29,837 13,87

 

Commodity Swap Dealers 64,812 78,102 55,763 23,352 222,029 67% 108,535 145,303 75,881 24,8

 

Unclassified Commercials 120 539 2 - 661 -96% 126 583 3 - 

Non-Registered 20,931 9,060 4,956 3,241 38,188 162% 41,808 25,532 6,879 3,24

 

Floor Brokers/Traders 16,130 20,600 8,999 2,460 48,190 89% 58,512 52,444 9,731 2,46

 

Hedge Funds 59,220 18,052 7,752 3,525 88,550 188% 74,950 29,117 10,857 3,53

 

Total 279,995 194,469 113,935 47,536 635,936 53% 343,904 433,704 144,798 49,20

Diff vs.2000 56% 51% 32% 144% 53% 65% 75% 42% 152% 

Commercial 183,713 146,757 92,228 38,310 461,008 34% 258,434 236,811 117,332 39,96

 

Non-Commercial 96,282 47,713 21,707 9,226 174,928 147% 175,270 107,093 27,467 9,24

 

NonComm + Swap Dealers 161,094 125,814 77,470 32,578 396,957 95% 283,805 252,397 103,348 34,04

 

Panel C: 2008

0-3 months 3-12 months 1-3 years 3+ years Total Diff vs.2000 0-3 months 3-12 months 1-3 years 3+ years 

Manufacturers 21,990 8,265 4,978 189 35,423 -34% 26,874 10,069 5,355 18

 

Other Commercials 861 373 170 - 1,404 -72% 1,000 387 170 - 

Producers 13,233 2,490 386 - 16,109 -24% 15,196 3,261 523 - 

Dealers/Merchants 86,349 63,208 43,399 9,844 202,800 81% 147,277 118,306 62,819 14,63

 

Commodity Swap Dealers 126,363 135,701 128,288 49,876 440,227 231% 217,358 343,346 292,534 94,7

 

Unclassified Commercials - - - - - -100% - - - - 

Non-Registered 74,951 41,741 23,072 8,116 147,880 914% 191,023 179,688 52,800 11,4

 

Floor Brokers/Traders 23,018 24,402 2,393 1,103 50,916 100% 139,676 147,703 10,736 1,10

 

Hedge Funds 125,066 145,722 76,899 24,692 372,378 1113% 181,590 252,291 137,939 38,3

 

Total 471,830 421,903 279,585 93,820 1,267,137 206% 1,055,052 919,992 562,877 160,36

Diff vs.2000 163% 227% 224% 381% 206% 251% 438% 451% 723% 

All Commercials 248,795 210,037 177,221 59,909 695,963 102% 407,704 475,369 361,401 109,53

 

All Non-Commercials 223,034 211,865 102,364 33,911 571,174 708% 512,288 579,682 201,476 50,83

 

NonComm + Swap Dealers 349,397 347,566 230,652 83,786 1,011,401 396% 729,646 923,029 494,010 145,54

 

Notes: Table 5 shows open interest in crude oil futures and options. After averaging the long and short positions of every large trad

we add these estimates of open interest appropriately (e.g., for all commercial traders, or for all positions less than 3 months, etc.). 

averages for each trader category and maturity bucket in 2000 (July-Dec.), 2004 (Jan.-Dec.), and 2008 (Jan.-Aug.). Table 5 is comp
Table 6: Augmented Dickey-Fuller (ADF) tests for the Regression Variables

Panel A: ADF for the Trace Statistic

Level First Difference 

Trace -0.70 -9.43***

Panel B: ADF for the Fundamentals

Level First Difference 

Kilian (2008b) Shipping Index -0.94 -5.82***

Crude Oil Spare Capacity -0.73 -11.23***

Industrial Commodity Price Index -0.42 -3.94***

Panel C: ADF for the Shares of Open Interest by Trader Category

Nearby 1-year 2-

Level First 

Difference 

Level First 

Difference 

Level 

Dealer/Merchants -4.14*** -12.04*** -2.71* -3.39** -1.83 

Manufacturers -3.61*** -15.79*** -2.62* -2.11 -0.46 

Producers -2.31 -7.16*** 0.66 -1.50 0.54 

Other Ag/Nat Resource 0.44 -2.25 -2.49 -1.61 -4.25*** 

Commodity Swap Dealers -8.74*** -11.99*** -6.29*** -6.76*** -3.47*** 

Floor Brokers/Traders -3.94*** -9.15*** -2.73* -3.59*** -3.44*** 

Hedge Funds -5.68*** -18.63*** -4.01*** -8.46*** -3.47*** 

Non-reporting 2.13 -9.63*** -2.35 -1.90 -3.16** 

All Commercials -8.32*** -26.73*** -4.61*** -6.29*** -2.70* 

All Non-Commercial -3.62*** -20.48*** -4.60*** -8.03*** -4.46*** 

Non-Commercials + Swap Dealers -5.20*** -20.10*** -6.17*** -8.95*** -4.47*** 

All Traditional Commercials -3.69*** -23.71*** -2.39 -3.45*** -1.30 

Notes: Stars (*, **, ***) indicate the rejection of non-stationarity at standard levels of statistical significance (10%, 5% 

Critical values are from McKinnon (1991). Panels A and B show, respectively, that the independent variable (the trace statistianalysis of nearby, one-year-out and two-year-out futures prices) and the explanatory variables that capture market fundamentals a

that the market shares of most trader categories are I(1). 
Table 7.1: Explaining the Trace Statistic by the Proportion of Open Interest held by each Trader Cate

Panel A: Single Variable for Positions at all 3 Contract Maturities

Constant Slope E-Trading LPI Nearby + 1-year + 2-year 

Dealer/Merchants -0.4525 2.9192* -0.5683*** 0.2962 -0.8589 

Manufacturers -1.1528* 2.2954* -0.5179*** 0.5160** -2.3584** 

Producers -1.1777 2.3729 -0.5803*** 0.4275 -0.5730 

Other Ag/Nat Resource -1.2090 2.4020 -0.5844*** 0.4239 1.1824 

Swap Dealers -1.1451 2.5498 -0.5721*** 0.3956* 0.3684 

Floor Brokers/Traders -1.1503 2.3782 -0.5865*** 0.4115 -0.1882 

Hedge Funds -1.2740 2.5833 -0.5500*** 0.4424* 0.5901 

Non-Reporting -0.5824 2.9445** -0.5099*** 0.2561* 3.7882*** 

Panel B: Separate Variables for Positions in Nearby vs. Long-dated Contracts

Constant Slope E-Trading LPI Nearby 1+2 y

Dealer/Merchants -0.3631 3.7606** -0.4747*** 0.2813 -0.7152 -5.3157*

Manufacturers -1.1802* 2.2586* -0.5185*** 0.5225** -2.3023** -2.9017 

Producers -1.2096 2.6689* -0.4939*** 0.4563* 0.4662 -34.6561

Other Ag/Nat Resource -1.2032 2.3887 -0.5856*** 0.4217 1.0782 7.6436 

Swap Dealers -1.0788 2.1463 -0.5865*** 0.3670 0.6842 -2.1063 

Floor Brokers/Traders -1.1382 2.5090 -0.5862*** 0.4116 -0.0040 -2.2947 

Hedge Funds -1.2754 2.6296 -0.5624*** 0.4472* 0.4646 1.2734 

Non-Reporting -0.8676 2.5943** -0.5291*** 0.3339* 3.5149** 10.9184

Panel C: Separate Variables for Positions at Each Contract Maturity 

Constant Slope E-Trading LPI Nearby 1-year 2-y

Dealer/Merchants -0.3658 3.7397** -0.4733*** 0.2820 -0.7059 -5.6044** -3.669

Manufacturers -1.1626* 1.8789 -0.5484*** 0.5125** -2.4461** 2.1487 -12.77

Producers -1.2084 2.7003* -0.4927*** 0.4557* 0.5095 -33.9794* -41.36

Other Ag/Nat Resource -1.1744 2.5036 -0.5761*** 0.4165 1.1409 -1.0078 15.10

Swap Dealers -1.3615 2.9263 -0.6348*** 0.4417 0.5332 0.3420 -11.75

Floor Brokers/Traders -1.1806 2.6616 -0.5955*** 0.4177 -0.0001 1.6124 -12.59

Hedge Funds -1.2374 2.6149 -0.5635*** 0.4367 0.4215 0.9278 2.963

Non-Reporting -0.8591 2.5925** -0.5303*** 0.3298* 3.6776*** 13.2414** -2.338

Notes: July 2000-August 2008. Bewley (1979) regressions control for the overall open interest across all contract maturities (TOI) a

after September 2006. We use an industrial commodity price index variable (LPI) as a proxy for fundamentals. We do not r

variables that are almost never statistically significantly different from 0 (dummies for June and December liquidity effects). Posit

the open interest, across all trader types, in nearby, 1- and 2-year contracts. Statistical significance at the 10%, 5% or 1% level is den
Table 7.2: Explaining the Trace Statistic by the Number of Open Contracts held by each Trader Cate

Panel A: Single Variable for Positions at all 3 Contract Maturities

Constant Slope E-Trading LPI Nearby + 1-year + 2-ye

Dealer/Merchants -1.4698 1.8412 -0.5689*** 0.4849* 1.38E-06 

Manufacturers -1.1634 2.3120 -0.6043*** 0.4298* -2.20E-06 

Producers -1.0456 2.6102 -0.5626*** 0.3620 9.29E-06 

Other Ag/Nat Resource -1.1850 2.4257 -0.5801*** 0.4210 6.09E-07 

Swap Dealers -0.9711 2.3883 -0.5783*** 0.3601 1.67E-06 

Floor Brokers/Traders -1.3339 2.2614 -0.5601*** 0.4599* 4.76E-06 

Hedge Funds -1.2322 2.8063* -0.5835*** 0.4570* 1.55E-06 

Non-Reporting -0.5621 2.8801** -0.6154*** 0.2870 5.86E-06** 

Panel B: Separate Variables for Positions in Nearby vs. Long-dated Contracts

Constant Slope E-Trading LPI Nearby 1+2 year

Dealer/Merchants -1.4557 1.9409 -0.5443*** 0.4770* 1.69E-06 -1.51E-07 

Manufacturers -1.1348 2.4134 -0.6012*** 0.4233* -2.31E-06 -2.48E-06 

Producers -0.9483 3.1042** -0.4816*** 0.3556 1.08E-05 -7.92E-05 

Other Ag/Nat Resource -1.1669 2.3541 -0.5740*** 0.4179 -1.73E-06 2.71E-05 

Swap Dealers -0.9346 2.3013 -0.5816*** 0.3461 1.84E-06 7.83E-07 

Floor Brokers/Traders -1.3329 2.4858 -0.5639*** 0.4614* 6.03E-06 -6.02E-07 

Hedge Funds -1.2261 2.8177* -0.5813*** 0.4554* 1.63E-06 1.37E-06 

Non-Reporting -0.6721 2.7522** -0.6242*** 0.3166 5.61E-06** 1.15E-05 

Panel C: Separate Variables for Positions at Each Contract Maturity 

Constant Slope E-Trading LPI Nearby 1-year 2

Dealer/Merchants -1.4241 1.9714 -0.5336*** 0.4711* 1.69E-06 -2.39E-06 6.68

Manufacturers -1.1297 1.9623 -0.6619*** 0.4046 -2.34E-06 2.64E-05 -4.92

Producers -1.0147 2.7147* -0.4910*** 0.3773 9.46E-06 -1.09E-04* 3.29

Other Ag/Nat Resource -1.1448 2.4988 -0.5669*** 0.4130 -1.22E-06 3.87E-05 1.15

Swap Dealers -0.9619 2.3778 -0.5814*** 0.3545 1.78E-06 1.53E-06 -2.81

Floor Brokers/Traders -1.3741 2.5927 -0.5628*** 0.4709* 5.72E-06 1.02E-05 -1.70

Hedge Funds -1.1587 2.8170* -0.5797*** 0.4350* 1.53E-06 5.47E-07 5.65

Non-Reporting -0.6798 2.6173* -0.6355*** 0.3180 6.06E-06** 1.45E-05 -1.62

Notes: July 2000-August 2008. All Bewley (1979) regressions control for the overall open interest across all contract maturities (TO

of side-by-side pit and electronic trading (1 after September 2006, 0 before). In this table, we use a non-exchange-traded industriindex variable (LPI) as a proxy for fundamentals. We do not report regression coefficients for variables that are almost never statisdifferent from 0 (dummies for June and December liquidity effects). Statistical significance at the 10%, 5% or 1% level is denoted b
Table 8: Explaining the Trace Statistic with Position Data from Swap Dealers and Multiple Non-Comm

Panel A.1: Commodity Swap Dealers and all Non-Commercial Traders Combined 

Constant Slope E-Trading LPI TOI Nearby 1+2 ye

All Non-Commercials + Swap Dealers (1. Market Share) -1.2403* 2.7710** -0.4992*** 0.3703** 5.40E-07 0.9091* 0.6665 

All Non-Commercials + Swap Dealers (2. Positions) -0.9391 2.6527* -0.5752*** 0.3699* 4.31E-07 1.18E-06* 3.72E-

Panel A.2: Breakdown by Sub-Category 

Model 1 Model 2

(Market Shares) (Positions)

Constant -0.6154 -0.5447Slope 2.9229** 3.0677*Electronic Trading -0.5333*** -0.6418***LPI (Commodity Prices) 0.2529 0.2809TOI (Total Open Interest) 5.38E-07 5.03E-07December 0.0993 0.0068June 0.0552 -0.0353

Commodity Swap Dealers Nearby 0.8269 1.15

(1-year + 2-year) -2.5287* -5.20

Floor Brokers & Traders Nearby -1.1535 0.87

(1-year + 2-year) 0.0112 2.28

Hedge Funds Nearby 0.1757 -0.80

(1-year + 2-year) 2.2005* 1.57

Non-Reporting Traders Nearby 3.2921** 5.76**

(1-year + 2-year) 9.1948* 25.40*

Notes: Sample period: July 2000 - August 2008. All Bewley (1979) regressions control for the overall open interest across every sing

dummies for June and December liquidity effects and the existence of side-by-side pit and electronic trading (1 after September 2006, use a non-exchange-traded industrial commodity price index variable (LPI) as a proxy for fundamentals. Statistical significance at th

denoted by *, **, or ***. 
Table 8 (continued): Explaining the Trace Statistic with Position Data from Multiple Commercia

Panel B.1: All Commercial Traders Combined (including Swap Dealers) 

Constant Slope E-Trading LPI TOI Nearby 1+2 ye

All Commercials (1. Market Share) -0.4329 3.1487** -0.4728*** 0.4380** 4.70E-07 -0.9203** -2.2878

All Commercials (2. Positions) -1.2190 2.1046 -0.5586*** 0.4047* 6.62E-07* 9.38E-07 1.84E-

Panel B.2: Breakdown by Sub-Category 

Model 1 Model 2

(Market Shares) (Positions)

Constant -1.1217** -0.7111Slope 2.3176** 2.7528**Electronic Trading -0.4561*** -0.5155***LPI (Commodity Prices) 0.4609** 0.3213TOI (Total Open Interest) 4.37E-07 5.81E-07*December 0.2118* 0.1241June 0.0104 -0.0673

Manufacturers Nearby -1.8737** -4.23

(1-year + 2-year) -1.1802 -0.03

Other Natural Resource Nearby -0.5001 -4.41

(1-year + 2-year) 18.5449** 39.20*

Producers Nearby 0.8301 6.94

(1-year + 2-year) -27.9645** -68.90*

Commodity Swap Dealers Nearby 0.8607** 1.90*

(1-year + 2-year) -0.7703 0.79

Notes: Sample period: July 2000 - August 2008. All Bewley (1979) regressions control for the overall open interest across every sing

dummies for June and December liquidity effects and the existence of side-by-side pit and electronic trading (1 after September 2006, use a non-exchange-traded industrial commodity price index variable (LPI) as a proxy for fundamentals. Statistical significance at th

denoted by *, **, or ***. 
Table 9: Explaining the Trace Statistic with Position Data from Swap Dealers and Multiple Non-Comm

Panel A.1: Commodity Swap Dealers and all Non-Commercial Traders Combined 

Constant Slope E-Trading Spare TOI Nearby 1+2 yea

Dealers (1. Market Share) 0.2110 4.0452** -0.4788*** -0.0372* 9.40 E-07*** 0.7579 0.1958 

wap Dealers (2. Positions) 0.4977*** 4.2057** -0.5232*** -0.0419** 6.53 E-07** 1.43 E-06** 3.15 E-0

Panel A.2: Breakdown by Sub-Category 

Model 1 

(Market Shares) 

Coefficient p value Coefficien

Constant 0.3944 ** 0.0117 0.543

Spare Capacity (0.0434) ** 0.0260 (0.046

Slope 3.4561 *** 0.0037 3.945

TOI (Total Open Interest) 7.81 E-07 *** 0.0076 6.95 E-07 

Electronic Trading (0.5281) *** 0.0000 (0.646

December 0.3488 0.1302 0.016

 

June 0.1773 0.1423 0.026

 

Floor Brokers & Traders Nearby (2.0397) 0.1665 -7.09 E-0

(1-year + 2-year) 0.7341 0.8823 1.26 E-06 

Hedge Funds Nearby 0.0168 0.9768 -9.12 E-0

(1-year + 2-year) 2.6162 * 0.0735 2.53 E-06 

Non-Reporting Traders Nearby 3.6755 ** 0.0476 7.44 E-06 

(1-year + 2-year) 8.2870 0.1371 2.41 E-05 

Commodity Swap Dealers Nearby 0.7819 0.2177 1.22 E-06 

(1-year + 2-year) (3.6009) ** 0.0422 -6.22 E-0

Notes: Sample period: July 2000 - August 2008. All Bewley (1979) regressions control for the overall open interest across every sing

dummies for June and December liquidity effects and the existence of side-by-side pit and electronic trading (1 after September 2006, use non-Saudi spare crude oil production capacity (SPARE) as a proxy for fundamentals. Statistical significance at the 10%, 5% or 1% 

or ***. 
Table 9 (continued): Explaining the Trace Statistic with Position Data from Multiple Commercia

Panel B.1: All Commercial Traders Combined (including Swap Dealers) 

Constant Slope E-Trading Spare TOI Nearby 1+2 y

All Commercials (1. Market Share) 1.1500*** 5.0167*** -0.3927*** -0.0580** 8.41 E-07*** -0.6959 -3.030

All Commercials (2. Positions) 0.3076** 3.6903* -0.4899*** -0.047* 9.50 E-07*** 1.27E-06 -1.68E

Panel B.2: Breakdown by Sub-Category 

Model 1 M

(Market Shares) (P

Coefficient p value Coefficient 

Constant 1.2188 *** 0.0008 0.5134 

Spare Capacity (0.0348) ** 0.0212 (0.0381Slope 4.9582 *** 0.0001 4.2750 

TOI (Total Open Interest) 7.04 E-07 *** 0.0004 8.17 E-07 

Electronic Trading (0.3683) *** 0.0005 (0.4727December 0.2855 ** 0.0253 0.1091 

June 0.2018 0.1021 (0.0186

 

Manufacturers Nearby (1.9859) * 0.0560 -3.17 E-06 

(1-year + 2-year) 2.4473 0.3285 8.03 E-08 

Producers Nearby 1.1362 0.4592 8.85 E-06 

(1-year + 2-year) (22.5346) ** 0.0455 -7.59 E-05 

Commercial Dealers Nearby (1.4558) *** 0.0097 -5.76 E-07 

(1-year + 2-year) (3.7662) * 0.0786 7.31 E-08 

Commodity Swap Dealers Nearby 0.2253 0.6172 2.24 E-06 

(1-year + 2-year) (1.6968) 0.2249 7.74 E-07 

Notes: Sample period: July 2000 - August 2008. All Bewley (1979) regressions control for the overall open interest across every sing

dummies for June and December liquidity effects and the existence of side-by-side pit and electronic trading (1 after September 2006, use non-Saudi spare crude oil production capacity (SPARE) as a proxy for fundamentals. Statistical significance at the 10%, 5% or 1% 

or ***. . 
Figure 1: Nearby and Backdated WTI Crude Oil Futures Prices, 2000-2008

Fig.1a: July 2000 to March 2004

15

30

45

7/5/20009/5/2000

11/5/2000

1/5/20013/5/20015/5/20017/5/20019/5/2001

11/5/2001

1/5/20023/5/20025/5/20027/5/20029/5/2002

11/5/2002

1/5/20033/5/20035/5/20037/5/20039/5/2003

11/5/2003

1/5/20043/5/2004

Year

Price (USD)

Nearby

1-year

2-year

Fig.1b: April 2004 to August 2008

25

85

145

4/6/20046/6/20048/6/2004

10/6/200412/6/2004

2/6/20054/6/20056/6/20058/6/2005

10/6/200512/6/2005

2/6/20064/6/20066/6/20068/6/2006

10/6/200612/6/2006

2/6/20074/6/20076/6/20078/6/2007

10/6/200712/6/2007

2/6/20084/6/20086/6/20088/6/2008

Year

Price (USD)

Nearby

1-year

2-year

Notes: Figure 1 shows the WTI futures prices for nearby (blue line), one-year out (pink line) and two-year out 

(yellow line) deliveries. A change in the pattern of the three time series is evident. Starting in the Fall of 2003, the 

three price series have an upward trend and move more closely together. 
Figure 2. Recursively Calculated Trace Test Statistics Scaled by the 5% Critical value for Variou

0123456

3/3/1992

9/3/1992

3/3/1993

9/3/1993

3/3/1994

9/3/1994

3/3/1995

9/3/1995

3/3/1996

9/3/1996

3/3/1997

9/3/1997

3/3/1998

9/3/1998

3/3/1999

9/3/1999

3/3/2000

9/3/2000

3/3/2001

9/3/2001

3/3/2002

9/3/2002

3/3/2003

9/3/2003

3/3/2004

9/3/2004

3/3/2005

9/3/2005

3/3/2006

Nearby, Nearby+1, Nearby+2Nearby, Nearby+6, Nearby+9Nearby, Nearby+12Nearby, Nearby+24Nearby, Nearby+12, Nearby+24

Jul.03

Jul.04

Notes: Figure 2 shows R-1 form of five recursively-calculated Trace Test statistics, scaled by the 5% critical value, for different set

March 1992 to August 2008. The solid horizontal line represents the 5% critical value (=1). The dark blue curve, starting close to 2 

trace for the “Nearby”, “Nearby + 1 month” and “Nearby + 2 months” contracts. For these near-term prices, there is strong evidenc

throughout the sample period. The green curve, starting just under 1, shows the scaled trace for the “Nearby”, “Nearby + 6 months” 

contracts. For these three series, there is evidence of a cointegrating vector from 1993 onward. The red curve, starting close to 0.3, 

the “Nearby” and “Nearby + 12 months” (i.e., 1-year) contracts. These two price series were not cointegrated until July 2003, with thcointegration increasing greatly after May 2004. Finally, the black curve (starting in 1998) shows the scaled trace statistic for t

months”, and “Nearby+24 months” (i.e., 1-year) contracts. These three price series were not cointegrated until Spring 2004. Finally, t

the scaled trace statistic for the “Nearby”, and “Nearby+24 months” (i.e., 2-year) contracts. These three price series were not cointe

three-year period was used for the initialization; hence, because daily price data are available for the 1-year (2-year) contract only from 

provide results starting in 1992 (1998) for tests involving the 1-year (2-year) contracts. Robustness checks with a one-year initialization similar results – in particular, there is no evidence of cointegration between the Nearby, 1-year and 2-year contracts until Fall 2003 at th
 

Figure 3: Number of Large Traders

Number of Crude Oil Traders Holding Reportable WTI Crude Oil Futures Positions

(January 2000 through August 2008)

0

50

100

150

200

250

300

350

7/5/20001/5/20017/5/20011/5/20027/5/20021/5/20037/5/20031/5/20047/5/20041/5/20057/5/20051/5/20067/5/20061/5/20077/5/20071/5/20087/5/2008

Total Number of Large Traders

Non-Commercial Long

Non-Commercial Short

Non-Commercial Spread

April 2003

January 2004

Notes: Figure 3 shows the total number of NYMEX traders reporting positions to the CFTC for its weekly 

Commitment of Traders Reports, between July 5 2000 and August 26 2008 (blue time series). The figure provides 

details for non-commercial traders, showing the number of non-commercials holding long (green time series), short 

(red time series) or spread positions (black time series). The number of non-commercial spread traders doubled 

between May 2001 and January 2004, and tripled again between February 2004 and July 2007. 

Figure 4: Market Shares

Contribution to the WTI Futures Open Interest: All Maturities (July 2000 - August 2008)

0%

20%

40%

60%

80%

100%

Jul-00

Oct-00

Jan-01

Apr-01

Jul-01

Oct-01

Jan-02

Apr-02

Jul-02

Oct-02

Jan-03

Apr-03

Jul-03

Oct-03

Jan-04

Apr-04

Jul-04

Oct-04

Jan-05

Apr-05

Jul-05

Oct-05

Jan-06

Apr-06

Jul-06

Oct-06

Jan-07

Apr-07

Jul-07

Oct-07

Jan-08

Apr-08

Jul-08

FBT

NRP

HF

AD

AP

AM

NC

AS

Notes: Figure 4 compares the contributions to the WTI futures open interest of eight types of large traders between 

July 5 2000 and August 26 2008. Commercial types include “Dealers/Merchants” (AD), “Manufacturers” (AM), 

“Producers” (AP), and “Commodity Swaps/Derivatives Dealers” (AS). Non-commercial types are “Hedge Funds” 

(HF), “Floor Brokers & Traders” (FBT), and “Non-Registered Participants” (NRP). Prior to August 2003, the “NC” 

category sums the positions of presently inactive commercial traders. 
Figure 5a: Daily Open Positions of the Major Commercial Trader Types, July 2000 to Augu

Commercial Manufacturers

-

50,000

100,000150,000200,000

Jul-00

Oct-00

Jan-01

Apr-01

Jul-01

Oct-01

Jan-02

Apr-02

Jul-02

Oct-02

Jan-03

Apr-03

Jul-03

Oct-03

Jan-04

Apr-04

Jul-04

Oct-04

Jan-05

Apr-05

Jul-05

Oct-05

Jan-06

Apr-06

Jul-06

Oct-06

Jan-07

Apr-07

Jul-07

Oct-07

Jan-08

Apr-08

Jul-08

0-3 months3-12 months1-3 years3+ years

Oil Producers

-

50,000

100,000150,000200,000

Jul-00

Oct-00

Jan-01

Apr-01

Jul-01

Oct-01

Jan-02

Apr-02

Jul-02

Oct-02

Jan-03

Apr-03

Jul-03

Oct-03

Jan-04

Apr-04

Jul-04

Oct-04

Jan-05

Apr-05

Jul-05

Oct-05

Jan-06

Apr-06

0-3 months3-12 months1-3 years3+ years

Oil Dealers and Merchants 

-

50,000

100,000150,000200,000

Jul-00

Oct-00

Jan-01

Apr-01

Jul-01

Oct-01

Jan-02

Apr-02

Jul-02

Oct-02

Jan-03

Apr-03

Jul-03

Oct-03

Jan-04

Apr-04

Jul-04

Oct-04

Jan-05

Apr-05

Jul-05

Oct-05

Jan-06

Apr-06

Jul-06

Oct-06

Jan-07

Apr-07

Jul-07

Oct-07

Jan-08

Apr-08

Jul-08

0-3 months3-12 months1-3 years3+ years

6

Commodity Swap Dealers

-

50,000

100,000150,000200,000

Jul-00

Oct-00

Jan-01

Apr-01

Jul-01

Oct-01

Jan-02

Apr-02

Jul-02

Oct-02

Jan-03

Apr-03

Jul-03

Oct-03

Jan-04

Apr-04

Jul-04

Oct-04

Jan-05

Apr-05

Jul-05

Oct-05

Jan-06

Apr-06

0-3 months3-12 months1-3 years3+ years

Notes: Figure 5a shows the open positions, by contract maturity, of the four most important commercial crude oil futures trader typeAM = Manufacturer, AP = Producer, and AS = Commodity Swaps/Derivatives Dealer (which includes FS = Financial Swaps / DeArbitrageur or Broker/Dealer). Every Tuesday between July 2000 and August 2008, for each trader and each futures delivery month

open interest in that contract as the average of the trader’s long and short positions. The open interests of all reporting traders in summed up appropriately (e.g., for all contracts with less than 3 months until maturity, etc.), and the Tuesday figures are then plottedallow for easy comparisons of the relative sizes of the open positions held by different trader categories. Figure 5a is directly comparab
Figure 5b: Daily Open Positions of the Major Non-Commercial Trader Types, July 2000 to Au

Hedge Funds

-

50,000

100,000150,000200,000

Jul-00

Oct-00

Jan-01

Apr-01

Jul-01

Oct-01

Jan-02

Apr-02

Jul-02

Oct-02

Jan-03

Apr-03

Jul-03

Oct-03

Jan-04

Apr-04

Jul-04

Oct-04

Jan-05

Apr-05

Jul-05

Oct-05

Jan-06

Apr-06

Jul-06

Oct-06

Jan-07

Apr-07

Jul-07

Oct-07

Jan-08

Apr-08

Jul-08

0-3 months3-12 months1-3 years3+ years

Floor Brokers and Traders

-

50,000

100,000150,000200,000

Jul-00

Oct-00

Jan-01

Apr-01

Jul-01

Oct-01

Jan-02

Apr-02

Jul-02

Oct-02

Jan-03

Apr-03

Jul-03

Oct-03

Jan-04

Apr-04

Jul-04

Oct-04

Jan-05

Apr-05

Jul-05

Oct-05

Jan-06

Apr-06

Jul-06

0-3 months3-12 months1-3 years3+ years

Non-Registered Participants 

-

50,000

100,000150,000200,000

Jul-00

Oct-00

Jan-01

Apr-01

Jul-01

Oct-01

Jan-02

Apr-02

Jul-02

Oct-02

Jan-03

Apr-03

Jul-03

Oct-03

Jan-04

Apr-04

Jul-04

Oct-04

Jan-05

Apr-05

Jul-05

Oct-05

Jan-06

Apr-06

Jul-06

Oct-06

Jan-07

Apr-07

Jul-07

Oct-07

Jan-08

Apr-08

Jul-08

0-3 months3-12 months1-3 years3+ years

Notes: Figure 5b shows the open positions, by contract maturity, of the three most important non-commercial crude oil futures trader Fund, and FBT = Floor Broker/Floor Trader. Every Tuesday between July 2000 and August 2008, for each trader and each futures measure the trader’s open interest in that contract as the average of the trader’s long and short positions. The open interests of all regiven category are then summed up appropriately (e.g., for all contracts with less than 3 months until maturity, etc.), and the Tuesdplotted. The graphs are scaled to allow for easy comparisons of the relative sizes of the open positions held by different trader categ

directly comparable to Figure 5a above. 
Figure 6: Open Interest at different Maturities: Changes, 2000-2008

Open Interest In Crude Oil Futures and Adjusted 

Option Positions

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

900,000

1,000,000

1,100,000

2000 2001 2002 2003 2004 2005 2006 2007 2008

< 3 months

3-12 months

12-36 months

> 3 yrs

Notes: Figure 6 shows annual open interest estimates for all reporting (i.e., large) traders by contract maturity. 

Every Tuesday from January 2000 through May 2008, for each trader and each futures delivery month, we measure 

the trader’s open interest as the average of the trader’s long and short positions in that contract. The open interests 

of all reporting traders are then summed up by maturity (all positions less than 3 months, etc.) and the annual 

averages are then computed. 

Figure 7: Cost of Rolling a Nearby Long Futures Position Forward, 2000-2008

-6.00%

-5.00%

-4.00%

-3.00%

-2.00%

-1.00%

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

5-Jan-99

6-Apr-99

6-Jul-99

5-Oct-99

4-Jan-00

4-Apr-00

5-Jul-00

3-Oct-00

2-Jan-01

3-Apr-01

3-Jul-01

2-Oct-01

2-Jan-02

2-Apr-02

2-Jul-02

1-Oct-02

31-Dec-02

1-Apr-03

1-Jul-03

30-Sep-03

30-Dec-03

30-Mar-04

29-Jun-04

28-Sep-04

28-Dec-04

29-Mar-05

28-Jun-05

27-Sep-0527-Dec-05

28-Mar-06

27-Jun-06

26-Sep-06

26-Dec-06

27-Mar-07

26-Jun-07

25-Sep-07

26-Dec-07

25-Mar-08

24-Jun-08 $0

$25

$50

$75

$100

$125

$150

nearby slope

Nearby WTI Futures Price

Nov. 2004

June 2004

Backwardation

Contango

Notes: Figure 7 shows the nearby WTI futures price (blue line, U.S. dollar; right scale) and the cost of rolling 

forward a nearby futures position, measured as the percentage difference between the nearby and next-nearby 

futures prices (red line, left scale). A negative value of the spread indicates a “backwardated” market, in which a 

trader holding a long futures position would realize a positive return from rolling the position forward (into the 

cheaper next-nearby contract). Conversely, a positive value of the spread indicates a “contangoed” market. 
Figure 8: Net Positions of Commodity Swap Dealers at Different Maturities, 2000-2008

WTI Futures: Commodity Swap Dealers, Net Positions (July 2000 - August 2008)

(150,000)

(100,000)

(50,000)

-

50,000

100,000

150,000

200,000

250,000

Jul-00

Oct-00

Jan-01Apr-01

Jul-01

Oct-01

Jan-02Apr-02

Jul-02

Oct-02

Jan-03Apr-03

Jul-03

Oct-03

Jan-04Apr-04

Jul-04

Oct-04

Jan-05Apr-05

Jul-05

Oct-05

Jan-06Apr-06

Jul-06

Oct-06

Jan-07Apr-07

Jul-07

Oct-07

Jan-08Apr-08

Jul-08

0-3 months

3-12 months

1-3 years

3+ years

Notes: Figure 8 shows the net positions, by contract maturity, held by all commodity Swap Dealers. Every Tuesday 

between July 2000 and August 2008, for each trader and each futures delivery month, we measure the trader’s net 

position in that contract. The net positions of all reporting traders in a given category are then summed up 

appropriately (e.g., for all contracts with less than 3 months until maturity, etc.), and the Tuesday figures are then 

plotted. 
Figure 9: Commodity Fundamentals

Non-Exchange Traded Commodity Real Price Index (1990=100)

-

50.00

100.00

150.00

200.00

250.00

199701

199707

199801

199807

199901

199907200001

200007

200101200107

200201

200207

200301200307

200401

200407200501

200507

200601

200607

200701

200707200801

200807

Price index (excl.coal, deflated with US CPI)

Price index (incl.coal, deflated without energy or food)

March 2004

Notes: Figure 9 displays an equally-weighted index of spot prices for seven non-exchange traded agricultural and 

industrial commodities (1990 = 100). The index covers rice, coal, and five industrial metals: manganese, rhodium 

cadmium, cobalt and tungsten. To abstract from absolute price-level differences across these commodities, we 

compute the index by compounding an equally-weighted average of the individual commodity returns, and deflating 

the resulting series. The spot price data are from Bloomberg; price indices, from the US Bureau of Labor Statistics. 

Figure 10: Crude Oil Market Fundamentals

WTI Spot Price v. Non-Saudi Crude Oil Spare Production Capacity (1995-2008) 

0.00

20.00

40.00

60.00

80.00

100.00

120.00

140.00

-0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

Surplus Capacity (monthly EIA estimate, million barrels per day)

WTI Spot Price 

(monthly average, USD per barrel)

Jan.1995 - Feb. 2004

Mar.2004 - Aug.2008

Notes: Figure 10 displays the spot price for West Texas Intermediate crude oil (U.S. dollars per barrel) against the 

crude oil spare production capacity outside of Saudi Arabia (million barrels per day). All data are from the Energy 

Information Administration (U.S. Department of Energy).