Speculators, Commodities and Cross-Market Linkages 

Bahattin Büyükşahin Michel A. Robe1

February 12, 2011 

 

 

1 Büyükşahin: International Energy Agency (IEA-OECD), 9 Rue de la Federation, 75739 Paris Cedex 15 France 

Tel: (+33) 1 40 57 65 71 Email: bahattin.buyuksahin@iea.org. Robe (Corresponding author): Kogod School of 

Business at American University, 4400 Massachusetts Ave. NW, Washington, DC 20016. Tel: (+1) 202-885-1880. 

Email: mrobe@american.edu. We thank Robert Hauswald, Jim Overdahl, Christophe Pérignon, Matt Pritsker and 

Genaro Sucarrat for detailed suggestions, and Valentina Bruno, Bob Jarrow, Pete Kyle, Delphine Lautier, Stewart 

Mayhew, Nikolaos Milonas, Geert Rouwenhorst, Wei Xiong, Pradeep Yadav and seminar participants at Southern 

Methodist University, Universidad Carlos III (Madrid), Université Paris Dauphine – Institut Henri Poincarré, the 

European Central Bank (ECB), the International Monetary Fund (IMF), the U.S. Securities and Exchange 

Commission (SEC), the 2010 CFTC Workshop on the Financialization of Commodity Markets, the 2010 Meeting of 

the Financial Management Association (FMA), the 2nd CEPR Conference on Hedge Funds (HEC-Paris) and the 20th

Cornell-FDIC Conference on Derivatives for helpful comments. We presented a companion paper on energy paper 

markets at the Atlanta Meeting of the American Economic Association under the title “Commodity Traders’ 

Positions and Energy Prices: Evidence from the Recent Boom-Bust Cycle” and thank Hank Bessembinder, our 

discussants, for a very helpful discussion. We are grateful to Yi Duo, Arek Nowak and Mehrdad Samadi for 

excellent research assistance. Our paper builds on results derived as part of research projects for the SEC (Robe) 

and the CFTC (Büyükşahin). The CFTC and the SEC, as a matter of policies, disclaim responsibility for any private 

publication or statement by any of their employees or consultants. The views expressed herein are those of the 

authors only and do not necessarily reflect the views of the CFTC, the SEC, the Commissioners, or other staff at 

either Commission. Errors or omissions, if any, are the authors' sole responsibility. 
1 

Speculators, Commodities and Cross-Market Linkages 

Bahattin Büyükşahin Michel A. Robe 

February 2011 

Abstract 

We utilize non-public data to construct a comprehensive dataset of individual 

trader positions in seventeen U.S. commodity futures markets and document 

the financialization of those markets between 2000 and 2010. We then show 

that the correlations between the returns on investable commodity and equity 

indices increase amid greater participation by speculators generally and hedge 

funds especially. We find no such effect for other kinds of commodity futures 

traders. The impact of hedge fund activity is complex. In particular, it is 

lower during periods of financial market stress. Our results indicate that who

trades helps explain the joint distribution of equity and commodity returns. 

JEL Classification: G10, G12, G13, G23

Keywords: Financialization, Cross-Market Linkages, Commodities, Equities, 

 Hedge funds, Index funds, Dynamic conditional correlations (DCC). 
2 

Introduction

In the past ten years, financial institutions have assumed an ever greater role in 

commodity futures markets.We provide novel evidence of this “financialization” and empirically 

show that it helps explain an important aspect of the joint distribution of commodity and equity 

returns. 

A large literature investigates whether the composition of trading activity (i.e., who

trades) matters for asset pricing. First, many traders face constraints on their choices of trading 

strategies. Hence, the arrival of traders facing fewer restrictions should in theory help alleviate 

price discrepancies (Rahi and Zigrand, 2009) and improve risk transfers across markets (Başak 

and Croitoru, 2006). Insofar as hedge funds are less constrained than other investors (e.g., Teo, 

2009) and commodity markets are partly segmented from other financial markets (Bessembinder, 

1992), this theoretical argument suggests that increased hedge fund activity could strengthen 

cross-market linkages. Second, suppose that the same traders who help link markets in normal 

times face, during periods of financial market stress, borrowing constraints or sundry pressures 

to liquidate risky positions. Then, their exit from “satellite” markets (such as emerging markets 

or commodity markets) after a major shock in a “central” asset market (such as the U.S. equity 

market) could in theory bring about cross-market contagion (Kyle and Xiong (2001), Kodres and 

Pritsker (2002), Broner, Gelos and Reinhart (2006), and Pavlova and Rigobon (2008)).2

 In the 

aftermath of the initial shock, conversely, reduced activity by value arbitrageurs or convergence 

traders could lead to a decoupling of the markets that they had helped link in the first place. 

In this paper, we show that hedge funds activity matters for market linkages, and that this 

impact differs in good vs. bad times. Controlling for macro-economic and commodity-market 

fundamentals, we find that commodity-equity co-movements are positively related to greater 

commodity market participation by financial speculators as a whole and by hedge funds 

especially – notably by hedge funds that trade in both equity and commodity futures markets. 

We find no such effect for other kinds of traders. The impact of hedge fund activity is complex. 

For instance, we find that it is weaker during periods of turmoil in financial markets. Our results 

contribute to the debate on the consequences of “financialization” in commodity markets. 

A major innovation of our paper is its dataset. In general, investigating whether specific 

types of traders contribute to cross-market linkages is empirically difficult because doing so 

 

2

 See Gromb and Vayanos (2010) for a thorough review of the theoretical work on limits to arbitrage and contagion. 
3 

requires detailed information about the trading activities of all market participants as well as 

knowledge of each participant’s main motivation for trading. We overcome this critical data 

pitfall by constructing a daily dataset of individual trader positions in seventeen U.S. commodity 

and equity futures markets. The underlying raw data, which are non-public, originate from the 

U.S. Commodity Futures Trading Commission’s (CFTC) large trader reporting system (LTRS). 

The LTRS contains information on the end-of-day positions of every large trader in each of these 

seventeen markets, as well as information on each trader’s main line of business. The individual 

position information in the LTRS covers more than 85% of the total open interest in the largest 

U.S. commodity futures markets from July 2000 to March 2010. 

We focus on the linkages between commodity and equity markets for several reasons. 

First, we need comprehensive data on trading in the “satellite market”. Commodity markets are 

ideal in this respect because commodity price discovery generally takes place on futures 

exchanges (rather than spot or over-the-counter – see Kofman, Michayluk and Moser, 2009) and 

it is precisely about the futures open interest that we have comprehensive information. Second, 

commodity-equity linkages fluctuate much more than the linkages between some other asset 

classes, offering fertile ground for an analysis of what (macroeconomic fundamentals, trading, or 

both) drives those fluctuations.3

 Third, we seek to add significantly not just to the literature on 

asset pricing but also to a fast-growing literature on the “financialization” of commodities – see, 

e.g., Acharya, Lochstoer and Ramadorai (2009), Büyükşahin and Robe (2009), Korniotis (2009), 

Tang and Xiong (2010), Etula (2010), Hong and Yogo (2010), and Stoll and Whaley (2010). 

In this last respect, we make three contributions. One, we provide a decade’s worth of 

novel data on the growing importance of different types of financial traders in a large number of 

U.S. commodity-futures markets. Two, we provide evidence about the extent to which different 

kinds of traders in those markets (in particular, hedge funds) also trade equity futures and show 

that such cross-market trading has grown substantially. Three, we use this heretofore unavailable 

information to shed light on the impact of financialization on cross-market linkages. 

 

3

 Theoretically, arguments have long existed that equities and commodities should be negatively correlated (Bodie, 

1976; Fama, 1981). Although there is to our knowledge no formal model of a common factor driving an equilibrium 

relationship between equity and commodity returns, empirical work shows that returns on commodity futures are 

driven not only by commodity-specific hedging pressures but also by some of the same macroeconomic factors that 

are priced for stocks – see, e.g., Bessembinder (1992), de Roon, Nijman and Veld (2000) and Khan, Khokher and 

Simin (2008). Consistent with these findings, Büyükşahin, Haigh and Robe (2010) and Chong and Miffre (2010) 

document that the dynamic conditional correlations between the rates of returns on equities and on commodites vary 

considerably over time around unconditional means close to zero (see also Gorton and Rouwenhorst (2006)). 
4 

We show that variations in the make-up of the commodity futures open interest do help 

explain long-term fluctuations in commodity-equity return co-movements. We employ ARDL 

regressions, using lagged values of the variables in the regression to tackle serial autocorrelation 

and possible endogeneity issues (arising from the possibility that speculative activity could result 

from high volatility and correlations, rather than the other way around). We find that a 1% 

increase in the overall commodity futures market share of hedge funds is associated ceteris 

paribus with an increase in equity-commodity return correlations of about 4%. 

We show that, in contrast, the positions of other kinds of commodity-futures market 

participants (traditional commercial traders, swap dealers and index traders, floor brokers and 

traders, etc.) hold little explanatory power for cross-market dynamic conditional correlations. 

Indeed, it is not just changes in the overall amount of speculative activity in commodity futures 

markets that helps explain the observed correlation patterns. Instead, we trace the explanatory 

power to hedge funds and, especially (and quite intuitively), to the subset of hedge funds that are 

active in both equity and commodity futures markets. 

Turning to the impact of financial turmoil on cross-market linkages, we identify two 

patterns. First, we show that equity-commodity co-movements are positively related to the TED 

spread (our proxy for financial-market stress). Pre-Lehman (from July 2000 through August 

2008), we find that a 1% increase in the TED spread brought about a 0.20% increase in the 

dynamic equity-commodity correlation estimate. Intuitively, hedge funds could be an important 

transmission channel of negative equity market shocks into the commodity space. In fact, the 

sign of an interaction term we use to capture the behavior of hedge funds during financial stress 

(“high TED”) episodes is statistically significant and negative. In other words, the impact of 

hedge fund activity is reduced during periods of global market stress. 

Second, we document that commodity-equity correlations soared after the demise of 

Lehman Brothers and remained exceptionally high through the Winter of 2010. Over and above 

the explanatory power of the TED spread, a time dummy capturing the post-Lehman period 

(September 2008 to March 2010) is highly statistically significant in all of our specifications. 

This finding suggests that the recent crisis is different from previous episodes of financial market 

stress and that this difference is reflected, in part, by an increase in cross-market correlations. 

The paper proceeds as follows. Section I discusses our contribution to the literature. 

Section II gives evidence on equity-commodity linkages. Section III presents our position data 
5 

and describes the financialization of commodity futures markets. Section IV presents our 

regressions and traces changes in equity-commodity return linkages to fundamentals as well as to 

hedge fund activity, stress, and the interaction of the last two factors. Section V concludes. 

I. Related Work

We contribute to several strands of the financial economics literature. As discussed in 

the introduction, we provide empirical evidence relevant to theoretical arguments that who trades 

helps explain some aspects of asset return patterns, and that the explanatory power of trader 

identity is different during periods of financial market stress. Our findings also place the present 

paper squarely within a fast-growing literature that analyzes whether the financialization of 

commodity markets in the past decade affects the levels or distributions of commodity prices. 

Three recent papers investigate the impact of financial speculation on commodity prices 

and returns. Using different techniques, Hamilton (2009), Korniotis (2009) and Kilian and 

Murphy (2010) conclude that macroeconomic fundamentals, rather than speculation, were most 

likely behind the 2004-2008 boom-bust commodity price cycle. Three other studies look at the 

impact of financialization through the lens of risk premia in commodity markets. Hong and 

Yogo (2010) argue that the growth (rather than the composition) of open interest in commodity 

futures markets drives commodity returns. Two related studies conclude that the risk-bearing 

capacities of broker-dealers (Etula, 2010) and the risk appetites of commodity producers 

(Acharya et al, 2009) play significant roles in determining commodity risk premia. 

Those six papers focus on price levels, returns or risk premia. We focus instead on 

commodities’ co-movements with equities. Through this lens and thanks to uniquely 

disaggregated data, we show that the composition of the open interest in commodity markets is 

an important explanatory factor of this aspect of commodities’ return distributions. Consistent 

with the predictions of theoretical models, we identify the activities of hedge funds and crossmarket traders as relevant to cross-market linkages. We also show that the extent to which 

speculative positions help explain linkages is weaker in periods of high financial-market stress. 

Related to our query, therefore, are two contemporaneous studies that utilize publiclyavailable data to investigate the possible impact of commodity index trading (CIT) on crosscommodity correlations in the past decade. One of those studies finds a CIT impact (Tang and 

Xiong, 2010); the other concludes that there is no causal relationship (Stoll and Whaley, 2010). 
6 

Unlike those papers, our main interest is in the co-movements between commodity and 

equity markets rather than the linkages between different commodity futures markets. Our paper 

further differs with respect to the types of financial traders whose behaviors and market impacts 

we analyze (not only index traders but also hedge funds and other types of commodity traders). 

Finally, our paper differs in how we measure financial activity in commodity markets. 

Absent other publicly available information, extant studies approximate total CIT activity 

in commodity futures markets by extrapolating from public CFTC information on CIT positions 

in 12 agricultural markets. Such data are only available starting in 2006. In contrast, we utilize 

the CFTC’s non-public trader-level position data for all U.S. markets dating from 2000. These 

data allow us to identify the daily and weekly shares of commodity futures open interest held not 

only by CITs but also by hedge funds and several other categories of commodity futures traders.4

 

Using the disaggregated data, we find little direct evidence that commodity-index trading 

drove long-term changes in equity-commodity co-movements. Our econometric analyses instead 

suggest that (besides macroeconomic fundamentals) it is mostly hedge fund positions that help 

explain changes in the strength of equity-commodity linkages. We furthermore show that the 

impact of hedge fund activity varies depending on the overall state of financial markets. 

Our interest in whether who trades matters differentially in periods of financial market 

stress links our paper to another literature – that on the financial vs. fundamental drivers of crossmarket linkages. Part of that literature asks whether financial shocks propagate internationally 

through financial channels such as bank lending (e.g., van Rijckeghem and Weder, 2001) and 

international mutual funds (e.g., Broner et al, 2006) or whether, instead, shocks spill over 

through real economy linkages such as trade relationships (e.g., Forbes and Chinn, 2004). Our 

findings suggest that, in periods when the TED spread shows elevated levels of financial-market 

stress, higher hedge fund participation ceteris paribus weakens (rather than increases) crossmarket correlations. 

Our analysis is thus also related to empirical papers that ask if speculators (in particular, 

hedge funds) can at times exert a destabilizing effect on financial markets. In equity markets, 

Brunnermeier and Nagel (2004) and Griffin, Harris, Shu and Topaloğlu (2011) argue that hedge 

 

4

 In this respect, our paper extends a small literature on the trading activities of specific types of market participants 

in U.S. futures markets – including Harzmark (1987) on speculative activity in agricultural commodity markets in 

1977-1981, Ederington and Lee (2002) on the heating oil market in the early 1990s, and Büyükşahin, Haigh, Harris, 

Overdahl and Robe (2009) on the crude oil market in 2000-2009. 
7 

funds moved stock prices during the technology bubble. In futures markets, however, Brunetti 

and Büyükşahin (2009) conclude that hedge funds do not affect price levels yet are key to the 

functioning of these markets through the liquidity that their trading provides to other market 

participants.5 Those studies focus on price levels for a given type of asset (in other words, on the 

first moments of an asset’s returns). Our paper, which measures the linkages between two types 

of asset markets, instead deals with the second moments of the joint distributions of asset returns. 

II. Commodity-Equity Co-movements, 1991-2010

This paper seeks to ascertain whether, in addition to economic fundamentals, commoditymarket participation by certain types of traders (speculators in general and hedge funds or index 

traders in particular) helps explain the extent to which a smaller “satellite” asset market (in our 

case, commodity futures) moves together with a “core” asset market (in our case, U.S. equities). 

This Section provides summary statistics for the returns on equity and commodity index 

investments, and plots our estimates of the dynamic conditional correlation (DCC, Engle 2002) 

between equity and commodity returns. This analysis extends, complements, or updates through 

the post-Lehman period a number of earlier studies documenting fluctuations over time in the 

extents to which commodities co-move with one another or with other financial assets (e.g., Erb 

and Harvey (2006), Gorton and Rouwenhorst (2006), Büyükşahin et al (2010), Chong and Miffre 

(2010), Silvennoinen and Thorp (2010), Stoll and Whaley (2010), Tang and Xiong (2010)). 

A. Commodity and Equity Return Data 

We use daily and weekly returns on benchmark commodity and stock market indices.6

 

We obtain price data from Bloomberg. Our sample runs from January 1991 (when the Goldman 

Sachs Commodity Index or GSCI was introduced as an investable benchmark) to March 2010. 

For commodities, we use the unlevered total return on Standard and Poor's S&P GSCI 

(“GSCI”), i.e., the return on a “fully collateralized commodity futures investment that is rolled 

forward from the fifth to the ninth business day of each month.” The GSCI includes twenty-four 

nearby commodity futures contracts. Because it uses weights that reflect each commodity’s 

 

5

 The evidence from foreign exchange and emerging markets on whether hedge funds are destabilizing is mixed. 

Chan, Getmansky, Haas and Lo (2006) provide a review the prior literature on hedge funds. 6

 Precisely, we measure the percentage rate of return on the I

th investable index in period t as rI

t = 100 Log(PIt / PIt-1), 

where PI

t is the value of index I at time t. 
8 

worldwide production figures, it is heavily tilted toward energy (see Table I). In robustness 

checks, we therefore use total (unlevered) returns on the second most widely used investable 

benchmark, Dow Jones’ DJ-UBS (until May 2009, DJ-AIG) total-return commodity index. This 

rolling index covers nineteen physical commodities and was designed to provide a more 

“diversified benchmark for the commodity futures market.” We find similar results for the GSCI 

and DJ-UBS indices, and therefore we focus our discussion on the GSCI. 

For equities, we focus on Standard and Poor’s S&P 500 index. This stock index is broadbased, making it a natural choice. Furthermore, the trading activity in the Chicago Mercantile 

Exchange’s S&P 500 e-Mini futures far exceeds that of other equity-index futures in the United 

States, making the S&P 500 e-Mini the ideal market in which to test the hypothesis that crossmarket traders may contribute to commodity-equity linkages. We find similar DCC patterns 

using Dow-Jones' Industrial Average (DJIA) index, and therefore we focus our discussion on the 

S&P500.7

 For comparison purposes, we also provide figures for the (generally slightly higher) 

correlations between the GSCI and the MSCI World Equity index (MSCI). 

B. Descriptive statistics 

Table II presents descriptive statistics for the weekly rates of return on the S&P 500 

equity index (Panel A) and on the S&P GSCI commodity index (Panel B). 

From January 1991 through February 2010, the mean weekly total rate of return on the 

GSCI was 0.0606% (or 3.16% in annualized terms), with a minimum of -14.59% and a 

maximum of 14.90%. The typical rate of return varied sharply across the sample period: it 

averaged 0.14% in 1992-1997 (7.45 % annualized); 0.045% in 1997-2003 (or a mere 2.36% 

annualized); and, 0.0290% in 2003-2010 (1.51% annualized). 

From January 1991 through February 2010, the mean weekly rate of return on the S&P 

500 was on average higher that on a commodity investment: 0.125% (or 6.71% in annualized 

terms), with a minimum of –15.77% and a maximum of 12.37%. However, the rank-ordering of 

the returns on the two asset classes fluctuates dramatically over time: in particular, equity returns 

crushed commodity returns in 1992-1997, but the reverse happened in 2003-2008. These 

differences suggest that equities and commodities do not move in lockstep. 

 

7

 On both equity indices, we use returns that omit dividends. This approach underestimates expected equity returns 

(Shoven and Sialm, 2000). Insofar as large U.S. corporations smooth dividend payments over time (Allen and 

Michaely, 2002), however, the correlation estimates that are the focus of our paper should be essentially unaffected. 
9 

Turning to volatility, Table II shows that the rate of return on a well-diversified basket of 

equities (S&P 500) is generally less volatile than that on commodities (GSCI). The standard 

deviation of the rate of return on commodities was particularly high after 2003. 

C. Dynamic Conditional Correlations 

Our main interest is in the relationship between commodity and equity returns at various 

points in time. With unconditional techniques such as rolling correlations or exponential 

smoothing, the sensitivity of the estimated correlations to volatility changes restricts inferences 

about the true nature of the relationship between variables, and periods of high volatility only 

magnify concerns of heteroskedasticity biases – see Forbes and Rigobon (2002). Consequently, 

we use the dynamic conditional correlation (DCC) methodology of Engle (2002) in order to 

obtain dynamically correct estimates of the intensity of commodity-equity co-movements. 

In essence, the DCC model is based on a two-step approach to estimating the timevarying correlation between two series. First, we estimate time-varying variances using a 

GARCH(p,q) model. For our sample, p=q=1. Second, we estimate a time-varying correlation 

matrix using the standardized residuals from the first-stage estimation. 

Figure 1A (1B) plots, from January 1991 to March 2010, our estimates of the dynamic 

conditional correlations between the weekly (daily) rates of return on two investable commodity 

indices (GSCI and DJ-UBS) vs. the unlevered rate of return on the S&P 500 equity index. As a 

benchmark, Figure 1A (1B) also provides a plot for the DCC between the weekly (daily) rates of 

return on the S&P 500 and a second U.S. equity index, the Dow Jones DJIA. 

Several facts are clear from Figures 1A-1B. First, in the eighteen months following the 

demise of Lehman Brothers in September 2008, equity-commodity correlations rose to levels 

never seen in the prior two decades. Second, prior to the Lehman collapse, equity-commodity 

correlations used to fluctuate substantially over time. At both weekly and daily frequencies, the 

equity-commodity DCC range was -0.38 to 0.4, approaching 0.4 in 1998, 2001-2002, mid-2006, 

and again in Fall 2008. Third, despite those ample fluctuations, there is no apparent up-trend in 

equity-commodity correlations prior to August 2008. 

D. Discussion 

Our finding that there was no obvious secular increase in commodity-equity correlations 

until Fall 2008 is in line with the conclusions of Büyükşahin et al (2010, p. 78) and Tang and 
10 

Xiong (2010, p.21) using weekly or daily data. It is also consistent with findings in Chong and 

Miffre (2010) and Silvennoinen and Thorp (2010) regarding dynamic correlations between the 

returns on the S&P 500 and on a number of individual commodity futures. Nevertheless, given 

that the DCC measure is the dependent variable in the econometric analyses of Section IV, we 

carry out several robustness checks. 

Figures 1A and 1B jointly show that the measurement frequency (daily vs. weekly) and 

the choice of commodity index (GSCI vs. DJ-UBS) are qualitatively immaterial. Figures 1C and 

1D likewise show that the choice of equity index (world vs. U.S.) does not alter this conclusion. 

In the present paper, we use the U.S. S&P 500 stock index (rather than a global stock 

market index) to compute equity-commodity correlations. There are two reasons why we do so. 

One, it minimizes the confounding effects of exchange rate fluctuations on the measurement of 

commodity-equity co-movements. Two, it allows us to match the correlation we seek to explain 

with the available equity-futures position data. Suppose, though, that the variable of interest 

were the MSCI-GSCI return co-movements: a comparison of Figure 1C (1D) with Figure 1A 

(1B) shows that we would still find no visible up-trend in equity-commodity correlations prior to 

September 2008. 

Figures 1E and 1F, which are based on unconditional rolling correlations, caution that not 

controlling for time-variations in return volatilities could lead to incorrect inferences. First, one 

might conclude from Figure 1F (based on unconditional one-year rolling correlations) that GSCIMSCI rolling correlations strengthened as early as 2006, i.e., well before the Lehman crisis – 

even though we know from Figure 1D (based on DCC) that such was not the case. Second, one 

might conclude from a comparison of Figures 1E and 1F (both based on one-year rolling 

correlations) that the choice of equity index (world vs. U.S.) matters – even though we know 

from a comparison of Figure 1C (1D) with Figure 1A (1B) that such is not the case. 

Having established that correlations fluctuated substantially, but not dramatically, prior to 

the Great Recession and having identified a structural break in mid-September 2008, we now ask 

what drives the fluctuations depicted in Figures 1A and 1B. Do market fundamentals explain the 

observed patterns or is the latter due partly to the financialization of commodity futures markets? 
11 

III. The Financialization of U.S. Commodity Futures Markets, 2000-2010 

In most U.S. commodity futures markets, the open interest is much greater in 2010 than it 

was a decade earlier. In this Section, we construct a comprehensive dataset of trader positions in 

seventeen futures markets and provide novel evidence that this growth entailed major changes in 

the composition of the overall open interest. In particular, we document considerable increases 

in the presence of hedge funds and in the extent to which equity futures traders are also active in 

commodity futures markets. 

We assemble our dataset by utilizing confidential data on individual trader positions from 

the U.S. government’s futures and options market regulator (i.e., the CFTC). This uniquely 

detailed information provides the foundation for the regression analyses of Section IV, in which 

we examine whether participatory changes have explanatory power for equity-commodity 

returns linkages. 

Sections III.A and III.B describe the dataset and contrast it with the less-detailed (but 

publicly available) information on futures open interest used in the prior literature.8

 Section III.C 

establishes that, compared to commercial activity, overall speculative activity has increased 

significantly since 2000. We then disaggregate this information and provide evidence on growth 

in these markets of hedge fund activity (Section III.C), cross-market trading (Section III.D) and 

commodity index trading (Section III.E). 

A. Trader Position Data 

We construct a database of daily trader positions in 17 U.S. commodity futures markets 

(see list in Table I) and the S&P 500 e-Mini futures market from July 1, 2000 to March 1, 2010. 

1. Raw Data on the Purpose and Magnitude of Individual Positions 

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

in the CFTC’s Large Trader Reporting System (LTRS). Specifically, to help fulfill its mission of 

 

8

 Only a handful of earlier studies have had access to disaggregated, non-public CFTC data. They are Harzmark 

(1987, 1991), studying the trading performance of individual traders in nine commodity futures markets from July 

1977 to December 1981; Leuthold, Garcia and Lu (1994), extending Harzmark’s work; Ederington & Lee (2002), 

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

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

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

Büyükşahin et al (2009), who document that increased market participation by hedge funds and commodity index 

traders since 2002 has helped link the prices of crude oil futures across the maturity structure. 
12 

detecting and deterring market manipulation, the CFTC’s Division of Market Oversight collects 

position-level information on the composition of open interest across all futures and options-onfutures contracts for each commodity. It gathers this information for each trader whose position 

exceeds a certain threshold (which varies by market). The CFTC also collects information from 

each large trader about his respective underlying business (hedge fund, swap trader, commodity 

producer, etc.) and about the purpose of his positions in different U.S. futures markets. 

Many smaller traders’ positions are also voluntarily reported to the CFTC and are thus 

included in the raw data made available for the present study. Depending on the specific market, 

our dataset therefore covers from 75% to more than 95% of the total open interest. 

The CFTC receives information on individual positions for every trading day. In our 

weekly analysis, we focus on the Tuesday reports because the underlying raw information is the 

one which the CFTC summarizes in weekly “Commitment of Traders (COT) Report” that it 

publishes every Friday at 3:30 p.m. Consequently, the information we provide in this Section 

can be contrasted with numerous extant studies of commodity markets that rely on COT data.9

 

2. Publicly Available Information 

 For every futures market with a certain level of market activity, the CFTC’s weekly COT 

reports provide information on the overall open interest. They also break down this figure 

between two (until 2009) or four (since 2009) categories of traders. 

Prior to September 2009, COT reports separated traders between two broad categories: 

“commercial” vs. “non-commercial.” The CFTC classifies all of a trader's futures and options 

positions in a given commodity as “commercial” if the trader used futures contracts in that 

particular commodity for hedging as defined in CFTC regulations. A trading entity generally is 

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”.10 The “noncommercial” group aggregates various types of mostly financial traders, such as hedge funds, 

mutual funds, floor brokers, etc. 

 

9

 A minor difference is that the large trader dataset 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. Thus, even our 

aggregate data are a bit more precise than the publicly available data. A second difference is COT frequency which, 

pre-2000, was lower than weekly. 

10 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. 
13 

Since September 4, 2009, COT reports differentiate between four (rather than two) kinds 

of traders. The reports now split commercial traders between “traditional” commercials 

(producers, processors, commodity wholesalers or merchants, etc.) and commodity swap dealers 

(in most markets this category includes commodity index traders). They also now differentiate 

between managed money traders (i.e., hedge funds) and “other non-commercial traders” with 

reportable positions.11 As of Fall 2010, however, the CFTC has not indicated plans to make this 

more detailed information available retroactively prior to 2006 or to break down the aggregate 

position information by contract maturity. 

3. Non-Public Information 

The LTRS data allow for much more differentiation than the simple COT classifications. 

Specifically, each reporting trader is classified into one of 28 (rather than a few) sub-categories – 

e.g., commercial dealers, swap dealers, producers, refiners, hedge funds, floor traders, brokers,... 

Because the LTRS data are contract-specific, they also make it possible to disentangle the 

activities of various kinds of traders at the near and far ends of the commodity-futures term 

structure. In contrast, public COT reports do not separate between traders’ positions at different 

contract maturities. Our results in Section IV show that this additional information is critical, in 

that it is the positions held by hedge funds in shorter-dated contracts (rather than further along 

the maturity curve) that contain explanatory power for equity-commodity index-return linkages. 

An independent contribution of the present paper is thus to provide, in Sections III.B to 

III.E, otherwise unavailable information on the composition of open interest in a cross-section of 

commodity futures markets – in particular, on the positions held by hedge funds since 2000 and 

on the extent to which equity futures traders have started to trade commodity contracts. We 

obtained clearance from the CFTC to summarize the individual position data for this paper. 

B. Increased Excess Speculation 

To gauge the growth of speculative activity in U.S. commodity futures markets, we use 

Working’s (1960) “T”. This index compares the activities of all “non-commercial” commodity 

futures traders (commonly referred to as “speculators”) to the demand for hedging that originates 

from “commercial” traders (commonly referred to as “hedgers”). 

 

11 COT reports also provide data on the positions of non-reporting traders (speculators, prop and other small traders). 
14 

1. Measuring “Excess Speculation” 

Working’s “T” is predicated on the idea that, if long and short hedgers’ respective 

positions in a given futures market were exactly balanced, then their positions would always 

offset one another and speculators would not be needed in that market. In practice, of course, 

long and short hedgers do not always trade simultaneously or in the same quantity. Hence, 

speculators must step in to fill the unmet hedging demand. Working’s “T” measures the extent 

to which speculation exceeds the level required to offset any unbalanced hedging at the marketclearing price (i.e., to satisfy hedgers’ net demand for hedging at that price). 

For each of the seventeen commodities in our sample (i = 1, 2, ..., 17), we calculate 

Working’s T every Tuesday from 2000 to 2010. In each market, we compute two “T” indices – 

one for short-term contracts (ܵܫܵ௜,௧) only, and one for all maturities (ܵܣܫ௜,௧). The latter measure 

can be computed using the publicly-available COT reports, which allows reader without access 

to the LTRS data to replicate this part of our results. 

For ܵܫܵ௜,௧, we use the three shortest-maturity contracts with non-trivial open interest. We 

do so based on the notion that it is those near-dated contracts whose prices are used to compute 

the commodity return benchmarks. Formally, in the i

th commodity market in week t: 

ܵܫܵ௜,௧ ≡ ܶ௜,௧ ൌ

ە

ۖ

۔

ۖ

	൅1ۓ

ܵܵ௜

௧,௜ܵܪ ൅ ௧,௜ܮܪ

௧,௜ܮܪ ൒ ௧,௜ܵܪ	݂݅		

1൅	

௜ܮܵ

௧,௜ܵܪ ൅ ௧,௜ܮܪ

௧,௜ܵܪ ൒ ௧,௜ܮܪ	݂݅	

							ሺ݅ ൌ 1, ... , 17ሻ

where ܵܵ௜ ≥ 0 is the (absolute) magnitude of the short positions held in the aggregate by all noncommercial traders (“Speculators Short”); ܵܮ ≤ ௜0 is the (absolute) value of all non-commercial 

long positions; ܪܵ ≤ ௜0 stands for all commercial (“Hedge Short”) short positions and ܮܪ ≤	௜0 

stands for all long commercial positions. 

We then average these individual index values to provide a general picture of speculative 

activity across all seventeen commodity markets in our sample: 

ܹܵܫܵ௧ ൌ	෍ݓ௜,௧

ଵ଻

௜ୀଵ

௧,௜ܵܫܵ

where the weight ݓ௜,௧ for commodity i in a given week t is based on the weight of the commodity 
15 

in the GSCI index that year (Source: Standard and Poor), rescaled to account for the fact that we 

focus on the seventeen U.S. markets (out of twenty-four GSCI markets) for which the LTRS 

position data are available. Table I lists the annual commodity weights per commodity, per year. 

 To obtain a picture of excess speculation across all contract maturities, we also compute: 

ܹܵܣܫ௧ ൌ	෍ݓ௜,௧

ଵ଻

௜ୀଵ

௧,௜ܣܫܵ

2. Excess Speculation in U.S. Commodity Futures Markets, 2000-2010 

Table III.A provides summary statistics of the weighted average speculative indices 

(WSIS and WSIA) from July 2000 to March 2010. During that period, the minimum value was 

1.11 for both short-term and all contracts; the maximum was 1.5 in near-term contracts (1.42 

across all maturities). In other words, speculative positions were on average 11% to 50% greater 

than what was minimally necessary to meet net hedging needs at the market-clearing prices. 

Figure 2A documents the growing importance of speculation in commodity markets in 

the past decade. Excess speculation increased substantially, from about 11% in 2000 to about 

40-50% in 2008.12 Interestingly, a comparison of the WSIS and WSIA curves in Figure 2A shows 

that, at almost all times in the sample period, excess speculation was several percentage points 

greater in near-term contracts than further out on the maturity curve. Notably, excess speculation fell 

after 2008, especially in near-term contracts (WSIS fell from 1.5 to 1.35). 

In sum, Figure 2A identifies a long-term increase, but also substantial variations, in excess 

commodity speculation. Those patterns will be of particular interest in the analysis of Section IV. 

Before proceeding to regression analyses, however, we investigate whether the changes in overall 

speculative activity hide differential patterns for distinct types of financial traders – hedge funds 

(III.C), index traders (III.D) and cross-market traders (III.E). 

C. Increased Hedge Fund Activity 

Working’s T lumps together all non-commercial traders: floor brokers and traders, hedge 

funds, other non-commercial traders not registered as managed money traders. Yet, there is little 

 

12 The values in Figure 2 are generally lower than historical T values for agricultural commodities. Peck (1981) gets 

values of 1.57-2.17; Leuthold (1983), of 1.05-2.34. See also Irwin, Merrin and Sanders (2008). 
16 

reason to believe that floor brokers in a specific commodity market should affect commodityequity linkages. Hedge funds, in contrast, are plausible candidates for such a role. 

1. Measuring Hedge Fund Activity 

We utilize the granularity of the LTRS data to compute summary statistics and plot time 

series of hedge funds’ share of the overall commodity futures open interest (see the Appendix for 

a formal definition of “hedge fund” in U.S. futures markets). We also compute similar market 

share figures for commodity swap dealers (a category that includes commodity index traders in 

most U.S. futures markets – see Section III.D) and for traditional commercial traders. For each 

sub-category of traders, we compute market shares across the three nearest-maturity futures with 

non-trivial open interest as well as across all contract maturities. 

Formally, we compute the open-interest or “market share” of a given category of traders, 

in each commodity futures market each Tuesday, by expressing the average of the long and short 

positions of all traders from this group in that market as a fraction of the total open interest in 

that market that same Tuesday. We then average these commodity-specific market shares across 

our seventeen commodity futures markets, using the commodity weights from Table I. 

We denote by WMSS_MMT, WMSS_AS, and WMSS_TCOM the respective weightedaverage market shares of hedge funds (or MMT, “managed money traders”), commodity swap 

dealers (AS, including CIT – commodity index traders), and traditional commercial traders 

(TCOM) in short-term contracts. We denote each types of traders’ contribution to the total open 

interest (i.e., across all contract maturities) as WMSA_MMT, WMSA_AS, and WMSA_TCOM. 

2. Hedge Funds in U.S. Commodity Futures Markets, 2000-2010 

The green line in Figure 2A depicts changes in the WMSS_MMT measure over time. This 

chart, together with Tables III.B and III.C, highlights several important market changes. 

First and foremost, hedge funds’ contribution to the commodity futures open interest 

more than tripled between 2000 and 2008. Their share grew from less than a tenth (a twentieth) 

of the near-term (overall) open interest in early 2002 to over a third (almost 30%) in early 2008. 

Second, Tables III.B and III.C, which provide summary statistics for various kinds of 

traders in near-term (III.B) and all (III.C) futures contracts, show that WMSS_TCOM and 

WMSA_TCOM both fell from 53% to less than 20% during that period. During the same period, 

the market share of floor brokers and traders did not change drastically. Thus, hedge funds’ 
17 

greater market share echoes a sharp drop in traditional commercial traders’ relative contribution 

to the overall open interest. This finding generalizes, to a cross-section of commodity futures 

markets, some of the observations of Büyükşahin et al (2009) in the specific case of WTI crude 

oil futures. 

Third, Figure 2A shows that the market share of hedge funds as a whole started trending 

downward in the second half of 2008. Interestingly, this trend has persisted in 2009 and 2010, 

i.e., in the period when cross-market correlations were unusually elevated. 

A natural question is whether all hedge funds pulled back from commodities in the postLehman turmoil. We debunk this notion in Section III.D, by showing that one type of hedge 

funds – those that trade in both commodity and equity markets – in fact increased its collective 

percentage contribution to the commodity open interest during that period. 

D. Increased Cross-Market Trading 

Of particular interest for this study are commodity futures traders that are also active in 

equity markets. Table III.D provides information on the number of such traders in each of the 

commodity futures market in our sample. Figure 2A and Table III.C document their growing 

contribution to the overall commodity-futures open interest in the past decade. 

1. Measuring Cross-Trading Activity 

Every reporting trader is uniquely identified in the CFTC’s LTRS. For each trading day, 

we use the unique ID of each commodity futures trader holding open positions at the market 

close that day to ascertain whether that trader also held overnight positions in the CME’s e-Mini 

S&P 500 equity futures at any point in our sample period. In the affirmative, we consider such a 

commodity-futures trader to be a “cross-market trader”. 

This exercise, which we summarize in Table III.D and discuss in Section III.D.2 below, 

tells us how many cross-traders there are on a given trading day. Intuition suggests, however, 

that traders that are active in both commodity and equity markets likely hold larger positions 

than do other commodity futures traders. We therefore also compute cross-market traders’ share 

of the overall open interest in a given commodity market on each trading day. To do so, we use 

the approach of Section III.C: for each group or subgroup of traders, we compute the open 

interest attributable to that group or sub-group as the average of the long and short positions of 
18 

the traders in that group in that market on that day as a fraction of the total open interest in that 

market on that same day. 

We denote by CMSA_MMTi,t, CMSA _ASi,t and CMSA _ALLi,t the shares of the open 

interest in the i

th commodity held respectively by cross-trading hedge funds (MMT), swap dealers 

(AS) and all commodity futures traders (ALL) (i = 1, 2, ..., 17). We then use the commodity 

weights from Table I to calculate the weighted-average market share of different types of traders 

(xxx = MMT, AS or ALL), across the seventeen commodity futures markets in our sample: 

௧,௜ݓ෍	ൌ xxx௧_ܣܵܯܥܹ

ଵ଻

௜ୀଵ

௧,௜xxx_ܣܵܯܥ

2. Equity-Commodity Cross-Market Activity in U.S. Futures Markets, 2000-2010 

Table III.D provides information the number of cross-market traders, and on the make-up 

of cross-trading activity, in the seventeen commodity futures markets in our sample period. In 

each of these commodity futures markets, hundreds of traders also held positions in the Chicago 

Mercantile Exchange’s e-Mini S&P 500 equity futures market (Column 1). In all but three of the 

smallest markets (feeder cattle, Kansas wheat and heating oil), at least 10% of all large 

commodity futures traders also traded equity futures in that period (Column 2). 

Using median figures (means are similar), we see that cross-market traders account for 

15% of all large commodity futures traders active at some point between July 2000 and March 

2010 (Column 2). Hedge funds make up almost 50% (Column 6) whereas commodity swap 

dealers account for less than 6% (Column 4) of the cross-trading contingent. Approximately 

38.9% of all cross-traders are classified as hedge funds in equity futures markets (Column 8). 

These median figures obscure two patterns. One, more than a quarter of all crude oil and 

gold traders also hold equity futures positions. Two, in contrast, only a seventh or less of all 

large traders in smaller futures markets (“softs”, “livestock” and heating oil) are cross-market 

traders. In smaller markets, more than half of the cross-traders are hedge funds while hedge 

funds make up about a third of all cross-traders in larger commodity markets. 

A comparison of Table III.D with the last four columns of Table III.C shows that the 

median weighted average share of the commodity futures open interest held by equitycommodity cross-traders was 40.9% during the sample period vs. 15% of the trader count. This 
19 

difference implies that cross-market traders typically hold (much) larger overnight positions than 

other types of commodity futures traders. 

The purple line in Figure 2A shows that the market share of cross-traders increased 

substantially between 2000 and 2010, from less than 20% of the total commodity futures open 

interest in 2000 and 2001 to around 40-47% since mid-2005. The light blue line in Figure 2A 

shows that cross-market-trading hedge funds’ share of the commodity open interest also grew 

substantially during that time period, but that the magnitude of their positions did not move in 

sync with the positions of other cross-market traders. 

Most striking is the difference between the activities of hedge funds that trade across 

markets vs. hedge funds that only hold positions in commodity futures markets. As a whole, the 

market share of hedge funds started a downward trend several months before the Lehman crisis. 

Notwithstanding some fluctuations, this trend accelerated the week following Lehman’s demise. 

In contrast, cross-trading hedge funds’ market share was fairly stable during that period and then 

increased steadily after mid-November 2008. 

E. Commodity Index Trading (CIT) 

While the non-public data to which we were granted access yields precise information on 

market shares for most trader categories (including, importantly, for hedge funds), it does not 

identify CIT activity in energy and metal markets at the daily or weekly frequency. This is 

because CIT activity percolates into commodity futures markets partly through CIT interactions 

with commodity swap dealers but, even in the CFTC’s non-public LTRS, CIT-related positions 

cannot be identified within the overall positions held by commodity swap dealers.13 

One solution to this issue (see, e.g., Stoll and Whaley (2010) and Tang and Xiong (2010)) 

is to extrapolate to all commodities the overall market share of CITs in twelve agricultural (“ag”) 

markets – information that has been published by the CFTC, weekly, for those twelve markets 

since 2006. This approximation, unfortunately, cannot be extended to prior years because of 

structural differences in CIT activity before and after 2005 (Büyükşahin et al, 2009). 

Furthermore, after 2006, the quality of that approximation depends on whether the magnitudes of 

investment flows into commodity markets were similar for ags and other types of commodities. 

In fact, the precision of the approximation gets worse over time insofar as specialized ag funds 

 

13 Since September 2008, the CFTC has provided quarterly reports about off- and on-exchange commodity index 

activity in a number of US commodity markets. 
20 

have grown in importance since 2006 and insofar as the open interest in ag futures markets has a 

different maturity structure than in energy and futures insofar markets. 

We draw instead on the granularity of the non-public CFTC data and on the notion that 

CIT activity has tended to concentrate in near-dated contracts. Specifically, we proxy the nearterm CIT market shares in each of our seventeen commodity futures markets each week by the 

shares of the near-dated open interest held by swap dealer in the same market.14 

Figure 2B plots WMSS_AS and WMSA_AS, i.e., the weighted-average market shares of 

swap dealers in respectively the three nearest-dated and all commodity futures. For shorter-term 

contracts in which CIT activity has tended to concentrate (Büyükşahin et al, 2009), Figure 2B 

shows that swap dealers’ contribution to the commodity open interest increased about two-thirds 

between mid-2002 and early-2007. Both WMSS_AS and WMSA_AS peaked in late October 2008 

before sharply falling in the following two months. In 2009, both series moved sideways with 

WMSS_AS approaching 25% of the near-dated open interest (a pattern seen from 2007 onward). 

IV. Economic Fundamentals, Speculation and Commodity-Equity Co-movements 

 In Section II, we showed that the conditional correlation between the weekly returns on 

investible equity and commodity indices fluctuates substantially over time. In Section III, we 

utilized a unique dataset of daily trader positions to quantify various aspects of financialization in 

U.S. commodity futures markets in the last decade. 

A comparison of Figures 1A and 2B suggests that the patterns exhibited by swap dealers’ 

positions do not much resemble the equity-commodity returns correlation patterns. Figure 2A, in 

contrast, suggests that the same is not true for hedge funds positions – especially for the positions 

of hedge funds that are active in both equity and commodity futures markets. 

In this Section, we ask formally whether long-term fluctuations in the intensity of 

speculative activity or in the relative importance of some kinds of trader (in particular, hedge 

funds) can help explain the extent to which commodity returns move in sync with equity returns. 

Besides speculative activity, of course, prior literature suggests that economic fundamentals and 

financial market stress should influence commodity-equity return correlations. Section IV.A 

 

14 An alternative methodology might be to proxy CIT activity by swap dealer positions changes that are common to 

all near-dated commodity futures. 
21 

therefore introduces our real-sector and financial-sector controls. Section IV.B discusses our 

ARDL regression methodology, which tackles possible endogeneity issues as well as the fact that 

some of our variables are stationary in levels while others are only stationary in first differences. 

Section IV.C presents our regression results. 

Tables III.A-B provide summary statistics for all the variables. Tables IV.A-B provide 

simple cross-correlations between the variables. Tables V-VIII summarize our regression results. 

A. Real Sector and Financial-Market Conditions 

1. Macroeconomic Fundamentals 

Business cycle factors affect commodity returns (e.g., Erb and Harvey, 2006; Gorton and 

Rouwenhorst, 2006). Furthermore, the response of U.S. stock returns to crude oil price increases 

depends on whether the increase is the result of a demand shock or of a supply shock in the crude 

oil space (Kilian and Park, 2009). These empirical facts point to the need to control for realsector factors when explaining time variations in the strength of equity-commodity linkages. 

To do so, we use a measure of global real economic activity recently proposed by Kilian 

(2009), who shows that “increases in freight (shipping) rates may be used as indicators of (...) 

demand shifts in global industrial commodity markets.” The Kilian measure is a global index of 

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

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

routes, commodities and ship sizes.” 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.” This indicator is available monthly from 1968.15 We derive weekly 

estimates (which we denote SHIP) by cubic spline. 

Table III.A contains summary statistics for SHIP. Figure 3, which charts its value from 

2000 to 2010, shows an inverse long-term relationship between SHIP and our DCC estimates – 

suggesting that correlations increase when world demand for commodities is low. 

While SHIP provides a measure of worldwide economic activity, U.S. macroeconomic 

conditions are central to U.S. equity prices and could affect commodity prices. Consequently, 

we also consider two macroeconomic variables that may be relevant when studying commodity-

 

15 We are grateful to Lutz Kilian for providing an update of his monthly series (Kilian, 2009) through March 2010. 
22 

equity relationships. One, which we denote ADS, is the Aruoba-Diebold-Scotti (2008) gauge of 

U.S. economic activity. This measure is available at weekly frequency for the entire sample 

period (1991-2010). The other variable captures U.S. inflationary expectations and the intuition 

that commodities may provide a better hedge against inflation than equities do. We use the 

figures released each month by the Federal Reserve Bank of Cleveland and carry out a linear 

interpolation to derive weekly figures, which we denote INF. Table III.A provides summary 

statistics for these two other macroeconomic indicators. 

2. Financial Stress and Lehman Crisis 

Cross-market co-movements increase during episodes of financial stress. Hartmann, 

Straetmans and de Vries (2004) identify cross-asset extreme linkages in the case of bond and 

equity returns from the G-5 countries. In a similar vein, Longin and Solnik (2001) document that 

international equity market correlations increase in bear markets. For commodities, Büyükşahin, 

Haigh and Robe (2010) show that equity and commodity markets can behave like a “market of 

one” during extreme events. We account for this reality in two ways. 

First, we include the TED spread in our regressions as a proxy of financial-market stress. 

Table III.A provides statistical information on the TED variable. The TED spread varied widely 

during our sample period, with a minimum of 0.027% and a maximum of 4.33%. 

Second, Figure 4 shows that the TED spread, though particularly high after the onset of 

the Lehman crisis, had already started rising in the previous 13 months (starting in August 2007 

when a French financial group froze two funds exposed to the sub-prime market). In contrast, 

equity-commodity correlations did not visibly increase until after the demise of Lehman Brothers 

in September 2008, and remained exceptionally high through the Winter of 2010. This 

difference suggests that the post-Lehman sub-period is exceptional. We use a time dummy 

(DUM) to account for specificities of that sub-period which the TED spread might not capture. 

B. Methodology 

Before testing the explanatory power of different variables on the DCC between equity 

and commodity returns, we check the order of integration of each variable using Augmented 

Dickey Fuller (ADF) tests. Unit root tests for the variables in our estimation equation are 

summarized at the bottoms of Tables III.A and III.B. They show that some of the variables are 

I(1) whereas the others are I(0). 
23 

By construction, correlations are bounded above (+1) and below (-1) so the DCC variable 

should intuitively be stationary. Yet, the ADF tests do not reject the non-stationarity of the DCC 

estimates in our sample period. This result holds at the 1% level of significance for the entire 

sample period (2000-2010, see Table III.A) and at the 10% level of significance for a sub-sample 

ending prior to the demise of Lehman Brothers (2000 to September 2008).16

In order to find the long run effects of different variables on commodity-equity return 

correlations, we use an autoregressive distributed lag (ARDL) model estimated by ordinary least 

squares. In this model, the dynamic conditional correlation is explained by lags of itself and 

current and lagged values of a number of regressors (fundamentals as well as traders’ positions). 

The lagged values of the dependent variable are included to account for slow adjustment of the 

correlation between commodities and equities. This approach also allows us to calculate the 

long-run effect of the regressors on the correlation. If our correlation measure is, in fact, 

stationary, then the ARDL model, estimated by OLS, should give us consistent parameter 

estimates. If our DCC variable is non-stationary, as suggested by the ADF test statistics, then 

both short-run and long run parameters in the ARDL model can be consistently estimated by 

OLS if there is a cointegrating relationship (Pesaran and Shin (1999)). 

Specifically, Pesaran and Shin (1999) show that the ARDL model can be used to test the 

existence of a long-run relationship between underlying variables and to provide consistent, 

unbiased estimators of long-run parameters in the presence of I(0) and I(1) regressors. The 

ARDL estimation procedure reduces the bias in the long run parameter in finite samples, and 

ensures that it has a normal distribution irrespective of whether the underlying regressors are I(0) 

or I(1). By choosing appropriate orders of the ARDL(p,q) model, Pesaran and Shin (1999) show 

that the ARDL model simultaneously corrects for residual correlation and for the problem of 

endogenous regressors. 

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

following ARDL(p,q) model: 

 

௜ି௧ݕ௜ߛ෍൅ ௧ݓߜ ൌ ௧ݕ																																																									

௣

௜ୀଵ

௜ି௧ݔ௜ߙ෍൅

௤

௜ୀ଴

൅ ߝ௧																																			ሺ1ሻ

 

16 Because it is well known that ADF tests have low power with short time spans of data, we also employ another 

test developed by Kwiatkowski et al (KPSS, 1992) to further analyze the DCC variable. Unlike the ADF test, the 

KPSS test has stationarity as the null hypothesis. With the KPSS test, we find that the null of stationarity cannot be 

rejected at the 5% level of significance but is rejected at the 1% significance level. 
24 

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

for a t x s vector of deterministic variables such as an intercept, seasonal dummies, time trends, 

or exogenous variables with fixed lags.17 In vector notation, Equation (1) is: 

ߛሺܮሻݕ௧ ൌ ݓߜ௧ ൅ 	ߙሺܮሻݔ௧ ൅ ߝ௧

where ߛሺܮሻ is the polynomial lag operator 1െߛଵܮെߛଶܮଶ െ...ߛ௣ܮ ;௣ߙሺܮሻ is the polynomial lag 

operator ߙ ൅ ଴ߙଵܮ൅ߙଶܮଶ ൅...൅ߙ௤ܮ ;௤and L represents the usual lag operator (ܮ௥ݔ௧ ൌ ݔ௧ି௥). 

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 (1) for the cointegrating 

relationship ݕ௧ ൌ ߰ݓ௧ ൅	ݔߠ௧ ൅	ߥ௧ by: 

௤ොߙ൅...൅ ොଵߙ ൅ ଴ොߙ ൌ෠ ߠ

1െߛොଵ െ ߛොଶ െ⋯െߛො௣

߰෠ ൌ ߜመ

1െߛොଵ െ ߛොଶ െ⋯െߛො௣

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

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

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

of the order of the distributed lag function on ݕ௧ and the explanatory variables ݔ௧. We carry out 

a 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 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. 

C. Regression Results 

Tables V to VIII sumarize our regression results. Table V establishes the explanatory 

power of economic fundamentals (SHIP and, to a lesser extent, ADS) and financial stress (TED). 

 

17 The error term is assumed to be serially uncorrelated.
25 

Table VI establishes the additional explanatory power of speculation and hedge fund activities. 

Tables VII and VIII present some of our robustness checks. 

1. Real sector and financial stress variables 

Panels A and B in Table V show that, for our sample period (2000-2010) as well as for an 

extended period (1991-2000, starting when the GSCI first became investable but before the start 

of our detailed position dataset), the commodity-equity DCC measure is statistically significantly 

negatively related to SHIP. Insofar as SHIP captures world demand for commodities, this finding 

confirms the intuition that cross-market correlations increase in globally bad economic times. 

Our two U.S. macroeconomic indicators (ADS and INF) have less explanatory power. 

The coefficient for ADS is consistently positive but is not always statistically significant. 

Intuitively, if equities and commodities respond differently to high inflation, then DCC and INF

should be negatively related. Column 7 of Panel A (using data from 1991-2010) supports this 

prediction. In most of our other regressions, however, INF is not statistically significant. As 

Gorton and Rouwenhorst (2006) note, asset returns are volatile relative to inflation; consequently, 

longer-term correlations better capture the inflation properties of commodity and equity investments. 

The lack of significance of INF, especially in regressions using data from 2000-2010 only, may 

therefore be a mere artifact of sample length. 

All of our models include a variable capturing momentum in equity markets (denoted 

UMD). This variable always has a positive coefficient (consistent with the notion that equity 

momentum could spill over into other risky assets such as commodities) but we never find UMD

to be a statistically significant explainer of commodity-equity correlations. 

The difference between Panels A and B in Table V is that the specifications in Panel B 

include a dummy for the post-Lehman period (DUM). That time dummy is always strongly 

statistically significant and positive, supporting the graphical evidence in Section II that this subperiod is exceptional. 

Our ARDL estimations show that commodity-equity return correlations also have a 

positive long-term relationship to the TED variable (our proxy for stress in financial markets). In 

2000-2010, a 1% increase in the TED spread brought about a 0.20 to 0.30% increase in the 

dynamic equity-commodity correlation; this increase is statistically significant at the 5% level of 

confidence (at the 1% level in 2000-2008; see Table VII). 
26 

Interestingly, Panel A suggests that TED was not a significant factor in 1991-2000. The 

differential importance of the TED spread in those two successive decades raises the question of 

whether changes in trading activity might help explain this evolution. We next turn to this issue. 

2. Speculative activity and hedge fund market share 

Table VI.A is key to our contribution. It shows that trading activity in commodity futures 

markets helps explain long-term changes in commodity-equity linkages. 

Intuitively, there is no reason to expect that traditional commercial traders (oil refiners, 

grain elevators, etc.) should drive correlations between commodity and stock index returns. 

Table VI.A confirms this intuition, showing little or no explanatory power for WMSS_TCOM. 

Likewise, insofar as commodity swap dealing overwhelmingly reflects swap dealers’ 

over-the-counter relationships with traditional commercials or with unlevered, long-only, passive

commodity-index traders (CITs), we would not expect swap dealers’ positions to affect crossmarket correlations. This is because CITs do not engage in value-arbitraging and may not alter 

their positions under financial-market stress. Table VI.A buttresses this intuition: swap dealers’ 

share of commodity open interest (WMSS_AS) is never statistically significantly positive. These 

findings present an interesting counterpoint to the conclusions of Stoll and Whaley (2010) and 

Tang and Xiong (2010), both based on public data, regarding intra-commodity market linkages. 

The main finding in Table VI.A is that, after controlling for economic fundamentals, it is 

speculative activity in commodity futures markets that helps explain the fluctuations in the 

commodity-equity DCC estimates over time. Ceteris paribus, an increase of 1% in the overall 

commodity-futures market share of hedge funds (WMSS_MMT) is associated with dynamic 

conditional equity-commodity correlations that are approximately 4% to 7% higher (given a 

mean hedge fund market share of about 25%). 

Crucially, Working’s “T” index of excess speculation in commodity futures markets, 

which aggregates the activities of all non-hedgers across all maturities, has less explanatory 

power than hedge fund activity in short-dated contracts. Precisely, the WSIA variable is often but 

not always significant and, when it is statistically significant, its level of statistical significance is 

typically lower than that of WMSS_MMT. A comparison of likelihood ratios supports this 

reading – suggesting that it is the positions of hedge funds specifically, rather than the activities 

of non-commercial traders in general, that help explain the correlation patterns. 
27 

3. Cross-market trading 

Table VI.B uses specifications similar to Table VI.A but focuses on cross-market traders. 

Two interesting results emerge. First, as intuition would suggest, the market share of hedge 

funds that trade in both equity and commodity markets helps explain long-term linkages between 

equity and commodity returns. Second, the market share of commodity swap dealers that are 

also active in equity markets is sometimes statistically significant – but always with a negative

sign. These results suggests that it is value arbitrageurs’ willingness to take positions in both 

equity and commodity markets, rather than the trading activities of more traditional commodity 

market participants, that help tie satellite and central markets. 

4. Interaction between hedge funds and financial stress 

Table VI shows that greater hedge fund participation enhances cross-market linkages. 

Yet if the same arbitrageurs or convergence traders, who bring markets together during normal 

times, face borrowing constraints or other pressures to liquidate risky positions during periods of 

financial market stress, then their exit from “satellite markets” after a major shock in a “central” 

market could lead to a decoupling of the markets that they had helped link in the first place. 

To test this hypothesis, some specifications in Table VI include an interaction term that 

captures the behavior of hedge funds in financial stress episodes. This interaction term is almost 

always statistically significant and is always, as expected, negative. That is, ceteris paribus, the 

ability of hedge fund activity to explain commodity-equity co-movements is lower during 

periods of elevated market stress. 

5. Implications for portfolio management 

Our results suggest that non-public information on the composition of commodity futures 

open interest (or, more generally, the make-up of trading activity in financial markets) could be 

relevant to asset allocation decisions. A corollary is that portfolio managers could benefit from a 

recent CFTC decision to disaggregate the position information that it makes available to the 

public, and to separate between aggregate trader positions according to the traders’ underlying 
28 

businesses – hedge fund, commodity-swap dealer, one of several “traditional” commercial types 

(commodity producer; manufacturer or refiner; wholesaler, dealer or merchant; other), etc.18 

D. Robustness 

Our results are qualitatively robust to using additional proxies for commodity investment; 

to introducing dummies to control for unusual circumstances in financial markets; and to use of 

alternative measures of hedge fund activity in commodity futures markets. 

1. Commodity indexing activity 

In the past decade, investors have sought an ever greater exposure to commodity prices. 

Part of this exposure has been acquired through passive commodity index investing. Some of 

this investment has, in turn, found its way into futures markets through commodity swap dealers. 

In our regressions, however, we never find the WMSS_AS variable (which measures commodity 

swap dealers’ market share in short-dated contracts) to be statistically significant and positive. 

One possible reason is that, although a part of commodity swap dealers’ positions in 

short-dated commodity futures reflects their over-the-counter interactions with index traders, the 

rest of their futures positions reflect over-the-counter deals with more traditional commercial 

commodity traders. In other words, the WMSS_AS variable is only an imperfect proxy of 

commodity index trading activity in commodity futures markets. 

We therefore also used another proxy for investor interest in commodities: the post-2004 

daily trading volume in the SPDR Gold Shares exchange-traded fund (ETF). Although this 

volume grew massively between 2004 and 2010, the GOLD_VOLUME variable does not help 

explain changes in commodity-equity correlations. 

Taken together with the lack of significance of the WMSS_AS variable, our interpretation 

is that the activities of passive commodity investors do not affect equity-commodity linkages. 

This result presents an interesting counterpoint to the findings of Büyükşahin et al (2009), who 

show that increased commodity index trading activity in the WTI crude oil futures market 

provided additional liquidity that helped integrate crude oil prices across contract maturities. 

 

 

18 It is worth noting that WMSA_MMT and WSIA (but not WMSS_MMT) can, after 2006, be constructed on the basis 

of the CFTC’s COT reports. In other words, some of the information that we show matters is publicly available. 
29 

2. Hedge fund activities in near-dated commodity futures vs. across the maturity curve 

Table VII repeats the analysis of Table VI except that we measure speculative activity 

and different traders’ market shares using position information across all maturities (rather than 

just the three nearest-maturity contracts with non-trivial open interest). The statistical 

significance of all the position variables drops dramatically, except for the variable capturing 

hedge fund activity (WMSA_MMT is sometimes significant at the 5% level). Again, Table VII 

shows little statistical evidence that swap dealers or traditional commercial traders affect the 

dynamic cross-market correlations. 

Taken together, Tables VI and VII imply that it is the positions of hedge funds in shorterdated commodity futures (rather than their activities in commodity markets further along the 

futures maturity curve) that help explain equity-commodity linkages. This result is intuitive, in 

that the GSCI index is constructed using short-dated futures contracts and, hence, one expects 

that it is short-dated positions that may matter for commodity-equity correlations. 

3. The Lehman crash 

In the last 30 months of the sample period, the TED spread was very or extremely high 

compared to spreads in most of the previous decade. The TED spread first jumped in August 

2007, following the suspension of investor withdrawals from some funds managed by a French 

bank. It reached stratospheric levels in September 2008, following the Lehman debacle. 

A natural question is whether our results are affected by unusual TED spread patterns 

during the latter part of our sample period. The answer is negative: our results are qualitatively 

robust to the introduction of either one of two dummies (one for the August 2007 - August 2009 

period or one for the September 2008-March 2010 period), and to the concomitant introduction 

of interaction terms between the relevant dummy and the TED variable. 

Table VIII provides additional evidence of robustness. It repeats the analysis of Table 

VI, with a sample that ends prior to November 2008 – the month when DCC estimates soared 

upward of 0.4 for the first time since the inception of the investable GSCI commodity index. 

The results in Table VIII are qualitatively similar to those in Table VI. The main difference is 

that the statistical significance of the hedge fund variables is stronger pre-crisis. Combined with 

the statistical significance of the post-Lehman dummy (DUM) in every single specification in 
30 

Table VI, as well as with the negative sign of the INT_TED_MMT interaction term, this finding 

suggests that hedge fund activity per se is not responsible for the exceptionally high correlation 

levels observed since the end of 2008. 

V. Conclusion and Further Work

Over the course of the past two decades, the strength of commodity-equity linkages has 

fluctuated substantially. The last decade also witnessed growing commodity-market activity by 

hedge funds, commodity index traders, and other financial traders. These facts provide fertile 

grounds to analyze whether the make-up of trading activity helps explain the joint distribution of 

commodity and equity returns. 

To ascertain whether who trades matters for asset pricing, we use non-public trader-level 

information from the CFTC. We create a daily dataset of all large trader positions in seventeen 

U.S. commodity and equity futures markets from 2000 to 2010. Using this uniquely detailed 

dataset, we present novel evidence on the financialization of commodity-futures markets. We 

then document that, besides macro-economic fundamentals, variations in the composition of the 

open interest in commodity futures markets do help explain fluctuations in the extent of 

commodity-equity co-movements. 

We trace this explanatory power to the activities of speculators in general and hedge 

funds in particular – especially hedge funds that are active in both equity and commodity futures 

markets. We find that the positions of other kinds of participants commodity-futures market 

(swap dealers and index traders, traditional commercial traders, floor brokers and traders, etc.) 

do not have much explanatory power for cross-market correlation patterns – whether or not they 

take positions in both equity and commodity markets. 

We identify two clear patterns when considering the impact of financial market stress on 

equity-commodity co-movements. First, both before and after Lehman Brothers’ demise, we 

find commodity-equity correlations to be positively related to the TED spread (our proxy for 

financial stress). Intuitively, hedge funds could be an important transmission channel of negative 

equity market shocks into the commodity space. In fact, we find that the impact of hedge fund 

activity is lower in periods of stress. Second, commodity-equity correlations soared after the 

demise of Lehman Brothers in Fall 2008 and remained unusually high through Spring 2010. 
31 

This last finding suggests a natural venue for further research. Our analyses in this paper 

establish that, in the long run, macroeconomic fundamentals, hedge fund activity, and the TED 

spread (a proxy for financial-market stress) help explain observed fluctuations in commodityequity correlations. An interaction term between hedge fund activity and TED spread is also 

significant. Yet, in addition to those other variables, we find that a time dummy for the crisis 

period (September 2008 to March 2010) is always highly significant. Further research is thus 

needed to explain the dummy. 

One possible explanation might be that, amid a crisis of historical proportion and massive 

uncertainty, a radical shortening of market participants’ horizons could have made both equities 

and commodities much more (less) sensitive to short-term (long-term) economic developments. 

Another possibility might be that the increased financialization of commodity markets we have 

documented in this paper could have made commodity markets more susceptible to “financial 

market sentiment” – either directly (for example, if collective decisions by passive investors to 

exit risky markets when uncertainty rises lead to greater correlations between different risky 

assets) or indirectly (for example, if the prevalence of gloom among too many traders 

overwhelmed value arbitrageurs’ willingness to take on risky positions). A companion project 

investigates those possibilities – in particular, whether sentiment (interacted or not with our 

proxies for value arbitraging and for index trading activity) helps explain the increases of equitycommodity correlations, of cross-commodity correlations and of the common component of 

stock returns during the Great Recession. 

 
32 

Bibliography 

Acharya, Viral V., Lars A. Lochstoer and Tarun Ramadorai (2010). “Limits to Arbitrage and 

Hedging: Evidence from Commodity Markets”, CEPR Working Paper No. 7327, July 2009. 

Presented at the European Finance Association 2009 (Bergen) and American Finance 

Association 2010 (Atlanta) Meetings. Revised, Columbia University, January 2010. 

Allen, Franklin and Roni Michaely (2002). “Payout Policy.” In Constantinides, G., Harris, M., 

and Stulz, R. [Eds.], Handbook of Financial Economics. North-Holland. 

Aruoba, S. Borağan, Francis X. Diebold, Chiara Scotti (2009). “Real-time Measurement of 

Business Conditions”. Journal of Business and Economic Statistics. 27 (4), pp. 417-27. 

Başak, Süleyman and Benjamin Croitoru (2006). “On the Role of Arbitrageurs in Rational 

Markets”. Journal of Financial Economics, 81(1), pp. 143-73. 

Bessembinder, Hendrik (1992). “Systematic Risk, Hedging Pressure, and Risk Premiums in 

Futures Markets”. Review of Financial Studies, 5 (4), pp. 637-67. 

Broner, Fernando A., R. Gaston Gelos and Carmen M. Reinhart (2008). “When in Peril, 

Retrench: Testing the Portfolio Channel of Contagion”, Journal of International Economics, 

69 (1), pp. 203-30. 

Brunetti, Celso and Bahattin Büyükşahin (2009). “Is Speculation Destabilizing?” Working 

Paper, John Hopkins University, July. http://ssrn.com/abstract=1393524 

Brunnermeier, Markus K., and Stefan Nagel (2004). “Hedge Funds and the Technology 

Bubble”, Journal of Finance 59, pp. 2013-2040. 

Büyükşahin, Bahattin, Michael S. Haigh, Jeffrey H. Harris, James A. Overdahl, J.A., and Michel 

A. Robe (2009). “Fundamentals, Trading Activity and Derivative Pricing”. Paper presented 

at the 2009 Meeting of the European Finance Association. http://ssrn.com/abstract=966692 

Büyükşahin, Bahattin, Michael S. Haigh and Michel A. Robe (2010). “Commodities and 

Equities: Ever a ‘Market of One’?” Journal of Alternative Investments, 12 (3), pp. 75-95. 

Büyükşahin, Bahattin and Michel A. Robe (2009). “Commodity Traders' Positions and Crude 

Oil Prices: Evidence from the Recent Boom-Bust Cycle.” Paper presented at the 2010 

American Economic Association Meeting, Atlanta, January. 

Chan, Nicholas, Mila Getmansky, Shane M. Haas, and Andrew W. Lo (2006). “Systemic Risk 

and Hedge Funds.” In M. Carey and R. Stulz (eds.), Risks of Financial Institutions, Chicago, 

IL: University of Chicago Press, pp. 35-330. 

Chang, Eric C., J. Michael Pinegar and Barry Schachter, 1997. “Interday Variations in Volume, 

Variance and Participation of Large Speculators Source,” Journal of Banking and Finance, 

21 (6), pp. 797-810. 

Chong, James and Joȅlle Miffre (2010). “Conditional Return Correlations between Commodity 

Futures and Traditional Assets”. Journal of Alternative Investments, 12 (3), pp. 61-75. 
33 

de Roon, Frans A., Theo E. Nijman, and Chris Veld (2000). “Hedging Pressure Effects in 

Futures Markets”. Journal of Finance 55 (3), pp. 1437-56 

Ederington, Louis H. and Jae Ha Lee, 2002. “Who Trades Futures and How: Evidence from the 

Heating Oil Futures Market”. Journal of Business, 75 (2), pp. 353-73. 

Engle, Robert (2002). “Dynamic Conditional Correlation: A Simple Class of Multivariate 

Generalized Autoregressive Conditional Heteroskedasticity Models”.Journal of Business and 

Economic Statistics, 20 (3), pp. 339–50. 

Erb, Claude B. and Campbell R. Harvey (2006). “The Strategic and Tactical Value of 

Commodity Futures”. Financial Analysts Journal, 62 (2), pp. 69–97.

Etula, Erkko (2010). “Broker-Dealer Risk Appetite and Commodity Returns”. Staff Report No. 

406, Federal Reserve Bank of New York, November 2009. Revised, September 2010. 

Forbes, Kristin J. and Menzie D. Chinn (2004). "A Decomposition of Global Linkages in 

Financial Markets Over Time”. Review of Economics and Statistics, 86 (3), pp. 705-22. 

Forbes, Kristin J. and J. Roberto Rigobon (2002). “No Contagion, Only Interdependence: 

Measuring Stock Market Comovements”. Journal of Finance, 57 (5), pp. 2223-61. 

Gorton, Gary and K. Geert Rouwenhorst (2006). “Facts and Fantasies about Commodity 

Futures”. Financial Analysts Journal, 62(2), pp. 47–68.

Griffin, John M., Jeffrey H. Harris, Tao Shu and Selim Topaloğlu (2011). “Who Drove and Burst 

the Tech Bubble?” Journal of Finance, forthcoming. 

Gromb, Denis and Dimitri Vayanos (2010). “Limits of Arbitrage: The State of the Theory”. 

NBER Working Paper No. 15821, March. 

Haigh, Michael S., Jana Hranaiova, and James A. Overdahl (2007). “Price Volatility, Liquidity 

Provision, and the Role of Hedge Funds in Energy Futures Markets”. Journal of Alternative 

Investments, 8 (4), pp. 10-38. 

Hamilton, James D. (2009). “Causes and Consequences of the Oil Shock of 2007-08”. NBER 

Working Paper No. 15002, May. 

Hartmann, P., S. Straetmans and C. G. de Vries (2004). “Asset Market Linkages in Crisis 

Periods”. Review of Economics and Statistics, 86 (1), pp. 313-326. 

Hartzmark, Michael L. (1987). “Returns to Individual Traders of Futures: Aggregate Results,””. 

Journal of Political Economy, 95 (6), pp. 1292-306. 

Hartzmark, Michael L. (1991). “Luck versus vs. Forecast Ability: Determinants of Trader 

Performance in Futures Markets”, Journal of Business, 64 (1), pp. 49-74. 

Hong, Harrison and Motohiro Yogo (2009). “Commodity Market Interest and Asset Return 

Predictability.” Paper presented at the 2010 American Finance Association Meeting, Atlanta, 

January. Revised, Princeton University, March 2010. 
34 

Irwin, Scott H., Merrin, Robert P. and Dwight R. Sanders (2008). “The Adequacy of 

Speculation in Agricultural Futures Markets: Too Much of a Good Thing?” Marketing and 

Outlook Research Reports No.2, University of Illinois at Urbana-Champaign. 

Khan, Saqib, Zeigham Khokher and Timothy Simin (2008). “Expected Commodity Futures 

Returns.” Working Paper, Penn State University, July. 

Kilian, Lutz (2009). “Not all oil price shocks are alike: Disentangling demand and supply 

shocks in the crude oil market.” American Economic Review, 99 (3), pp. 1053-69. 

Kilian, Lutz and Dan Murphy (2010). “The Role of Inventories and Speculative Trading in the 

Global Market for Crude Oil.” CEPR Discussion Papers 7753, May. 

Kodres, Laura E. and Matthew Pritsker (2002). A Rational Expectations Model of Financial 

Contagion. Journal of Finance, 57 (2), pp. 1540-62. 

Kofman, Paul, David Michayluk, and James T. Moser (2009). “Reversing the Lead, or a Series 

of Unfortunate Events? NYMEX, ICE and Amaranth”. Journal of Futures Markets, 29 (12), 

pp. 1130-60. 

Korniotis, George M. (2009). “Does Speculation Affect Spot Price Levels? The Case of Metals 

with and without Futures Markets.” Working Paper No. 2009-29, Board of Governors of the 

Federal Reserve System, May. 

Kyle, Albert S. and Wei Xiong (2001). “Contagion as a Wealth Effect.” Journal of Finance, 56 

(4), pp. 1401-40. 

Kwiatkowski, Denis, Peter C.B. Phillips, Peter Schmidt and Yongcheol Shin (1992). “Testing the 

Null Hypothesis of Stationarity against the Alternative of a Unit Root: How Sure are We that 

Economic Time Series Have a Unit Root?” Journal of econometrics, 54, pp. 159-178. 

Leuthold, Raymond M. (1983). “Commercial Use and Speculative Measures of the Livestock 

Commodity Futures Markets”. Journal of Futures Markets 3 (2), pp. 113-35. 

Leuthold, Raymond M., Philip Garcia, and Richard Lu (1994). “The Returns and Forecasting 

Ability of Large Traders in the Frozen Pork Bellies Futures Market,” Journal of Business, 67 

(3), pp. 459-73. 

Longin, Francois and Bruno Solnik (2001). “Extreme Correlation of International Equity 

Markets.” Journal of Finance, 56 (2), pp. 649–76. 

Pavlova, Anna and Roberto Rigobon (2008). “The Role of Portfolio Constraints in the 

International Propagation of Shocks.” Review of Economic Studies, 75 (4), pp. 1215-56. 

Peck, Anne E (1981). “The Adequacy of Speculation on the Wheat, Corn, and Soybean Futures 

Markets.” In Research in Domestic and International Agribusiness Management, R. A. 

Goldberg, ed.,Greenwich, CN: JAI Press, Inc., Vol. 2, pp. 17-29. 

Pesaran, M. Hashem and Yongcheol Shin, 1999. “An Autoregressive Distributed Lag Modelling 

Approach to Cointegration Analysis.” In S. Strom, ed., Econometrics and Economic 

Theory in the Twentieth Century. New York, NY: Cambridge University Press. 
35 

Rahi, Rohit and Jean-Pierre Zigrand (2009). “Strategic Financial Innovation in Segmented 

Markets.” Review of Financial Studies, 22 (6), pp. 2941-71. 

Shoven, John. B. and C. Sialm. (2000). “The Dow Jones Industrial Average: The Impact of 

Fixing its Flaws.” Journal of Wealth Management, 3 (3), pp. 9–18. 

Silvennoinen, Annastiina and Susan Thorp (2010). “Financialization, Crisis and Commodity 

Correlation Dynamics”. Research Paper No 267, Quantitative Finance Research Centre, 

University of Technology, Sydney. 

Stoll, Hans R. and Robert E. Whaley (2010). “Commodity Index Investing and Commodity 

Futures Prices.” Journal of Applied Finance, 2010 (1), forthcoming. 

Tang, Ke and Wei Xiong. (2010)· Index Investing and the Financialization of Commodities. 

NBER Working Paper No. 16325, September. 

Teo, Melvyn (2009). “The Geography of Hedge Funds.” Review of Financial Studies, 22 (9), 

pp. 3531-61. 

van Rijckeghem, Caroline and Beatrice Weder (2001). “Sources of Contagion: Is It Finance or 

Trade? Journal of International Economics, 54 (2), pp. 293-308. 

Working, Holbrook (1960). “Speculation on hedging markets.” Stanford University Food 

Research Institute Studies 1, pp. 185-220. 
36 

Figure 1: Return Correlations between U.S. Equity vs. GSCI Commodity Indices

Figure 1A: Weekly Returns Correlations (DCC), January 1991 to March 2010

Figure B: Daily Returns Correlations (DCC), January 1991 to March 2010

Notes: Figure 1A (1B) depicts the time-varying correlation between the weekly (daily) unlevered rates of return 

(precisely, changes in log prices) on the S&P 500 (SP) equity index and: (i) the S&P GSCI total return commodity 

index (GSTR, green line) or (ii) the DJ-UBS total return commodity index (DJTR, red line). As a benchmark, the 

Figure also plots the correlation between the S&P 500 equity index and the other traditional equity index, the Dow 

Jones Industrial Average equity index (DJIA, blue line on top). In each case, we estimate dynamic conditional 

correlation by log-likehood for mean-reverting model (DCC_MR, Engle, 2002) using Tuesday-to-Tuesday returns 

from January 3, 1991 to March 1, 2010. 

‐0.6

‐0.4

‐0.2

‐1E‐15

0.2

0.4

0.6

0.8

1

DCC_MR_SP_DJ

DCC_MR_SP_GSTR

DCC_MR_SP_DJTR

‐0.6

‐0.4

‐0.2

‐1E‐15

0.2

0.4

0.6

0.8

1

DCC_MR_SP_DJ

DCC_MR_SP_GSTR

DCC_MR_SP_DJTR
37 

Figure 1C: Weekly Return DCC -- World Equity vs. Commodity Indices, 1991 to 2010

Figure 1D: Daily Return DCC -- World Equity vs. Commodity Indices, 1991 to 2010

Notes: Figure 1C (1D) depicts the dynamic conditional correlations between the weekly (daily) unlevered rates of 

return (precisely, changes in log prices) on S&P’s GSCI total return commodity index and: (i) the S&P 500 (SP) 

equity index (green line) or (ii) the MSCI World Equity Index (MXWO, orange line). As a benchmark, Figure 1C 

also plots the correlation between the S&P 500 US equity index and the MSCI World equity index (purple line on 

top). In each case, dynamic conditional correlation are estimated by log-likehood for mean-reverting model 

(DCC_MR, Engle, 2002) from January 3, 1991 to March 1, 2010. Patterns are suimilar, though equity-commodity 

correlations are slightly greater when estimated with the world equity index rather than with the US equity index. 

After 2003, the difference between US and world correlations is typically 0.05 to 0.1. 

‐0.6

‐0.4

‐0.2

‐1E‐15

0.2

0.4

0.6

0.8

1

DCC_MR_SP_MXWO

DCC_MR_SP_GSTR

DCC_MR_MXWO_GSTR

‐0.6

‐0.4

‐0.2

‐1E‐15

0.2

0.4

0.6

0.8

1

DCC_MR_SP_MXWO

DCC_MR_SP_GSTR

DCC_MR_MXWO_GSTR
38 

Figure 1E: Daily Return Correlations (Rolling) -- US Equity vs. Commodity Indices, 1991 to 2010

Figure 1F: Daily Return Correlations (Rolling) -- World Equities vs. Commodities, 1991 to 2010

Notes: Figures 1E and 1F depict unconditional rolling correlations between the daily rates of return on 

commodity indices and on U.S. (S&P 500, Fig. 1E) or world (MSCI, Fig. 1F) equity indices. Precisely, Figure 1E 

depicts one-year rolling correlations between the daily unlevered rates of return (precisely, changes in log prices) on 

the S&P 500 (SP) equity index and: (i) the S&P GSCI total return commodity index (GSTR, green line) or (ii) the 

DJ-UBS total return commodity index (DJTR, red line). As a benchmark, the Figure also plots the rolling 

correlation between the S&P 500 equity index and the Dow Jones Industrial Average equity index (DJIA, blue line

on top). Figure 1F depicts one-year rolling correlations between the daily unlevered rates of return (precisely, 

changes in log prices) on S&P’s GSCI total return commodity index and: (i) the S&P 500 (SP) equity index (GSTR, 

green line) or (ii) the MSCI World Equity Index (MXWO, orange line). As a benchmark, Figure 1F also plots the 

correlation between the S&P 500 US equity index and the MSCI World equity index (purple line on top). A 

comparison of, respectively, Figure 1E with Figure 1C and of Figure 1F with Figure 11D shows the importance of 

controlling for timve-varying variances . 

‐0.6

‐0.4

‐0.2

‐1E‐15

0.2

0.4

0.6

0.8

1

MOVAV_SP_DJ

MOVAV_SP_GSTR

MOVAV_SP_DJTR

‐0.6

‐0.4

‐0.2

‐1E‐15

0.2

0.4

0.6

0.8

1

MOVAV_SP_MXOW

MOVAV_SP_GSTR

MOVAV_MXOW_GSTR
39 

Figure 2: Financialization of Commodity Markets

Figure 2A: Excess Speculation, Hedge-fund activity and Cross-Market Trading

Figure 2B: Swap Dealing (including Commodity Index Trading)

Notes: Figure 2A plots the weighted-average speculative pressure index (“Working’s T”) in the 17 U.S. commodity 

futures markets linked to the GSCI index in near term contracts (dark red, WSIS) or across all maturities (dark 

blue, WSIA).between the first week of January 2000 and the first week of March 2010. The green line shows the 

aggregate share of the short-term open interest held by hedge funds in the same commodity markets (WMSS_MMT). 

The purple line shows the share of the commodity futures positions held by large commodity futures traders that 

also trade US equity futures (WCMSA_ALL), and the bright blue line shows the market share of hedge funds that 

also trade US equity futures (WCMSA_MMT). During the same time period, Figure 2B shows the market shares of 

commodity swap dealers in the same 17 commodity futures markets during the last decade across all contract 

maturities (dark blue, WMSA_AS).or in the nearest three months (dark red, WMSS_AS). 

0

0.1

0.2

0.3

0.4

0.5 WSIA (rescaled Working T ‐‐ all maturities)

WSIS (rescaled Working T ‐‐neart contracts)

WMSS_MMT

WCMSA_ALL

WCMSA_MMT

Nov. 17, 2008

Oct. 6, 2008

0.1

0.15

0.2

0.25

0.3

0.35

WMSA_AS

WMSS_AS

Oct. 20, 2009

Dec. 29, 2009

(all maturities)

(3 nearest months)
40 

Figure 3: Equity-Commodity Correlations, Economic Activity, and Hedge-fund Cross-Trading

Notes: The green line in Figure 3 shows, between the first Tuesday of January, 2000 and the last Tuesday of 

February, 2010, the dynamic conditional correlation between the weekly unlevered rates of return (precisely, 

changes in log prices) on the S&P 500 (SP) equity index and on the S&P GSCI total return (GSTR) index. We use 

Tuesday-to-Tuesday return to estimate dynamic conditional correlations by log-likehood for mean-reverting model 

(DCC_MR; Engle, 2002). The dark red line (SHIP) shows the Kilian (2009) index of worldwide economic activity. 

The blue line shows the weighted-average proportion of hedge funds trading in U.S. commodity futures markets 

that also trade U.S. equity futures (WCMSA_MMT). A negative relationship between SHIP and DCC is clearly 

apparent as is, after 2003, a positive long-term relationship between DCC and WCMSA_MMT. 

 

‐0.6

‐0.4

‐0.2

‐1E‐15

0.2

0.4

0.6

DCC_MR_SP_GSTR

SHIP

WCMSA_MMT (Cross‐trading hedge funds)

Sept. 8, 2010
41 

Figure 4: TED Spread and Equity-Commodity Correlations, August 2007 to March 2010 

Notes: The green line in Figure 4 shows, between the first Tuesday of July, 2007 and the last Tuesday of February, 

2010, the dynamic conditional correlation between the weekly unlevered rates of return (precisely, changes in log 

prices) on the S&P 500 (SP) equity index and on the S&P GSCI total return (GSTR) index. We use Tuesday-toTuesday return to estimate dynamic conditional correlations by log-likehood for mean-reverting model (DCC_MR; 

Engle, 2002). The blue line shows the the 90-day TED spread, expressed as a percentage of the contemporaneous 

90-day LIBOR (Source: Bloomberg). Figure 4 shows that this TED-based measure of financial market went up an 

order of magnitude in the year leading up to Lehman’s demise, but that equity-commodity correlations did not 

increase sharply until right after Lehman’s demise. 

 

‐0.4

‐0.2

0

0.2

0.4

0.6

0.8

1

DCC_MR_SP_GSTR

TED (% of contemp. LIBOR)

Lehman

August 2007
42 

Table I: Commodity weights

Year

CBOT

wheat

Kansas

wheat Corn Soybeans Coffee Sugar Cocoa Cotton

Lean

hogs

Live

cattle

Feeder

cattle

Heating

oil Crude

Natur

gas 

2000 3.5% 1.3% 4.0% 2.0% 1.3% 2.0% 0.4% 2.2% 3.0% 6.1% 0.0% 7.6% 47.4% 9.6% 2001 4.1% 1.4% 4.4% 2.1% 0.9% 2.2% 0.5% 1.7% 3.1% 6.6% 0.0% 6.8% 47.1% 10.02002 4.5% 1.8% 4.7% 2.4% 0.9% 1.8% 0.7% 1.6% 2.4% 4.9% 0.9% 7.2% 48.4% 8.6% 

2003 4.0% 1.5% 4.1% 2.5% 0.8% 1.6% 0.5% 1.9% 2.2% 4.2% 1.0% 6.9% 48.4% 11.7

2004 3.3% 1.4% 3.6% 2.4% 0.7% 1.3% 0.3% 1.4% 2.1% 3.5% 0.8% 7.7% 51.6% 10.7

2005 2.4% 0.9% 2.3% 1.6% 0.7% 1.3% 0.2% 1.0% 1.8% 2.7% 0.7% 8.6% 55.8% 11.4

2006 2.6% 1.0% 2.5% 1.4% 0.7% 1.7% 0.2% 0.9% 1.5% 2.3% 0.6% 8.2% 56.5% 8.1% 

2007 3.5% 1.2% 3.2% 1.9% 0.7% 1.1% 0.2% 0.9% 1.4% 2.6% 0.6% 5.9% 57.1% 7.4% 

2008 3.8% 0.9% 3.6% 2.2% 0.6% 1.1% 0.2% 0.8% 1.1% 2.2% 0.4% 5.2% 61.8% 6.9% 

2009 4.6% 1.0% 4.6% 3.2% 0.9% 2.0% 0.4% 1.0% 1.9% 3.3% 0.6% 4.3% 55.9% 5.5% 

2010 4.6% 1.0% 4.6% 3.2% 0.9% 2.0% 0.4% 1.0% 1.9% 3.3% 0.6% 4.3% 55.9% 5.5% 

Note: Table I shows the average weights of the 17 GSCI commodities (out of 24 commodities in the index) for which we have trader p

weights used to compute the weighted average measures of trader importance (WMSSi and WMSAi, where i=AS, AD, AM, AP, MMT

weighted average speculative index (SIS and SIA). Excluded are four GSCI commodities (aluminum, lead, nickel and zinc) that accoun

GSCI in 2008 and 2009. The GSCI weight of London Metal Exchange (LME) copper is applied to Nymex copper positions. FinaWTI crude oil is the GSCVI weight of WTI crude, plus the weights of Brent crude, gasoil and RBOB gasoline. 
43 

Table II: Weekly Rates of Return – Summary Statistics 

(%, January 1991 to March 2010) 

Panel A: S&P 500 Equity Index 

 

1991-2010 1992-1997 1997-2003 2003-2010

 Mean 0.124839 0.272260 0.039058 0.049310

 Median 0.292318 0.345607 0.366951 0.247137

 Maximum 12.37463 4.194317 12.37463 7.818525

 Minimum -15.76649 -4.112432 -12.18282 -15.76649

 Std. Dev. 2.348732 1.440671 2.943599 2.371901

 Skewness -0.596507 -0.258227 -0.026150 -1.440103

 Kurtosis 7.920789 3.371816 4.791780 10.70166

 

 Jarque-Bera 1066.091*** 4.42 42.04*** 994.47***

 

 Observations 998 262 314 353

Panel B: S&P GSCI Commodity Index 

 

1991-2010 1992-1997 1997-2003 2003-2010

 Mean 0.060691 0.138182 0.044902 0.028993

 Median 0.188237 0.148651 0.023027 0.416007

 Maximum 14.90087 5.340624 7.479387 14.90087

 Minimum -14.59139 -9.208887 -14.59139 -13.12567

 Std. Dev. 3.023849 1.811528 2.876870 3.870732

 Skewness -0.527095 -0.395102 -0.445674 -0.406732

 Kurtosis 5.668868 5.426632 4.888678 4.058999

 

 Jarque-Bera 342.40*** 71.10*** 57.06*** 26.23***

 

 Observations 998 262 314 353

Notes: Table II provides summary statistics for the unlevered rates of return on the S&P 500 

equity index (excluding dividends; Panel A), as well as on the S&P GSCI commodity index (total 

return; Panel B). In each Panel, the first column uses sample moments computed using weekly rates of 

return (precisely, changes in log prices multiplied by 100) from January 8, 1991 to March 1, 2010. 

The second, third and fourth columns use, respectively, weekly rates of returns for three successive 

sub-periods: May 26, 1992 to May 27, 1997; May 27, 1997 to May 27, 2003; and, May 27, 2003 to 

February 27, 2010. One, two or three stars indicate that normality of the return distribution is rejected 

at, respectively, the 10%, 5% or 1% level of statistical significance. 
44 

Table III.A: Summary Statistics on Macroeconomic and Market Fundamentals, July 2000 to Marc

Dynamic Conditional

Correlations (DCC_MR) Macroeconomic Fundamentals Financial Market Conditions 

SP500 ‐ GSCI MSCI ‐ GSCI SHIP Index ADS Index

INF

(expected

inflation)

LIBOR (%) TED (%) VIX UMD

Mean 0.058803 0.124428 0.128101 ‐0.475016 0.023591 3.058959 0.487749 21.99812 0.003010 Median 0.051580 0.135190 0.156134  ‐0.246892 0.023665 2.715900 0.296456 20.41000 0.090000 Maximum 0.510420 0.602070 0.553002 0.992458 0.033083 6.802500 4.330619 67.64000 4.550000 Minimum ‐0.353440  ‐0.306940  ‐0.524973  ‐3.747359 0.015084 0.248800 0.027512 9.900000  ‐6.560000 Std. Dev. 0.219529 0.224322 0.263191 0.787462 0.003213 1.874571 0.517985 9.744099 1.127080 Skewness 0.186990 0.151839  ‐0.463355  ‐1.789994  ‐0.091070 0.328567 2.951072 1.653419  ‐0.700811 Kurtosis 1.974010 2.284501 2.329421 6.952640 3.066411 1.842329 14.63722 6.761389 8.153885 Jarque‐Bera 25.09252*** 12.71253*** 27.53235*** 598.4182*** 0.790863 37.28642*** 3582.557*** 527.7928*** 600.2571*** Sum 29.69551 62.83637 64.69118 ‐239.8829 11.91341 1544.774 246.3134 11109.05 1.520000 Sum Sq. Dev. 24.28930 25.36143 34.91194 312.5287 0.005201 1771.064 135.2274 47853.53 640.2358 Observations 505 505 505 505 505 505 505 505 505 ADF (Level) ‐1.943171 ‐1.785006 ‐1.928436 ‐3.137666** ‐1.959157 ‐1.414196 ‐2.880949** ‐2.995549** ‐24.261*** ADF (1st Diff) ‐22.8378***  ‐22.9515***  ‐6.6142***  ‐12.2230***  ‐5.7425***  ‐10.9312***  ‐12.8887***  ‐12.3767***  ‐12.6374***  

Note: Dynamic conditional correlation (DCC) are between the Tuesday-to-Tuesday unlevered rates of return (precisely, changes in log 

total return index (GSTR) and either the S&P 500 equity index (SP) or the MSCI World equity index (MXWO). DCC estimated breverting model (Engle, 2002). SHIP is a measure of worldwide economic activity (Kilian, 2009). ADS is a measure of U.S. economic 

and Scotti, 2008). INF measures expected inflation (source: Federal Reserve). LIBOR and TED are the 90-day annualized LIBOR ra

Bloomberg). UMD is the Fama-French momentum factor for U.S. equities. Excess commodity speculation for the three nearest-ter

contract maturities (WSIA) is the weigthed-average Working “T” for the 17 U.S. commodity futurees in the GSCI index (source: calculations); annual weights equal the average of the daily GSCI weights that year (source: Standard & Poor). For the augmented D

stars (*, **, ***) indicate the rejection of non-stationarity at standard levels of statistical significance (10%, 5% and 1%, respectivelyMcKinnon (1991). The momentum series is I(0); the others are I(1); the optimal lag length K is based on the Akaike Information Critefor all statistics: June 26, 2000 to February 26, 2010. 
45 

Table III.B: Summary Statistics on Positions by Trader Types (Short-dated Commodity Futures), July 2000 

Weighted‐average Market Shares in Short‐term Commodity Futures (WMSS) Weighted‐average Market Share of Cross

across All Maturities (WCM

Hedge Funds

(WMSS_MMT)

All Non‐

Commercials

(WMSS_NON)

Swap Dealers

(WMSS_AS)

Non‐

Commercials

+ Swap

Dealers

(WMSS_ANC)

Traditional

Commercials

(WMSS_TCOM)

All traders

(WCMSA_ALL)

Hedge Funds

(WCMSA_MMT)

All Non

Commerc

(WCMSA_N

Mean 0.205009 0.323114 0.200292 0.523406 0.350287 0.366633 0.108932 0.145403 Median 0.219606 0.330037 0.214260 0.547531 0.323999 0.409356 0.115865 0.169236 Maximum 0.331050 0.462969 0.285468 0.726409 0.535883 0.476945 0.186921 0.233494 Minimum 0.069817 0.176206 0.109090 0.309224 0.191607 0.204867 0.028521 0.049580 Std. Dev. 0.067763 0.081353 0.042617 0.119861 0.094142 0.083519 0.045061 0.058244 Skewness ‐0.334458  ‐0.192197  ‐0.294154  ‐0.257259 0.398954  ‐0.495232  ‐0.230657  ‐0.275388  Kurtosis 1.784851 1.671668 1.867022 1.646867 1.866572 1.720597 1.608840 1.492605 Jarque‐Bera 40.48491*** 40.23639*** 34.29254*** 44.09696*** 40.42775*** 55.08477*** 45.20034*** 54.19478*Sum 103.5296 163.1725 101.1477 264.3202 176.8947 185.1498 55.01070 73.42855 Sum Sq. Dev. 2.314283 3.335603 0.915365 7.240772 4.466850 3.515615 1.023347 1.709729 Observations 505 505 505 505 505 505 505 505 ADF Level ‐1.730639 ‐1.624713 ‐1.543089 ‐1.268780 ‐1.430115 ‐0.778171 ‐1.521733 ‐1.099423 ADF First Diff ‐16.2192***  ‐16.5962***  ‐11.5294***  ‐20.40521***  ‐18.5002***  ‐11.9348***  ‐17.1272***  ‐17.5837*

Note: WMSS_MMT, WMSS_NON, WMSS_AS, WMSS_ANC and WMSS_TCOM stand, respectively, for the weighted-averag

open interest in the three nearest-dated futures with non-trivial open interest for 17 commodity futures markets of: hedge funds traders”), non-commercial traders (NON, including MMT), commodity swap dealers (AS, including CIT – commodity index traders), dealers (ANC), and traditional commercial traders (TCOM) (source: CFTC and authors’ computations). The averaging weights 

average of the GSCI weights for those 17 commodities that year and rescaled to account for GSCI commodity markets for which no laavailable (Source: S&P). For three trader types (MMT, AS,NON) as well as all large traders (ALL), the WCMSA variables m

commodity traders who also hold positions in the S&P 500 e-Mini equity futures (“cross-market traders). For the augmented Dickey-F

**, ***) indicate the rejection of non-stationarity at standard levels of statistical significance (10%, 5% and 1%, respectively); critical v

(1991). The optimal lag length is based on the Akaike Information Criterion (AIC). Sample period for all statistics: June 26, 2000 to F
46 

Table III.C: Summary Statistics of Positions by Trader Type (All Maturities), July 2000 to March 

Weighted‐average Market Shares in All Contracts (WMSA) Weighted‐average Market Share of Cross‐Mar

across All Maturities (WCMSA)

Hedge Funds(WMSA_MMT)

All Non‐Commercials

(WMSA_NON)

Swap Dealers

(WMSA_AS)

Non‐Commercials+ SwapDealers

(WMSA_ANC)

TraditionalCommercials(WMSA_TCOM)

All traders

(WCMSA_ALL)

Hedge Funds

(WCMSA_MMT)

All NonCommerci

(WCMSA_N

Mean 0.179422 0.305027 0.251360 0.556387 0.338149 0.366633 0.108932 0.145403 Median 0.203755 0.318948 0.263481 0.594333 0.305290 0.409356 0.115865 0.169236 Maximum 0.299671 0.426953 0.335178 0.743590 0.537561 0.476945 0.186921 0.233494 Minimum 0.056014 0.171564 0.146260 0.327628 0.185196 0.204867 0.028521 0.049580 Std. Dev. 0.074465 0.079310 0.045016 0.120579 0.096908 0.083519 0.045061 0.058244 Skewness ‐0.132589  ‐0.145310  ‐0.522460  ‐0.315453 0.408567  ‐0.495232  ‐0.230657  ‐0.275388  Kurtosis 1.543973 1.614327 2.205411 1.764964 1.966243 1.720597 1.608840 1.492605 Jarque‐Bera 46.08829*** 42.17908*** 36.25959*** 40.47064*** 36.53593*** 55.08477*** 45.20034*** 54.19478*Sum 90.60832 154.0387 126.9370 280.9757 170.7651 185.1498 55.01070 73.42855 Sum Sq. Dev. 2.794682 3.170228 1.021327 7.327757 4.733181 3.515615 1.023347 1.709729 Observations 505 505 505 505 505 505 505 505 ADF Level ‐1.379692 ‐1.440425 ‐1.193593 ‐1.043211 ‐1.135093 ‐0.778171 ‐1.521733 ‐1.099423 ADF First Diff ‐21.94130***  ‐23.08369***  ‐8.5710***  ‐20.19457***  ‐21.98602***  ‐11.9348***  ‐17.1272***  ‐17.5837*

Note: WMSA_MMT, WMSA_NON, WMSA_AS, WMSA_ANC and WMSA_TCOM stand, respectively, for the weighted-ave

futures open interest across all futures contract maturities in 17 commodity markets of: hedge funds (MMT), non-commercial trader

commodity swap dealers (AS, including CIT), non-commercial + swap dealers (ANC), and traditional commercial traders (TCOM) (s

computations). Weights are set each year equal to the average of the GSCI weights for those 17 commodities that year, and resc

commodity markets for which no large trader position data are available (Source: S&P). WCMSA variables are as in Table III.A, PaDickey-Fuller (ADF) tests, stars (*, **, ***) indicate the rejection of non-stationarity at standard levels of statistical signific

respectively). Critical values are from McKinnon (1991). The optimal lag length is based on the Akaike Information Criterion (AIstatistics: June 26, 2000 through February 26, 2010. 
47 

Table III.D: Equity-Commodity Cross-Trading Activity, 2000-2010 

Classifications in Commodity Markets 

Commodity All Cross‐Market Traders Commodity Swap Dealers Hedge Funds 

Count % of all traders Count % of all cross‐traders Count % of all cross‐traders Co

Cocoa 417 10.5% 29 7.0% 218 52.3% 1Coffee 619 15.6% 37 6.0% 298 48.1% 2Copper 679 17.2% 29 4.3% 290 42.7% 2

Corn 786 19.9% 27 3.4% 322 41.0% 2

Cotton 587 14.8% 38 6.5% 314 53.5% 2

Crude Oil 1108 28.0% 63 5.7% 363 32.8% 2

Feeder Cattle 129 3.3% 15 11.6% 66 51.2% 

Gold 1058 26.7% 43 4.1% 366 34.6% 2

Heating Oil 335 8.5% 26 7.8% 170 50.8% 1

Kansas Wheat 251 6.3% 21 8.4% 142 56.6% 1

Lean Hogs 426 10.8% 23 5.4% 229 53.8% 1

Live Cattle 469 11.9% 24 5.1% 242 51.6% 1

Natural Gas 743 18.8% 49 6.6% 300 40.4% 2

Silver 604 15.3% 35 5.8% 249 41.2% 2

Soybeans 742 18.7% 31 4.2% 305 41.1% 2

Sugar 453 11.4% 38 8.4% 230 50.8% 1

CBOT Wheat 704 17.8% 27 3.8% 311 44.2% 2Median 15.0% 5.9% 49.4% 

Notes: For seventeen commodity futures markets, Table III.D provides information on the number and relative importance of the sub

futures traders who also held, at some point in the sample period (July 1, 2000 through February 26, 2010), positions in the S&P50

contract. 
48 

Table IV.A: Simple Correlations, 2000-2010 (Dependent and Explanatory Variables)

DCC_MRSP_GSTR

DCC_MRMXWO

_

GSTR SHIP ADS VIX LIBOR TED INF WSIS UMD WMSS_AS W

M

DCC_MR_SP_GSTR 1.0000DCC_MR_MXWO_GSTR 0.9576*** 1.0000SHIP ‐0.4463*** ‐0.2839*** 1.0000ADS ‐0.3070***  ‐0.3284*** 0.3058*** 1.0000VIX 0.4916*** 0.4464*** ‐0.5028*** ‐0.6855*** 1.0000LIBOR ‐0.1824***  ‐0.1455*** 0.1422*** 0.0464  ‐0.2890*** 1.0000TED 0.0823* 0.2085*** 0.1876*** ‐0.5504*** 0.5031*** 0.2026*** 1.0000INF ‐0.1912***  ‐0.2836***  ‐0.1728*** 0.3263***  ‐0.3869*** 0.6692***

‐0.2580*** 1.0000

WSIS 0.1930*** 0.4073*** 0.5488*** ‐0.2747*** 0.0653 0.0605 0.5525*** ‐0.5043*** 1.0000UMD 0.0030  ‐0.0101  ‐0.0405 0.0869**  ‐0.0168 0.0167  ‐0.1045** 0.0949**  ‐0.0852* 1.0000WMSS_AS 0.0331 0.2245*** 0.5787*** ‐0.0579 ‐0.0668 0.0134 0.4090*** ‐0.4233*** 0.8394*** ‐0.0779* 1.0000WMSS_MMT 0.1045** 0.3240*** 0.6183***  ‐0.1452***  ‐0.0354  ‐0.0235 0.4613***  ‐0.5307*** 0.9511***  ‐0.0694 0.8599*** 1WMSS_TCOM ‐0.0713 ‐0.2883*** ‐0.6143*** 0.1325*** 0.0205 0.0608

‐0.4581*** 0.5489*** ‐0.9472*** 0.0800*

‐0.9274***

‐

WCMSA_ALL 0.0572 0.2756*** 0.6151***  ‐0.0097  ‐0.1429***  ‐0.0085 0.3410***  ‐0.4750*** 0.8860***  ‐0.0745* 0.9263*** 0

Note: Table IV.A shows the simple correlations of the variables in our regression analyses. Stars (*,**,***) highlight correla

significantly different from 0 at, respectively, the 10%, 5% and 1% levels of statistical significance. The dependent variables (DCCfootnote to Figures 1A (SP_GSTR) and 1B (SP_MXWO). The DCC measures are described the footnotes to Table III. Sample periFebruary 26, 2010. 
49 

Table IV.B: Simple Correlations, 2000-2010 (Speculation)

DCC_MR_SP_GSTR WSIS WSIA WMSS_AS WMSS_MMT WCMSA_ALL WCMSA_MM T WCMSA_AS

DCC_MR_SP_GSTR 1.0000WSIS 0.1930*** 1.0000WSIA 0.2166*** 0.9738*** 1.0000WMSS_AS 0.0331 0.8394*** 0.8583*** 1.0000WMSS_MMT 0.1045** 0.9511*** 0.9379*** 0.8599*** 1.0000WCMSA_ALL 0.0572 0.8860*** 0.9025*** 0.9263*** 0.9346*** 1.0000WCMSA_MMT 0.0370 0.8404*** 0.8570*** 0.8305*** 0.9102*** 0.9446*** 1.0000WCMSA_AS 0.0204 0.7749*** 0.7922*** 0.9095*** 0.8134*** 0.9119*** 0.7405*** 1.0000

Note: Table IV.B shows the sample correlations of the left-hand side variable (DCC_MR) and some of the explanatory varia

in our regression analyses. Stars (*,**,***) highlight correlations that are statistically significantly different from 0 respectively, the 10%, 5% and 1% levels of statistical significance. The dependent variable (DCC_MR) is described in footnote to Figure 1A. The DCC measure is described the footnotes to Table III.A. Sample period: June 26, 2000 throFebruary 26, 2010. 
50 

Table V: Market Fundamentals as Long-run Determinants of the GSCI-S&P500 Dynamic Conditional C

Panel A: Treating the Post‐Lehman Period as any other Period

Model 1 Model 2 

1991‐2000 2000‐2010 1991‐2010 1991‐2000 2000‐2010 1991‐2010 1991‐2000 

Constant 0.182915 ** ‐0.0676649 ‐0.0769808 0.183340 ** ‐0.0425855 ‐0.0456055 0.983731 ** 

(0.08855) (0.1154) (0.08335) (0.08916) (0.1139) (0.07643) (0.3937) 

ADS ‐0.0192282 0.136424 ‐0.0784245

(0.09266) (0.1530) (0.06634)

INF ‐0.240932 ** 

(0.1138) 

SHIP ‐0.03187 ‐0.607037 ** ‐0.247640 ‐0.0328020 ‐0.785661 ** ‐0.249104 0.268479 

(0.2955) (0.2892) (0.1946) (0.2987) (0.3811) (0.1790) (0.2753) 

UMD 0.0375003 0.141408 0.0915841 0.0399188 0.126140 0.0924424 0.0274178 

(0.07593) (0.1081) (0.07970) (0.07709) (0.1070) (0.07331) (0.06095) 

TED ‐0.273596 0.500917 ** 0.332428 ** ‐0.264308 0.630212 ** 0.240228 * ‐0.268425 * 

(0.1932) (0.2171) (0.1497) (0.1967) (0.3125) (0.1410) (0.1550) 

Loglikelihood 811.133 855.65 1656.98 811.218 857.236 1658.69 813.068 

Notes: The dependent variable is the time-varying conditional correlation (DCC) between the weekly unlevered rates o

changes in log prices) on the S&P 500 (SP) equity index and the S&P GSCI total return (GSTR) index. Dynamic condestimated by log-likehood for mean reverting model (Engle, 2002). The explanatory variables are described in Table III.A. are from the two step ARDL(p,q) estimation approach of Pesaran and Shin (1999). When estimating the long-run relationshimportant issues is the choice of the order of the distributed lag function on yt and the explanatory variables xt. The Schwarz i

suggests that the optimal lag lengths are p=1 and q=1 in our case. The sample periods for the first, fourth and seventh colu

1991 to June 30, 2000; for the second, fifth and eight columns: July 1, 2000 to February 26, 2010; for the other columns: February 26, 2010. 
51 

Table V: Market Fundamentals as Long-run Determinants of the GSCI-S&P500 Dynamic Conditional C

Panel B: Treating the Post‐Lehman Period unlike previous Years

Model 1 + DUM Model 2 + DUM Model 3 + 

2000‐2010 1991‐2010 2000‐2010 1991‐2010 2000‐2010 1

Constant

‐0.0314680

‐0.0201306

‐0.00925942

‐0.0193913 ‐0.661521 ‐0

(0.06202) (0.04778) (0.05749) (0.04863) (0.4067) (0

ADS 0.153715 * 0.00826134

(0.08115) (0.04729)

INF 0.255100 0

(0.1599) (0

SHIP ‐0.407224 ** ‐0.256007 ** ‐0.596757 *** ‐0.251052 ** ‐0.361970 ** ‐0

(0.1609) (0.1165) (0.1880) (0.1165) (0.1539) (0

UMD 0.0929004 0.0695521 0.0760120 0.0692592 0.0762112 0

(0.05706) (0.04675) (0.05278) (0.04678) (0.05147) (0

TED 0.201796 * 0.110997 0.334082 ** 0.111721 0.211402 ** 0

(0.1061) (0.08428) (0.1368) (0.08770) (0.09905) (0

DUM 0.426993 *** 0.487423 *** 0.485022 *** 0.486330 *** 0.551450 *** 0

(0.1220) (0.1136) (0.1232) (0.1243) (0.1442) (0

Loglikelihood 859.856 1663.9 862.685 1664.68 861.654 

Notes: The dependent variable is the time-varying conditional correlation (DCC) between the weekly unlevered rates of return 

in log prices) on the S&P 500 (SP) equity index and the S&P GSCI total return (GSTR) index. Dynamic conditional correlationlikehood for mean reverting model (Engle, 2002). The explanatory variables are described in Table III.A, except for DUM – 

takes the value DUM=0 prior to September 1, 2008 and DUM=1 afterwards (“Lehman dummy”). Long-run estimates are 

ARDL(p,q) estimation approach of Pesaran and Shin (1999). When estimating the long-run relationship, one of the most impchoice of the order of the distributed lag function on yt and the explanatory variables xt. The Schwarz information criterio

optimal lag lengths are p=1 and q=1 in our case. The sample periods in the first, third and fifth columns are July 1, 2000 to Febsample period for the other columns is January 2, 1991 to June 30, 2000. 
52 

Table VI – Panel A: Speculative Activity as a Long-run Contributor to the GSCI-S&P500 Dynamic Conditio

2000‐2010 2000‐2010 2000‐2010 2000‐2010 2000‐2010 2000‐2010 2

Constant ‐1.08653 *** ‐3.85024 *** ‐3.42068 ‐3.10830 ‐0.582685 ** ‐2.08797 ** ‐3

(0.4039) (1.328) (2.801) (3.180) (0.2820) (0.9959) (1

ADS 0.0900166 0.103863 0.0819206 0.110121 0.121550 0.132858 * 0

(0.1031) (0.09492) (0.1009) (0.09684) (0.07598) (0.07009) (0

SHIP ‐1.04023 *** ‐0.933864 *** ‐1.00618 *** ‐0.939143 *** ‐0.716249 *** ‐0.693805 *** ‐0

(0.3210) (0.2664) (0.3116) (0.3016) (0.2348) (0.1905) (0

UMD 0.0879904 0.0893486 0.0826899 0.0896561 0.0703609 0.0712289 0

(0.07248) (0.06653) (0.07076) (0.06753) (0.05058) (0.04622) (0

TED 2.58964 ** 6.24366 ** 2.44687 ** 6.60861 * 1.70441 ** 4.27775 ** 1

(1.076) (3.120) (1.041) (3.448) (0.6964) (2.102) (0

WMSS_MMT 5.07041 *** 8.63094 ** 2.56186 * 7

(1.789) (4.267) (1.338) (2

WMSS_AS 1.84774 ‐1.68847 1

(4.015) (3.330) (2

WMSS_TCOM 3.54272 ‐0.879623 4

(3.967) (2.595) (2

WSIA 3.07302 *** 2.99394 1.64474 ** 

(1.048) (1.842) (0.8008) 

INT_TED_MMT ‐8.19136 ** ‐7.71792 ** ‐5.22160 ** N.A. ‐4

(3.651) (3.543) (2.409) N.A. (1

INT_TED_WSIA ‐4.37543 * ‐4.64281 * ‐2.96711 * 

(2.261) (2.490) (1.533) 

DUM 0.398321 *** 0.391434 *** 0

(0.1393) (0.1273) (0

Log likelihood 867.929 861.365 868.866 862.06 871.537 865.766 

Notes: The dependent (equity-commodity weekly return DCC) and most of the explanatory variables are described in Tables 

time dummy for the post-Lehman period (Septemer 2008 to March 2010). Long-run estimates are from a 2-step ARDL(p,q) estim1999). The Schwarz information criterion suggests optimal lag lengths p=1 and q=1. Sample period: July 1, 2000 to February 26, 
53 

Table VI, Panel B: Cross-Market Trading as a Long-run Contributor to the GSCI-S&P500 Dynamic Conditi

2000‐2010 2000‐2010 2000‐2010 2000‐2010 2000‐2010 200

Constant ‐0.865047 ** ‐0.506191 ‐3.77118 *** ‐0.414669 0.755510 *** ‐1.8

(0.3862) (0.5007) (1.354) (0.2550) (0.2726) (0.6

ADS 0.121594 0.105277 0.103118 0.138361 0.112309 ** 0.12

(0.1249) (0.1232) (0.09608) (0.08670) (0.05263) (0.0

SHIP ‐0.948281 *** ‐0.898426 *** ‐0.913188 *** ‐0.738715 *** ‐0.484892 *** ‐0.4

(0.3531) (0.3492) (0.2732) (0.2386) (0.1500) (0.1

UMD 0.0948335 0.0914709 0.0954178 0.0689901 0.0335555 0.04

(0.08516) (0.08515) (0.06932) (0.05648) (0.03449) (0.0

TED 2.42418 ** 2.55259 ** 6.00666 * 1.23989 * 0.719877 * 2.67

(1.173) (1.209) (3.148) (0.7334) (0.4138) (1.4

WCMSA_MMT 7.85698 ** 9.43273 ** 3.82713 * 4.83782 ***

(3.248) (4.018) (2.231) (1.491)

WCMSA_AS ‐2.94989 ‐0.256259 ‐7.06668 *** ‐5.1

(3.406) (2.633) (1.756) (1.4

WSIA 3.05038 ** 2.26

(1.210) (0.5

INT_TED_CMMTA ‐15.5298 * ‐16.3698 * N.A. ‐7.24763 ‐3.52202

(8.375) (8.591) N.A. (5.354) (3.072)

INT_TED_WSIA ‐4.19862 * ‐1.8

(2.280) (1.0

DUM 0.356019 ** 0.612165 *** 0.52

(0.1430) (0.1104) (0.0

Log lik. 869.216 869.797 862.067 871.446 877.968 8

Notes: The dependent variable, most of the dependent variables and the methodology are described in Table VI.A. INT_T

INT_TED_WSIA are interaction terms of the TED spread with, respectively, the weekly shares of open interest held by cro

hedge funds (MMT) and swap dealers (AS). Sample period: July 1, 2000 to February 26, 2010. 
54 

Table VII: Long-run Determinants of GSCI-S&P500 Correlations, pre-Lehman

Variable Model 6 

2000-2008

Model 7 

2000-2008

Model 8 

2000-2008

Model 9 

2000-2008

Constant -0.3374 0.3935 -0.6700 2.6124

(1.4730) (1.5120) (2.3950) (2.8380)

SHIP -0.529*** -0.5681*** -0.521*** -0.6084***

(0.1688) (0.1699) (0.1709) (0.1794)

UMD 0.0254 0.0253 0.0219 0.0150

(0.0363) (0.0366) (0.0369) (0.0376)

TED 0.2094*** 1.198*** 0.203*** 1.4042***

(0.0711) (0.4270) (0.0752) (0.5043)

WMSA_AS -2.7322 -4.4055* -2.6185 -5.5734*

(2.5210) (2.6040) (2.6170) (2.9240)

WMSA_MMT 3.2591* 3.9145** 3.0660 5.5919**

(1.8280) (1.8320) (2.3060) (2.5420)

WMSA_TCOM 0.9390 -0.4773 1.0720 -1.4469

(1.9110) (2.0190) (2.0040) (2.3250)

INT_TED_MMTA -4.111** -4.8562**

(1.6870) (1.9450)

WSIA 0.2370 -1.5383

(1.4070) (1.6330)

Observations 437 437 437 437

Notes: Explanatory variables are described in Tables VI.A and VI.B. The dependent variable is the the 

time-varying conditional correlation between the weekly unlevered rates of return (precisely, changes in log 

prices) on the S&P 500 (SP) equity index and the S&P GSCI total return (GSTR) index. Dynamic 

conditional correlations estimated by log-likehood for mean reverting model (Engle, 2002). 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 . Long-run estimates are from the two step ARDL(p,q) 

estimation approach of Pesaran and Shin (1999). The Schwarz information criterion suggests that the optimal 

lag lengths are p=1 and q=1 in our case. The sample period is January 2, 2000 to November 11, 2008. 

 
55 

Table VIII: Long-run Determinants of GSCI-S&P500 Correlations, pre-Lehman

Variable Model 2 

2000-2008

Model 3 

2000-2008

Model 4 

2000-2008

Model 5 

2000-2008

Constant -2.3482 -3.2999* -5.1089** -5.3486**

(1.7890) (1.7390) (2.1470) (2.1140)

SHIP -0.7083*** -0.8846*** -0.6126*** -0.775***

(0.1818) (0.1846) (0.1593) (0.1738)

UMD 0.0369 0.0240 0.0324 0.0204

(0.0441) (0.0410) (0.0373) (0.0364)

TED 0.3232*** 1.7639*** 0.2329*** 1.4261***

(0.0891) (0.5417) (0.0811) (0.4992)

WMSS_AS 0.8208 0.7740 0.9516 0.9502

(2.6290) (2.4640) (2.2190) (2.1810)

WMSS_MMT 5.3721** 9.143*** 5.0791** 8.2813***

(2.6490) (2.9180) (2.2280) (2.5880)

WMSS_TCOM 2.9873 3.4884 4.5071** 4.663**

(2.3960) (2.2690) (2.2210) (2.1890)

INT_TED_MMT -5.924*** -4.8259**

(2.1200) (1.9220)

WSIA 1.803* 1.4199

(0.9239) (0.9360)

Observations 437 437 437 437

Notes: Explanatory variables are described in Tabls 6A-6B. The dependent variable is the the time-varying 

conditional correlation between the weekly unlevered rates of return (precisely, changes in log prices) on the 

S&P 500 (SP) equity index and the S&P GSCI total return (GSTR) index. Dynamic conditional correlations 

estimated by log-likehood for mean reverting model (Engle, 2002). 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 . Long-run estimates are from the two step ARDL(p,q) estimation approach 

of Pesaran and Shin (1999). The Schwarz information criterion suggests that the optimal lag lengths are p=1 

and q=1 in our case. The sample period is January 2, 2000 to November 11, 2008. 
56 

Appendix: Defining Hedge Funds.

“Hedge fund” activity in commodity derivatives markets has been the subject of intense 

scrutiny. Yet, there is no accepted definition of a “hedge fund” in futures markets, and there is 

nothing in the U.S. statutes governing futures trading that defines a hedge fund. 

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.19 

Clearly, many 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. 

 

19 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.