Convective Risk Flows in Commodity Futures Markets 

Ing-Haw Cheng†, Andrei Kirilenko‡, and Wei Xiong§

March 2014 

Abstract 

We study the joint responses of commodity futures prices and positions of various 

trader groups to changes of the VIX before and after the recent financial crisis. 

Financial traders reduced their net long positions during the crisis in response to 

market distress, while hedgers facilitated this by reducing their net short positions 

as prices fell. This convective risk flow induced by the greater distress of financial 

institutions led to a change in the allocation of risk with hedgers holding more risk 

than they did previously. The presence of such a risk flow confirms the market 

impact of financial traders conditional on trades they initiate. 

 

JEL Classification: G12, G01, G20, G14

For helpful comments and encouragement, the authors would like to thank Robin Greenwood, Ken Froot, Joseph 

Gruber, Bob Hodrick, Michael Johannes, Uday Rajan, Ken Singleton, Moto Yogo, and seminar participants at the 2013 

and 2014 American Finance Association Meetings, Columbia University, University of Toronto, 2013 Econometric 

Society Meetings, European Central Bank, the Federal Reserve Board, Fordham University, 2012 NBER Summer 

Institute, and Princeton University for helpful comments and discussions. We thank Matt Baron and Philip Yan for able 

research assistance. Xiong acknowledges financial support from Smith Richardson Foundation grant #2011-8691. 

The research presented in this paper was co-authored by Ing-Haw Cheng worked under CFTC contract (CFOCE11-CO-0235) and former CFTC employee Andrei Kirilenko who wrote this paper in his official capacity. The Office of 

the Chief Economist and CFTC economists produce original research on a broad range of topics relevant to the CFTC’s 

mandate to regulate commodity future markets, commodity options markets, and the expanded mandate to regulate the 

swaps markets pursuant to the Dodd-Frank Wall Street Reform and Consumer Protection Act. These papers are often 

presented at conferences and many of these papers are later published by peer-review and other scholarly outlets. The 

analyses and conclusions expressed in this paper are those of the authors and do not reflect the views of other members 

of the Office of Chief Economist, other Commission staff, or the Commission itself. 

† Tuck School of Business, Dartmouth College, e-mail: ing-haw.cheng@tuck.dartmouth.edu. 

‡

 Sloan School of Management, MIT, e-mail: ak67@mit.edu. 

§

 Department of Economics and Bendheim Center for Finance, Princeton University, e-mail: 

wxiong@princeton.edu. 
1

According to an estimate by a report of the Commodity Futures Trading Commission (CFTC) in 

2008, over $160 billion of investment capital had accumulated into commodity futures markets in 

the U.S. through index investment. This large investment flow, together with episodes of large 

commodity price volatility, has led to a heated debate among the academic and policy circles 

regarding the impact of both index and non-index financial traders on commodity futures prices. 

This debate largely builds on the premise that if financial traders’ trading impacts commodity 

futures prices, their positions should be correlated with and predict futures prices. So far, the 

literature has provided mixed results in testing this hypothesis. On one hand, by using various data 

from the CFTC on futures positions of commodity index traders (CITs), a number of recent studies, 

such as Brunetti and Buyuksahin (2009), Irwin, Sanders and Merrin (2009), Sanders, Irwin and 

Merrin (2010), Stoll and Whaley (2010), Brunetti, Buyuksahin and Harris (2011), Buyuksahin and 

Harris (2011), and Hamilton and Wu (2013), who argue that there is little evidence of CIT positions 

being either correlated with or predictive of futures prices. On the other hand, Singleton (2012) 

provides evidence of positive price impact of CITs on futures prices of crude oil based on CIT 

positions imputed from index weights of different commodities and CIT positions in agricultural 

commodities. 

To resolve this controversy, it is important to fully account for the multiple roles played by 

financial traders in commodity futures markets. The long-standing hedging-pressure theory, which 

was initially proposed by Keynes (1923) and Hicks (1939) and formally developed by Hirshleifer 

(1988, 1990), emphasizes that commodity producers need to short commodity futures to hedge their 

commodity price risk. By taking the other side of commodity producers’ trades, financial traders 

facilitate their hedging needs. On the other hand, financial traders may also have to trade for their 

own reasons such as portfolio diversification and risk management. For example, reduced risk 

appetite due to investment losses elsewhere may cause them to cut down their commodity futures 

positions. 

These two motives for trading may mitigate the ability of empirical tests to pick up evidence of 

a relationship between position changes and futures prices. For example, for financial traders, an 

increase in their futures position which facilitates producers’ hedging needs should be negatively 

correlated with futures prices, while an increase in their futures position for other trading purposes 

might positively impact prices. Empirical tests may pick up zero relationship between financial 
2

traders’ positions and futures prices because they do not condition on the motive for trading. 

To confront this challenge, we take advantage of the lower risk absorption capacity experienced 

by many financial institutions during the recent financial crisis to isolate the trades initiated by 

financial traders in commodity futures markets. Our analysis builds on a growing body of 

theoretical work examining the relationship between financial institutions and asset prices (e.g., 

Shleifer and Vishny 1997; Kyle and Xiong 2001; Gromb and Vayanos 2002; Brunnermeier and 

Pedersen 2009; He and Krishnamurthy 2009; and Danielsson, Shin, and Zigrand 2010). This 

intermediary pricing theory emphasizes that at times, especially during crises, reduced risk appetite 

and binding funding and risk constraints may cause financial traders to unwind positions.1 This 

mechanism also implies that lower risk absorption capacities may cause financial traders to reduce 

their commodity futures positions during the recent crisis. Following the literature, we use changes 

in the VIX to proxy for shocks to financial traders’ risk appetite and funding constraints during the 

crisis.2

 

Our approach relies on the basic idea that the group of traders driving the price at any given 

moment is the group with the most incentive to trade. If financial traders experience lower risk 

absorption capacities due to a larger exposure to changes in the VIX than hedgers during the crisis 

period, the amount of risk transferred from hedgers to financial traders may be reduced as financial 

traders’ risk absorption capacity tightens. This is true even if the VIX also affects hedgers’ 

incentives to hedge, so long as financial traders have a differentially larger exposure. As financial 

traders cut down their long positions due to their smaller risk absorption capacity, the equilibrium 

price falls and hedgers end up holding more risk than they did previously. Much as warm air flows 

towards cool air in a convective current, a portion of the risk which was previously held by financial 

traders will be taken back on by hedgers. We call this phenomenon a convective risk flow. 

Our empirical analysis identifies the existence and direction of a convective risk flow by 

 

1

 Mitchell, Pedersen, and Pulvino (2007), Froot (2008), Adrian and Shin (2009), and He, Khang, and Krishnamurthy 

(2010) provide evidence for capital constraints to affect liquidity and risk premium in various asset markets such as 

convertible bond market, catastrophe reinsurance market, and mortgage-backed security market. 

2

 Shocks to the VIX are widely used to analyze the risk absorption capacity of financial institutions on asset markets. 

For example, Brunnermeier, Nagel, and Pedersen (2009), Coffey, Hrung, and Sarkar (2009), and Longstaff et al. (2011) 

show that VIX shocks can explain price dynamics in several markets that are not directly related to equity during the 

crisis, such as currency crashes, violation of covered interest rate parity, and fluctuations of sovereign bond spreads. 
3

exploiting the joint dynamics of futures price changes and position changes of different trader 

groups. Our analysis proceeds in two steps. First, we examine whether changes in futures prices 

are correlated with changes in the VIX during the crisis period. Second, using a comprehensive 

dataset of positions of market participants, we examine which trader groups’ position changes are 

correlated with changes in the VIX in the same direction. For example, if prices tend to fall as the 

VIX rises in the crisis, we examine which trader groups are selling. This identifies which groups 

transmit the VIX movements into the futures prices and thus who the marginal price setters are 

during this period. If, as the prices fall, financial traders reduce their positions in response to 

increases in the VIX, there is a convective risk flow towards hedgers. 

We obtain account-level data on each trader’s daily positions in commodity futures markets by 

making use of the CFTC's Large Trader Reporting System (LTRS) database.3 Based on each 

trader’s registration with the CFTC and its positions in the LTRS database, we classify the trader as 

a hedger, hedge fund, or CIT. Hedge funds and CITs are the two major groups of financial traders. 

We find that during the recent financial crisis, the aggregated positions of both CITs and hedge 

funds displayed significant and negative position responses to increases in the VIX in a large 

number of commodity futures markets. Hedgers took the other side and displayed a positive 

position response to increases in the VIX, meaning they tended to reduce their net short positions 

just as uncertainty was rising. Averaging the effect across agricultural commodities, a one-standard 

deviation (SD) increase in the VIX is associated with a 0.20-SD decrease in CIT positions, 0.12-SD 

decrease in hedge fund positions, and 0.16-SD increase in hedger positions. The increases in the 

VIX were also accompanied by significant price drops in almost all commodity futures. In contrast, 

prior to the financial crisis, neither financial traders nor hedgers exhibited significant responses to 

the VIX. This contrast in the traders’ responses before and during the crisis is consistent with an 

implication of the intermediary pricing theory that financial traders’ exposures to financial shocks 

are non-linear and are particularly large after they suffer large losses during a crisis. 

The main alternative hypothesis for our results is that the observed price and position 

 

3

 By regulation, when a trader’s position in a commodity futures contract becomes larger than a certain threshold, 

clearing members are obligated to report the trader’s end-of-day positions in the commodity to the CFTC. The 

reportable traders in the LTRS account for 70%-90% of open interest in any given commodity. 
4

correlations are driven by changes to hedgers’ hedging demand rather than the fluctuating risk 

absorption capacities of financial traders. To more directly associate the observed position 

responses to the VIX with the risk absorption of financial traders, we exploit the detailed crosssection of traders within different trader groups. First, by taking advantage of the availability of 

CDS spreads of many CITs, we find more sensitive position responses to the VIX by more 

distressed CITs (i.e., those with larger CDS spreads). Second, we find that, within hedgers, net 

short hedgers reduced their short positions in response to increases in the VIX, while net long 

hedgers did not reduce their long positions or even increased their long positions. This absence of a 

response by long hedgers suggests that changes in the VIX were less related to hedging demand and 

more related to the capacity of financial traders to bear risk. 

Our results are robust to including macroeconomic controls and other indicators of risk in 

commodities, and do not rely on a particular definition of the financial crisis. To show our results 

do not rely on our use of the CFTC’s proprietary LTRS data, we also re-confirm similar convective 

flows in positions of different trader groups covered by the CFTC’s public Commitment of Trader 

(COT) reports, although the public COT reports do not allow for as fine of a disaggregation of 

groups of traders as our analysis. 

Taken together, our empirical evidence shows that, during the recent crisis, in response to a 

spike in the VIX, financial traders reduced their commodity futures positions instead of facilitating 

the hedging needs of hedgers. Our results directly link the changing face of market participants in 

commodity futures markets over the past ten years to changes in commodity futures price dynamics. 

In this sense, our results echo those of Etula (2010), which emphasizes the balance sheet strength of 

securities brokers and dealers as an important determinant of risk premia and return volatility in 

commodity markets, and Acharya, Lochstoer, and Ramadorai (2010), which shows that decreases in 

hedgers’ hedging cost appear to fluctuate with the strength of the financial sector. However, our 

study is distinct in that it focuses on the recent dramatic changes in market participation and thus 

helps identify the effect of these changes, notably the growth of CITs, on commodity futures price 

dynamics.4

 Furthermore, while distressed sales are a phenomenon shown to exist in other asset 

 

4

 Tang and Xiong (2010) argue that the increasing presence of index traders in commodity futures markets improves 

risk sharing at the expense of having volatility spillover from outside markets, but do not directly measure positions of 

market participants. 
5

classes (for example, as in Coval and Stafford, 2007), our paper emphasizes the interaction between 

different trader groups and highlights which traders provide liquidity during sales and purchases 

related to fluctuations in risk absorption capacity. 

Our results also help resolve some specific controversies in the aforementioned debate of CITs 

on commodity markets. First, our results motivate the need to expand the debate about the impact 

of speculators’ trading on commodity prices to one that studies which trader groups have the 

greatest incentive to trade during different time periods. For example, our results help reconcile the 

views of Singleton (2012) and Hamilton and Wu (2013), the latter of which argues that the findings 

of the former are specific to the crisis. Other studies in the aforementioned debate do not condition 

on how shocks affect different traders during specific periods in their analyses. 

Second, our results help directly resolve the controversy over the lack of a contemporaneous 

relationship between futures price changes and CIT position changes, which is often used as key 

evidence against any effect of CIT trading on prices. The final section of the paper shows that 

conditioning on changes in the VIX to isolate trades due to the fluctuation of risk absorption 

capacity of CITs in the crisis reveals consistently positive and significant correlations between CIT 

position changes and price changes across almost all commodities in our sample, although our point 

estimates only provide upper bounds of price impacts. Our analysis thus echoes another recent 

study of Henderson, Pearson, and Wang (2012), who identify price impacts of CITs by conditioning 

on the startup of a set of commodity-linked notes (CLNs, a type of instruments used by CITs) to 

isolate trades initiated by CITs. They find that such trading by CITs has a positive impact on 

commodity prices over a two-day window following the launch of CLNs. Our analysis 

complements theirs by studying the reaction of all market participants and expanding the scope of 

their study to whether such effects related to market microstructure can lead to persistent reallocations of risk over long horizons. 

The paper is organized as follows. Section 1 introduces a theoretical framework and discusses 

our empirical design. Section 2 describes the data and provides summary information on 

participation of different trader groups. Section 3 examines the joint responses of futures prices and 

traders’ positions to VIX changes. Section 4 concludes the paper. We also provide an Online 

Appendix, available online, to report additional data description and empirical results. 
6

1. Theory and Empirical Design

1.1 A theoretical framework

To develop the notion of a convective risk flow, we adopt a setting broadly consistent with the 

hedging pressure theory formulated by Hirshleifer (1988, 1990). Specifically, we consider a futures 

market with 2 groups of participants, hedgers and financial traders. The hedgers represent 

commodity producers and need to short futures to hedge the commodity price risk in their 

commercial business.5

 The financial traders take the other side of the hedgers’ trade, but also face 

their own shocks that motivate them to change their position. 

We consider only one period (possibly out of many periods in a more general model), during 

which random shocks cause the two groups of traders to change their positions. For simplicity, we 

specify the following demand curves for the two groups: 

 ,௛ݑെݖ௛ߛ െ ܨ݀௛ߚെ ൌ௛ ݔ݀

 ,ݖ௙ߛ െ ܨ݀௙ߚെ ൌ௙ ݔ݀

where ݀ݔ ௛and ݀ݔ ௙are changes in the futures position of the hedgers and financial traders across 

the period. ݀ܨ is the futures price change. The coefficients ߚ ൒ ௛0 and ߚ ൒ ௙0 are the slopes of the 

two groups’ demand curves with respect to the price change ݀ܨ. These slopes also represent the 

two groups’ capacities to absorb each other’s trades. 

Note that financial traders’ slope ߚ ௙reflects the number of financial traders who choose to 

participate in this market in the presence of a fixed setup cost (a la Hirshleifer, 1988). A larger ߚ௙

implies that for the same futures price drop, financial traders are able to absorb a larger position 

sold by hedgers. Similarly, ߚ ௛measures how price sensitive the hedgers are. Hedgers will choose a 

smaller hedging position when the futures price drops. 

 

5

 Our setting takes as given several conditions highlighted by Hirshleifer (1990) for the existence of hedging pressure. 

First, different from concentrated commodity price risk faced by producers, consumers face dispersed risk across a 

variety of commodities and do not hedge due to fixed setup costs in participating in each futures market. Second, 

producers face inelastic commodity demand and need to short futures to hedge their risk. 
7

We build in two types of random shocks in the hedgers’ and financial traders’ position changes. 

ݑ ௛is an idiosyncratic shock that causes hedgers to increase their short position in the futures 

contract. We also introduce another shock ݖ, which motivates financial traders to reduce their 

positions. One can think of the ݖ shock as a shock to the VIX during the financial crisis, which 

increased the overall risk of financial traders’ investment portfolios and thus caused them to cut 

down their positions in commodity futures. ߛ ൐ ௙0 measures financial traders’ exposure to the 

shock. For generality, we also allow the ݖ shock to affect hedgers although with a different degree 

ߛ௛. In the case that ߛ ൐ ௙ߛ௛, financial traders have a greater exposure than hedgers to the ݖ shock. 

Market clearing imposes an add-up constraint on ݀ݔ ௛and ݀ݔ௙: 

0. ൌ௙ ݔ݀ ൅ ௛ݔ݀

The equilibrium price acts as the key channel to balance the two groups’ net demand. Simple 

algebra gives that the futures price has to change by 

݀ܨ ൌ െ

1

௙ߚ ൅ ௛ߚ

ൣݑ ൅ ௛൫ߛ ൅ ௛ߛ௙൯ݖ൧,																								ሺ1ሻ

which is accompanied by the following position changes: 

௙ߚ െ ൌ௛ ݔ݀

௙ߚ ൅ ௛ߚ

௛ߛ௙ߚ െ௙ ߛ௛ߚ ൅ ௛ݑ

௙ߚ ൅ ௛ߚ

ݖ,																					ሺ2ሻ	

and 

௙ߚ ൌ௛ ݔ݀െ ൌ௙ ݔ݀

௙ߚ ൅ ௛ߚ

௛ߛ௙ߚ െ௙ ߛ௛ߚ െ௛ ݑ

௙ߚ ൅ ௛ߚ

ݖ.																			ሺ3ሻ

Equation (1) nests the hedging pressure theory in the sense that the presence of financial traders 

dampens the price impact of the hedgers’ idiosyncratic shock ݑ௛. That is, a higher value of ߚ ௙leads 

to a smaller exposure of the futures price to ݑ ௛due to the financial traders’ greater capacity to share 

the hedgers’ shock (e.g., equations (2) and (3)). 

Equations (2) and (3) also highlight the convective risk flow induced by the ݖ shock from the 

financial traders to the hedgers. For illustration, consider the simple case with ߛ ௛ൌ 0 (i.e., the ݖ

shock does not affect the hedgers). In this case, the ݖ shock nevertheless causes the hedgers to 
8

increase their futures position by ఉ೓ఊ೑

ఉ೓ାఉ೑

ݖ. This is because the shock causes the futures price to drop 

by ఊ೑

ఉ೓ାఉ೑

ݖ, which in turn induces the hedgers to buy back their short position. In other words, the 

price drops so much that the hedgers find it desirable to take some risks back. 

More generally, as long as ߚ௛ߛ ௙െ ߚ௙ߛ ൐ ௛0, which is equivalent to ఊ೑

ఉ೑

൐ ఊ೓

ఉ೓

 (i.e., the financial 

traders’ exposure to the ݖ shock after adjusting for their capacity is greater than the hedgers’), the 

hedgers buy back some of their futures position in response to the shock. As a result, a convective 

risk flow, or a change in how much risk is held by different trader groups, emerges. The premise of 

this convective risk flow is that one group of participants (financial traders) has a greater exposure 

to the shock than the other group (hedgers) and that both groups are price sensitive (i.e., both groups 

have elastic demand curves). This convective flow reduces risk sharing by financial traders even 

though they may nevertheless still hold a net long position and share some of hedgers’ risks. Our 

empirical analysis anchors on documenting such a convective flow in the commodity futures 

markets during the recent financial crisis. 

This simple framework also shows a subtle relationship between the futures price change and 

traders’ position changes. In the ongoing debate regarding whether the large inflows of investment 

capital into commodity futures markets affect commodity prices, a commonly used test of the price 

impact of commodity index traders (CITs) is to examine whether their position changes are 

correlated with futures price changes. The premise of the test is that, if CITs’ trading affects futures 

prices, there must be a positive correlation between their position changes and price changes. 

Despite its intuitive appeal, this test ignores that CITs might trade for different reasons. 

It is convenient to interpret the financial traders in our model as CITs and/or hedge funds. 

Equations (1) and (3) show that the hedgers’ shock ݑ ௛and financial traders’ shock ݖ induce 

opposite correlations between the futures price change ݀ܨ and financial traders’ position change 

݀ݔ௙. When financial traders trade to accommodate hedgers’ shock ݑ௛, they share hedgers’ risk and 

their position change is negatively correlated with the price change. On the other hand, when they 

trade in response to their own shock, they demand risk sharing from hedgers and their position 

change is positively correlated with the price change. The unconditional correlation of their 

position change and the price change nets out these two offsetting effects: 
9

ݎݎ݋ܥ൫݀ݔ௙, ݀ܨ൯ ൌ ଵ

ට௏௔௥൫ௗ௫೑൯௏௔௥ሺௗிሻ ൥െ ߚ݂

ቁ݂ߚ൅݄ߚቀ

ቁ݂ߛ൅݄ߛቁቀ݄ߛ݂ߚെ݂ߛ݄ߚቀ ൅ ሻ݄ݑሺݎܸܽ 2

ቁ݂ߚ൅݄ߚቀ

 .ሻ൩ݖሺ	ݎܸܽ 2

The sign of this unconditional correlation is ambiguous and depends on the relative magnitudes 

of the two terms in brackets. The ambiguous sign helps explain the lack of consistent findings in 

the extant literature of significant correlations between the changes of CITs’ positions and futures 

prices.6 At the same time, it also motivates more systematic tests of price impacts of CITs and other 

financial traders after conditioning on trades initiated by them or accommodated by them. Our 

analysis of convective risk flows exactly serves as such a conditional test. 

1.2 Empirical design 

Our empirical analysis focuses on using the change in the VIX as a proxy for the ݖ shock. An 

increased VIX implies greater volatility in financial markets. This may particularly affect financial 

traders as they face both more stringent risk controls as well as potential funding constraints (e.g., 

Gromb and Vayanos 2002 and Brunnermeier and Pedersen 2009). In response, they may have to 

reduce their risk exposures. A common implication of these models (e.g., Kyle and Xiong 2001 and 

He and Krishnamurthy 2009) is that this effect is non-linear and is particularly strong during a crisis 

after financial institutions suffer large losses and are vulnerable to any additional shock to their risk 

appetite and funding constraints. As a result, during the recent crisis, they could have responded to 

increases in the VIX by reducing their commodity exposures, even though they may not have 

responded in the same way before the crisis.7

 

The VIX may also affect hedgers’ incentives to hedge, although the existing literature does not 

provide a clear cut implication on the effect. To the extent that an increase in the VIX implies 

greater economic uncertainty in the economy, the greater economic uncertainty may motivate 

hedgers to hedge more, either due to increased wedge between costs of external and internal sources 

of funding (e.g., Froot, Scharfstein, and Stein, 1993) or greater default risk faced by leveraged firms 

(e.g., Smith and Stultz, 1985). Hirshleifer (1991) provides a two-good model to analyze how a 

 

6

 While our analysis focuses on the contemporaneous relationship between position changes and price changes, the 

same concern also applies to lead-lag relationship between them. 

7

 One caveat, however, is that the VIX may affect both financial institutions’ funding constraints as well as the risk 

appetites of clients (who themselves may be financial institutions), and our analysis does not explicitly differentiate 

between these mechanisms. 
10

farmer’s optimal hedging policy depends on various factors such as demand elasticity and 

sensitivity of his output to weather. He shows that the farmer’s optimal hedging position can be 

time-varying and even reverse in direction during the crop year. On the other hand, Rampini, Sufi 

and Viswanathan (2013) show that commercial hedgers may wish to reduce hedges in time of 

distress due to collateral constraints. To be clear, our framework allows for the VIX to induce 

distress at commercial hedgers. Our analysis is designed to tease out which trader groups are more 

incentivized to trade as a result of VIX fluctuations during the crisis, by identifying whether ఊ೑

ఉ೑

൐ ఊ೓

ఉ೓

. 

We examine how price changes and position changes are correlated with changes in the VIX, 

conditional on a set of controls. We focus on the following set of weekly time-series regressions 

estimated before and after the financial crisis: 

Price correlation: ݀ܨ௧ ൌ ܽ෤൅ܾ෨

ଵݖ௧ ൅ ܾ෨ଶ	ݖ௧ିଵ ൅ ܿ̃	݀ܨ௧ିଵ ൅ ݀ሚ	݋ܥ݊݋ݎݐ݈ݏ௧ ൅ ݑ௧ , (4) 

Position change: ݀ݔ௧ ൌܽ൅ܾଵݖ௧ ൅ ܾଶ	ݖ௧ିଵ ൅ ܿ	݀ܨ௧ିଵ ൅ ݀	݋ܥ݊݋ݎݐ݈ݏ௧ ൅ ݒ௧ , (5) 

where ݖ௧ is the change in the VIX, ݀ܨ௧ is the fully collateralized return to a rolling position in the 

currently indexed futures contract, and ݀ݔ௧ is the position change for a group of traders (e.g., CITs, 

hedge funds, or hedgers). We control for lagged changes in the VIX due to its persistence, and also 

control for one lag of commodity returns to allow for persistence in commodity price movements.8

 

Our other control variables are a series of macroeconomic forecasting variables plus commodityspecific fundamental indicators and are designed to control for fundamental factors that may affect 

prices discussed in Section 2. We focus on the weekly frequency to abstract away from daily 

microstructure-related effects. 

By establishing a price correlation ܾ෨

ଵ of the VIX shock and then examining ܾଵ for different 

trader groups during different sample periods, we identify which traders are trading in the same 

direction as the price correlation with the VIX shock, and thus which way risk is flowing during that 

time period. Notice that ܾ෨

ଵ will have the same sign as ௗி

ௗ௭ from equation (1), ܾଵ for hedgers will 

have the same sign as ௗ௫೓

ௗ௭ from equation (2), and ܾଵ for financial traders will have the same sign as 

 

8

 We focus on changes in the VIX orthogonal to lagged changes in the VIX instead of imputed innovations to the VIX, 

as estimating such innovations requires using the full sample of the VIX. 
11

ௗ௫೑

ௗ௭ from equation (3). Maintaining the assumption that ߚ௛, ߚ ൒ ௙0, if we observe ܾ෨

ଵ ൏ 0, then 

ߛ ൅ ௛ߛ ൐ ௙0, so at least one group of traders has exposure to the VIX. If, at the same time, ܾଵ ൏ 0

for financial traders and ܾଵ ൐ 0 for hedgers, then ఊ೑

ఉ೑

൐ ఊ೓

ఉ೓

. In words, a rising VIX induces financial 

traders to sell and hedgers to accommodate the trade, while the equilibrium price falls. 

Alternatively, if ܾଵ ൐ 0 for financial traders and ܾଵ ൏ 0 for hedgers, a rising VIX induces hedgers 

to short more, with financial traders buying as the equilibrium price falls. Note that, by the market 

clearing condition, ܾଵ for financial traders cannot have the same sign as ܾଵ for hedgers. 

Observing ܾ෨

ଵ ൏ 0, along with ܾଵ ൏ 0 for financial traders and ܾଵ ൐ 0 for hedgers, establishes 

that the VIX induced fluctuations in the risk absorption capacity of financial institutions during the 

crisis to a greater degree than commercial hedgers, which in turn affected prices. An alternative 

hypothesis is that hedgers were intentionally reducing short hedges (i.e., buying) in response to rises 

in the VIX, which would generate the same pattern of ܾଵ coefficients. This may arise, for example, 

if commercial hedgers experienced greater distress than financial traders from rises in the VIX so 

that ఊ೑

ఉ೑

൏ ఊ೓

ఉ೓

. However, this hypothesis is actually not consistent with ܾ෨

ଵ ൏ 0	if equations (4) and (5) 

are well-specified, since prices should rise if hedgers were the marginal price setters and were 

buying in response to rises in the VIX; instead, prices fell during the crisis. One could go on to tell 

a story where ܾ෨

ଵ ൏ 0	 due to an omitted variable negatively correlated with the VIX, where the 

omitted variable is positively correlated with prices, so that the counterfactual price would have 

been even lower had hedgers not been buying. An example of such a variable might be expected 

demand, which presumably fell during the crisis as the VIX rose, and drove prices down. Or, 

hedgers might have certain beliefs that prices would mean revert. 

To address these concerns, we exploit the cross-section of traders in more detail to directly 

establish the role of financial institutions. First, we take advantage of the availability of CDS 

spreads for CIT traders (spreads are not available for hedge funds) to examine whether CITs with 

higher CDS spreads displayed a higher sensitivity of positions to the VIX than those with lower 

CDS spreads. If so, this would provide direct evidence that it was financial institutions under 

distress which were responding to the VIX. 

Second, we also directly examine hedgers. As discussed above, the existing theories do not 
12

provide a sharp prediction on how the VIX affects hedgers’ incentives to hedge commodity price 

risk. Without taking a strong stand on a particular mechanism, we examine whether the VIX 

increased hedging demand by examining whether the hedging demand of both long and short 

hedgers increased in response to the VIX. If the relationship between the VIX and prices is driven 

by hedging behavior, one might expect that it should motivate both long hedgers and short hedgers 

to reduce their hedges. In contrast, the effect coming from financial institutions would be more 

consistent with either a muted response from long hedgers or even long hedgers buying in response 

to rises in the VIX. 

2. The Data and Market Participants 

Our analysis uses the CFTC’s proprietary Large Trader Reporting System (LTRS) database. The 

LTRS data includes disaggregated end-of-day positions for each large trader in all commodity 

futures and options markets subject to the jurisdiction of the CFTC. The LTRS data underlies the 

weekly reports published by the CFTC on aggregate long and short positions of trader groups: the 

Commitments of Traders (COT) report and the Supplemental Report on Commodity Index Traders. 

Our data spans January 1, 2000 to June 1, 2011. We focus on large traders with positions in the 

19 U.S. commodity futures included in the Standard & Poor’s-Goldman Sachs Commodity Index 

(SP-GSCI Index) and the Dow Jones-UBS Commodity Index (DJ-UBSCI). These commodities 

include Chicago wheat, corn, Kansas City wheat, soybeans and soybean oil in grain, feeder cattle, 

lean hogs and live cattle in livestock, cocoa, coffee, cotton and sugar in softs, crude oil, heating oil, 

natural gas and gasoline in energy, and copper, gold and silver in metals. 

The LTRS compiles daily account-level data of traders’ long and short end-of-day positions in 

individual commodity futures contracts, e.g., a Chicago Board of Trade (CBOT) corn futures 

contract expiring in December 2001. Based on the LTRS data, we construct a weekly time series 

from 2000 to 2011 aggregated across contracts but within commodities that matches the timing of 

the Tuesday-to-Tuesday COT reports to best facilitate comparison with public data. We provide 

more details of our data construction in the Online Appendix. 

2.1 Trader classification 

We use specific attributes that identify each trader’s registration, designation or reporting status, 
13

as well as the prior year’s position patterns, to classify the trader in any given year into several 

trader groups, including hedgers, hedge funds, and commodity index traders (CITs), for the 2000-

2011 period. We give a rough outline of our classification below; full details are in the Online 

Appendix. Relative to the COT reports, our classification is conservative in the sense that we aim 

to minimize the effect of traders with ambiguous purposes by moving any trader with ambiguous 

registration into a fourth unclassified group called others. This conservative classification gives a 

more accurate measure of the covariance of each group’s position change with shocks and prices, at 

the expense of under-estimating its net size.9

 

We classify hedgers as traders in the LTRS system with registration, reporting and designation 

codes that clearly indicate commercial use in all the commodities in which they trade. They 

represent farmers, producers, and consumers, who regularly trade commodity futures to hedge 

commodity price risk inherent in their commercial activities. 

We group Commodity Pool Operators (CPOs), Commodity Trading Advisors (CTAs), and 

traders otherwise labeled by the CFTC as “Managed Money” together as hedge funds. These funds 

invest others’ money on a discretionary basis in commodities, commodity futures, and options on 

futures, and make extensive use of leverage. The use of leverage makes funding risk an important 

part of their business, as illustrated in other crisis episodes such as the LTCM crisis in 1998. 

CITs represent portfolio investors who seek index exposure to commodities.10 At a practical 

 

9

 The existing literature on the role of CITs and financial traders has primarily drawn on three data sources in addition to 

the LTRS. The first two are public data sources: the weekly Disaggregated Commitment of Traders report (DCOT), 

and the weekly Supplemental CIT report, and the third is the CFTC’s special call report on CIT positions. The 

Supplemental CIT report contains data specifically measuring CIT positions; however, the only other market participant 

categories are broad commercial and non-commercial categories, rather than more precise categories for hedge funds 

and commercial hedgers. While the DCOT report contains detailed data on hedge fund and commercial hedger 

positions, it does not contain data specifically measuring CIT positions. It does contain aggregated positions for general 

swap dealers – i.e., physical and financial swap dealers together – and many studies have proxied for CIT positions 

using these swap dealer positions. However, this swap dealer classification is likely a noisy the measure of CIT 

positions due to the presence of physical swap dealers. The CFTC’s special call report on CITs is not ideal for our 

analysis either due to its low-frequency and unavailability before December 2007. In the Online Appendix, we provide 

a more detailed review of our classification as well as how it compares to the literature. 

10 A commodity index functions like an equity index, such as the S&P 500, in that its value is derived from the total 

value of a specified basket of commodity futures contracts with specified weights. These contracts are typically nearby 

contracts with delivery times longer than one month. When a first-month contract matures and the second-month 

contract becomes the first-month contract, a commodity index specifies the so-called “roll” (i.e., replacing the current 

contract in the index with a following contract). In this way, commodity indices provide returns comparable to passive 

long positions in listed commodity futures contracts. By far the largest two indices by market share are the SP-GSCI 
14

level, investors often establish commodity index positions by acquiring index swap contracts from 

financial swap dealers, rather than directly taking long positions in individual commodity futures. 

These financial swap dealers then hedge themselves by taking long positions in individual 

commodity futures and report their futures positions to the CFTC. For this reason, many CITs 

classified in our analysis are swap dealers, even though many swap dealers – in particular, physical 

swap dealers – are not CITs. Unlike CPOs and CTAs, CITs are not a registered category with the 

CFTC. We identify CITs based on the CIT classification of the CFTC’s Supplemental CIT report 

and two additional criteria motivated by the trading patterns of broad-based portfolio investors in 

commodity indices: 1) they should be invested in many commodities (have exposure to at least 8 

commodities on average over a year); and 2) they should be mostly net long in those commodities 

over the previous year (the daily average over the previous year of the equal-weight commodity 

average percentage of total contracts held which were long must be greater than 70%). We refresh 

our classification each year based on the previous year’s data. 

2.2 The netting problem 

Although CITs should be theoretically 100% long, this is not true in the data due to a netting 

problem. CITs are large financial institutions which may hold positions both on behalf of clients as 

well as proprietary non-client positions. However, client positions are not broken out from nonclient positions in the LTRS data – the data are “netted” together. Furthermore, the LTRS does not 

cover commodity swaps, as until recently, the CFTC lacked jurisdiction over the swap market. The 

data thus includes only a subset of positions of market participants in the universe of commodity 

derivatives. As a result of these two issues, certain commodities, particularly those where 

derivatives other than futures are very common, are particularly ill-suited for measuring CIT 

positions using the LTRS data. As noted by the CFTC (in the accompanying note to its initial 

Supplemental COT report), this problem is severe in energy and metals, where proprietary and 

client positions might be more mixed due to the size of markets such as gold and oil and where 

over-the-counter derivatives other than futures are common, but less so in agricultural commodities, 

 

and the Dow-Jones UBS Commodity Index (DJ-UBS). These indices differ in terms of index composition, commodity 

selection criteria, rolling mechanism, rebalancing strategy, and weighting scheme. Instead of entering positions on 

individual futures contracts, CITs typically purchase financial instruments that give them exposures to returns of a 

commodity index. There are three types of such instruments: commodity index swaps, exchange-traded funds, and 

exchange-traded notes. 
15

where derivatives other than futures are rarer. 

The netting problem also manifests itself in that some traders may carry multiple designations; 

for example, a trader may be both a CIT and hedge fund according to the above criteria. For the 

bulk of our analysis, we exclude traders with multiple designations in order to best capture the 

covariance properties of their position changes with prices. We discuss the netting problem in more 

detail in the Online Appendix. 

 2.3 Market participation of different trader groups 

Table 1 reports summary statistics by trader types and year. Each summary statistic is a crosssectional statistic (over traders) of a measure that is a daily average over a year. There are relatively 

few CITs, yet their net positions are typically very large, and, by construction, they are invested in 

many commodities.12 Since 2004, both the number of CIT traders and their median net notional 

position have grown significantly, reflecting the rapid rise in index investing. Hedge funds tend to 

have slightly net long exposure. Despite the diversity of hedgers, the average hedger tends to be net 

short in commodity futures. Hedgers are also mostly invested in one or two commodities, consistent 

with the nature of their specific hedging needs.13 The number of hedge funds and commercial 

hedgers have also grown markedly throughout our sample. 

Our annual categorization is persistent. Table 1 shows that the probability of a given account 

having the same categorization in the following year is almost 1 for a hedger, hedge fund, or 

otherwise unclassified trader. For a CIT trader, the probability is 93%. 

Figure 1 plots the aggregate net notional position of each trader category, where positions have 

been aggregated across all 19 commodities, once where we have aggregated using contemporaneous 

 

12 The number of traders is low compared to those reported in the public COT reports. In this sense, our classification 

scheme is conservative and likely an underestimate of the size of the CIT sector. For our purposes, we are more 

interested in the time series properties of changes in true CIT behavior. 

13 As indicated in Table 2, there are a number of other unclassified traders. Although most of these traders are small, 

some of them have a significant net short exposure. These traders might be hedgers who were not registered with, 

reported to or designated by the CFTC as such, or traders who are registered as hedgers in one but not all commodities 

in which they trade. There are also a few traders with both CIT and HF designations. The behavior of these traders, 

however, appears to be fairly similar to CITs. There are very few traders who have designations as both a hedger and a 

hedge fund. To preserve confidentiality, statistics for CIT-HFs and Hedger-HFs are omitted. 
16

prices and once where we have aggregated using fixed prices as of December 15, 2006.14 Although 

our categorizations are meant to conservatively capture the trading pattern of different groups rather 

than the pure level of positions, the plots are useful in describing the pattern of investing through 

time. Evidently, the long side of commodities futures markets has become increasingly dominated 

by CITs and hedge funds, with hedgers and other unclassified traders forming the bulk of the short 

side. 

Figure 2 plots the aggregate net notional positions (using fixed prices) for each of the five 

sectors of commodities, and these observations seem to hold within each sector. It is evident that the 

netting problem is severe in energy and metals, with CITs “appearing” to take even a net short 

position in metals, consistent with our earlier discussion. Metals face the additional issue that many 

positions are taken in the London Metals Exchange, for which there is no position data available. 

Relative to other trader groups, CITs should be passive traders, and this is borne out in the data. 

Table 2 shows that the volatility of flows for CITs is substantially lower than other groups in nearly 

every commodity. Averaged across the 12 agricultural commodities, the volatility of hedge fund 

flows is 2.6 times the volatility of CIT flows. However, although CITs are passive, their positions 

are not constant, as Figures 1 and 2 show sharp decreases in CIT positions during the financial crisis. 

Additionally, contrary to the common perception that hedgers establish hedges and then do not trade, 

hedgers have a high volatility of flows, which is twice as large as that of CITs and 70% as large as 

that of hedge funds. The large amount of trading by each of the trader groups suggests that each 

group is price sensitive and would accommodate trades initiated by another group. 

2.4 Price and other data 

We define excess returns in a commodity as the returns to a position that is always invested in 

the currently indexed contract.15 It accounts for a roll return where the position in the currently 

 

14 Figures 1 and 2 may exhibit jumps in positions on January 1 of each year, as positions are re-shuffled due to the recategorization of traders on an annual basis. In this sense, the change in the aggregate level here is not the same as the 

flow on the first trading day of each year (or any week/time unit that spans multiple years). In subsequent calculations 

involving flows, flows are always computed using a constant sample composition. For example, the flow on the first 

trading day of the year for the CIT grouping is the change in position for the new group. 

15 The currently indexed contract is often, but not necessarily, the front month contract. In particular, the index rolls out 

of the front month contract in the month before expiration, so that after the roll date in that month, the second month 

contract is typically held by the index. Additionally, the index may choose to skip certain contract months due to 

liquidity reasons. For example, in some commodities, the CME introduces contracts for certain expiration months after 
17

indexed contract is liquidated and reinvested in the next indexed contract on a pre-specified 

schedule. We follow the S&P GSCI roll schedule to roll contracts on the fifth business day of each 

contract month.16 Tracking the indexed contract ensures that our generic contract is always liquid. 

Our baseline analysis controls for the one-week percentage change in the Baltic Dry Index 

(BDI), change in break-even inflation compensation (Gürkaynak, Sack, and Wright 2010), and 

change in the Baa credit spread. The BDI tracks worldwide international dry cargo shipping rates 

and is a measure of global demand for commodities (Kilian 2009); higher values represent higher 

shipping rates and greater expected demand. Higher Baa credit spreads indicate worsening credit 

conditions in the economy, and higher inflation compensation generally indicates higher inflation 

expectations. We also include the 12-month percentage change in expected world demand, US 

production, and US stocks for the harvest year for wheat, corn, soybeans, soybean oil, and cotton, 

hand-collected from the monthly U.S. Department of Agriculture (USDA) World Agricultural 

Supply and Demand Estimates. 

3. Empirical Results 

3.1 Commodity returns and the VIX 

Table 3 reports the estimated ܾ෨

ଵ coefficients from equation (4), a linear regression where the 

left-hand side variable is the weekly commodity futures return and the right-hand side variables are 

weekly VIX changes (contemporaneous and lagged), the lagged futures return, plus our baseline 

control variables. Unless otherwise noted, we use the Newey and West (1987) construction for the 

covariance matrix with four lags throughout. 

 

contracts for other expiration months. An example is October gold, which is introduced 24 months before expiration, 

while December gold is introduced 72 months before expiration. This results in certain front-month contracts having 

lower liquidity than others. 

16 The S&P GSCI rolls smoothly over the fifth through ninth business days; for simplicity we switch contracts on the 

fifth day. The monthly roll schedule for the GSCI is provided in the Online Appendix. We define the fifth business day 

as the fifth trading day of the month, where a trading day is a day in which all 19 commodities have positions. We use 

the S&P GSCI schedule for most commodities, except for soybean oil and copper we use the DJ-UBSCI roll schedule. 

This is because S&P GSCI does not include soybean oil and tracks copper contracts traded in London (for which we 

have no data) rather than the CME. Mou (2011) finds price pressure associated CITs rolling during the GSCI roll 

period. Our results are similar under alternative rolling schedules, such as one which rolls later during the month closer 

to when contracts expire. Singleton (2012) finds that rolling according to later schedules increases noise significantly 

due to the lower liquidity of those contracts. 
18

The results indicate a strong correlation of VIX changes with commodity futures returns in the 

142 weeks of what we define as the post-crisis period (September 15, 2008 to June 1, 2011). The 

first column reports the coefficient of a one-standard deviation (SD) contemporaneous change in the 

VIX during this period. With the exception of lean hogs and gold, all commodities display a 

negative price relationship with the VIX, with almost all coefficients statistically significant at the 5% 

level. On average, a one-SD change in the VIX during this period (432 basis points) was associated 

with a 1.5% drop in commodity prices. The second column shows that this negative correlation 

persisted over the period January 1, 2010 to June 1, 2011, more than a year after the collapse of 

Lehman Brothers, although the magnitudes are smaller. 

This relationship does not hold during the pre-crisis period. The third column of Table 3 reports 

the estimated ܾ෨

ଵ for the period January 1, 2006 to September 15, 2008, a pre-crisis period of nearly 

equal length with our post-crisis period. The coefficients during this period are mostly insignificant 

with the exception of cocoa, coffee, and copper having negative coefficients statistically significant 

at the 10% level. The fourth column goes back even further and analyzes the period January 1, 2001 

to January 1, 2006 and similarly finds little systematic relationship between VIX changes and 

commodity returns.17

3.2 Trader positions and the VIX 

We estimate the effect of changes in the VIX on the changes of aggregate positions of different 

groups of traders in equation (5). We focus on the aggregate positions of different trader groups as 

we are interested in identifying which groups have been driving the price, and do not want small 

individual traders who may behave in a nonsystematic way to change our analysis. As discussed 

above, the netting problem in the LTRS position data is particularly severe for commodities in 

energy and metals. Below, we report results only for agricultural commodities (grains, livestock, 

and softs). 

 

17 For brevity, we report coefficients for our control variables in the Online Appendix. The contemporaneous percentchange in the BDI index generally shows a positive relationship to commodity returns, with statistical significance in 

five commodities. Contemporaneous increases in breakeven inflation are generally positively related to commodity 

returns, with statistical significance in four commodities. The change in the Baa spread is negatively related to futures 

price change in most commodities (statistically significant in 3 commodities). For wheat, corn, soybeans, soybean oil, 

and cotton, we also include the 12-month percentage change in expected world demand, US production, and US stocks, 

which do not show consistent patterns with price changes, likely due to these variables being equilibrium quantities 

which are correlated. 
19

Table 4 reports the estimated ܾଵ coefficients from equation (5). Panel A reports the estimated ܾଵ

coefficients during the post-crisis September 15, 2008 to June 1, 2011 period, while Panel B reports 

the estimated ܾଵ coefficients during the period immediately before the crisis, January 1, 2006 to 

September 15, 2008. The results in Panel A strongly indicate asymmetric responses of market 

participants to VIX changes in the post-crisis period. When the VIX increases, both CITs and hedge 

funds tend to reduce their net long exposures, while hedgers and other unclassified traders tend to 

buy, in virtually all commodities. For CITs, the association between position changes and 

contemporaneous VIX changes is negative and statistically significant at the 10% level or better in 8 

of the 12 agricultural commodities, with an average economic significance of -0.20-standard 

deviations. Hedge funds display a negative and statistically significant relationship (at the 10% level 

or better) between position changes and contemporaneous VIX changes in 7 of the 12 commodities, 

with an average economic significance of -0.12-standard deviations; only one commodity has a 

positive point estimate. 

In contrast, hedgers tend to display a positive relationship between VIX changes and position 

changes. The relationship is positive and statistically significant for 9 of the 12 commodities, with 

only one negative point estimate. The average economic significance among all 12 commodities is 

+0.16-standard deviations. The other unclassified traders are similar to hedgers: the relationship is 

negative and statistically significant for 8 of the 12 commodities, with an average economic 

significance of +0.15 standard deviations.18

Panel B of Table 4 shows little evidence of traders’ positions correlating with the VIX during 

the pre-crisis period from January 1, 2006 to September 15, 2008, a period nearly equal in length to 

our post-crisis period, consistent with the lack of price correlation before the crisis. In sharp contrast 

to the post-crisis period, across all of the trader groups and all 12 commodities, the coefficients of 

the contemporaneous VIX changes in the pre-crisis period are virtually insignificant with the 

exception of one or two commodities for each group. R-squareds during this period are relatively 

low for the CIT group during this pre-crisis period relative to other groups, both because funding 

constraints had yet to tightly bind and because of their general passiveness in trading before the 

 

18 Our baseline control variables show mixed relationships with changes in positions for the various trader groups. For 

example, the percentage change in the BDI exhibits only weak statistical correlation with the position changes of each 

group, as does the change in breakeven inflation and Baa credit spread. 
20

crisis. Panel C of Table 4 reports results for January 1, 2001 through January 1, 2006 and similarly 

finds little relationship. 

3.3 Cross-sectional evidence 

In this subsection, we exploit individual traders within each group to provide additional 

evidence supporting the presence of convective risk flows during the crisis. 

3.3.1 Evidence on distressed financials. We test whether distressed financials are driving the 

negative sensitivity of CIT positions to the VIX by identifying CIT traders with high CDS spreads 

and examining whether they are more negatively sensitive to the VIX than CIT traders with lower 

CDS spreads. The theory implies that CIT traders with high CDS spreads should have a higher 

position change-sensitivity to the VIX changes through two possible channels. First, the CITs (large 

financial institutions) may need to sell their own proprietary positions when volatility rises in order 

to control risk exposures. Second, investors who entered into swap contracts with a CIT may 

potentially withdraw their investment when the institution is distressed. 

We manually match large traders identified as CITs to the names of their respective firms and 

collected their CDS spreads from Bloomberg. For each week, we split the group of CIT accounts 

into accounts with high CDS spreads (above the median) and low CDS spreads (below the median). 

We regress the account-level position change as the left-hand side variable on the change in the 

VIX, an indicator for whether the trader has a high CDS spread, and the interaction of these two 

terms, including our baseline controls and the lagged logarithm of absolute notional position size in 

the commodity. This regression exploits the relative ranking of firms with high and low CDS 

spreads. Table 5 reports the results from this regression. Consistent with the intermediary pricing 

theory, high CDS spread firms sell more, and, furthermore, are more sensitive to changes in the 

VIX in 5 of the 12 commodities.20 In unreported analysis, a regression that substitutes the lagged 

CDS ranking yields nearly identical results. 

3.3.2 Evidence on long vs short hedgers. To further isolate the intermediary pricing channel, 

we address the possibility that observed reactions of traders to the VIX is driven by hedgers’ 

 

20 We cluster standard errors at the weekly level because position changes across traders may be correlated within a 

week given aggregate shocks. Clustering standard errors at the account-level generates nearly identical results, which 

are available from the authors. Including our extended set of controls also yields nearly identical results. 
21

hedging demand by exploiting the fact that some hedgers are long and some hedgers are short. 

While hedgers’ positions are typically net short, there is a subset of hedgers taking net long 

positions. These net long hedgers are clustered in the lower right corner of each plot. If the position 

responses to the VIX observed during the crisis were driven by increased hedging demand, we 

should expect long hedgers to increase their long positions and short hedgers to increase their short 

positions during this period. 

To explore this consideration, we classify a hedger as a “long hedger” in a commodity if the 

hedger maintained an average net long position in the previous calendar year. Specifically, for each 

day, we compute the fraction of long contracts in which a hedger is invested (first within 

commodities, and then as an equal-weight average over commodities in which there is a position), 

and compute the time average over the year. If the average fraction is greater than 50%, we classify 

the hedger as long, while a fraction less than 50% corresponds to short. We then separately regress 

the aggregate position change of long hedgers on changes in the VIX, including both 

contemporaneous and lagged changes, together with our baseline controls. Table 6 reports the 

results and shows that, consistent with Table 4, short hedgers drive the positive relationship 

between hedgers’ position changes and changes in the VIX. However, there is no clear pattern of 

long hedgers reducing positions. For example, it appears that although long hedgers were selling in 

sugar, they were buying in Chicago wheat and coffee. That is, long hedgers of coffee and Chicago 

wheat were trading in the same direction as short hedgers, which is inconsistent with the hypothesis 

that traders’ reactions to the VIX during the crisis were driven by changes in hedging demand. 21

3.4 Robustness 

We first check whether our results can be replicated using the public Commitment of Traders 

reports. Table 7 reports estimated ܾଵ coefficients for equation (5) with position changes calculated 

for the trader groups from the public Disaggregated COT (DCOT) reports, as well as the 

 

21 In the Online Appendix, we also provide extensive results on how hedgers adjust their cash positions (physical stocks, 

and purchase and sales commitments) in response to changes in the VIX. We make use of a CFTC dataset on the cash 

positions of bona fide hedgers in a subset of agricultural commodities subject to federal position limits. We find that socalled bona fide hedgers reduced their net short positions in commodity futures in response to changes in the VIX as 

well as their long positions in cash commodities. Large traders who have been granted bona fide hedger designations 

by the CFTC are typically agribusiness middlemen rather than farmers. Thus, the fact that they reduced their long 

position in cash agricultural commodities in response to an increase in the VIX suggests that they reduced purchase 

commitments to farmers, which, in turn, led risk to flow towards the ultimate producers of these commodities. 
22

Supplemental CIT report. The results from the DCOT classification are consistent with those in 

Table 4, and show that producers’ positions react positively to the VIX and that managed money 

traders react negatively to the VIX. Although CITs are not separately classified within the DCOT 

reports, much of the CIT business is conducted through swaps. Consistent with this, swap dealers in 

the DCOT reports react qualitatively negatively to the VIX. However, the statistical significance is 

limited. This suggests that swap dealer positions in the DCOT are a noisy signal of CIT positions, 

since while many CITs are swap dealers, many swap dealers such as physical swap dealers are not 

CITs. 

Table 7 also examines how position changes of CITs from the Supplemental CIT report respond 

to changes in the VIX, and confirm our earlier result that CITs react negatively. Overall, the 

analysis of the public data echoes the results from our proprietary LTRS data. However, the nature 

of the aggregation implies that our groups are not jointly represented in any single public report. 

Second, we test whether our results using the implied volatility of at-the-money options on the 

Financial Select Sector SPDR exchange-traded fund (ETF) rather than the VIX as the key righthand side variable.22 This ETF (ticker: XLF) tracks the S&P Financial Select Sector Index, which 

itself tracks S&P 500 finance stocks as defined using their GICS sector code. We obtain at-themoney-forward, constant 91-days-to-expiration implied volatilities from the volatility surface 

provided by OptionMetrics and average over puts and calls. The results, reported in Table 8, show 

that position changes of CITs are sensitive to this implied volatility, consistent with results in Table 

4. The statistical relationship of hedge fund positions with the ETF implied volatility is weaker than 

the relationship with the VIX which suggests that our ETF implied volatility is doing well in 

picking up the risk of large financial institutions. 

We report the next five robustness checks in our Online Appendix for brevity but briefly discuss 

them here. Our third test checks whether our results are robust to an alternative classification 

scheme. We expand our analysis to a cutoff of 80% and 12 commodities. Doing so reduces the 

number of CIT traders over our baseline sample by roughly 20%, and we obtain very similar results. 

 

22 Due to a change of status in CFTC data availability, we are unable to re-run our account-level analyses (Table 5) 

using XLF implied volatilities. We chose to keep the VIX for the main results of the paper and run our tests using the 

XLF implied volatility as a robustness check to maintain consistency throughout the main results. 
23

Our fourth test checks whether our results are robust to alternative factors that may have been 

influencing commodity prices during the crisis period. We add an extended series of controls to 

equations (4) and (5): the one-week return to the Shanghai A-share stock index (to capture the effect 

of demand from China on commodity prices), the lagged commodity basis (to further capture any 

other time-varying risk premia, storage costs, and convenience yields), a set of month dummies (to 

control for seasonality), the change in the 1-year minus 3-month term spread and the change in the 

3-month interest rate (to capture other information about changes in the macroeconomy). We 

obtain these data from Bloomberg and the Federal Reserve Board website. 

In estimating equation (4), the return to the Shanghai A-share stock index is positively related to 

futures price changes for many commodities across all sectors. The lagged basis, changes in 

interest rates and term spread, as well as monthly dummies add some explanatory power for futures 

price changes, but the signs are different across commodities. Overall, adding these controls 

increases the average R-squared across all commodities from 22.7% to 33.9% yet does not change 

our conclusion that ܾ෨

ଵ ൏ 0 across many commodities in the post-crisis period. 

In estimating equation (5), the 1-week lagged basis tends to be positively associated with 

changes in CIT positions. Hedge fund position changes load positively on returns to the Shanghai 

A-share stock index, while hedger position changes load negatively. Overall, including our 

extended controls raises the average R-squared for CITs, hedge funds, hedgers, and unclassified 

traders from 13.7% (averaged across commodities and groups) to 25.7% but does not change our 

overall results. 

Fifth, we show that our results are not dependent on a specific time window for the financial 

crisis. In the above analysis, we take the collapse of Lehman Brothers as a convenient starting point, 

but our results are very similar if we take either the starting point as mid-March 2008, immediately 

prior to the collapse of Bear Stearns, or August 2007, the period in which serious signs of strain 

began showing in the broader financial sector with significant increases in the rates of asset-backed 

commercial paper, or even June 2007. We also show that our results are driven more by the 

immediate post-crisis period of September 15, 2008 through January 1, 2010, rather than the 2010 

period onward, although the sample size is significantly smaller. 

Sixth, we test whether changes in risk allocation were persistent or transitory. This relates to an 
24

alternative hypothesis that that financial traders were exploiting an informational advantage over 

hedgers by reacting more quickly to information about deteriorating fundamentals contained in a 

rising VIX during the crisis. We compute impulse response functions from a vector auto-regression 

of positions on lagged position changes and changes in the VIX, and we find that position changes 

of CITs in response to the VIX are largely persistent over a thirteen-week horizon. We also 

examine the trading patterns of sophisticated hedgers who trade actively and find that if anything 

they were reducing short positions in response to VIX changes, inconsistent with the informational 

advantage hypothesis. 

Finally, we also examine the behavior of cash commitments of commercial hedgers and find 

that they reduced their cash positions as well as futures positions, which highlights that the reallocation of risk affected choices in the real economy. 

3.5 Conditioning on the VIX to infer price and position correlations 

As noted in the introduction, the ongoing debate on the effects of CITs on commodity markets is 

concerned by the lack of a contemporaneous relationship between commodity futures returns and 

CIT position changes (Stoll and Whaley, 2010). This finding is often used as evidence against any 

effect of CITs on commodity markets. In this subsection, we re-examine this relationship by 

conditioning on the VIX during the crisis period. 

Table 9 reproduces contemporaneous correlations between commodity futures returns and 

position changes of different trader groups. We estimate the following equation using OLS: 

݀ܨ௧ ൌ ܽത൅ܾത	ܨ݈ݏݓ݋௧ ൅ ܿ̅	݀ܨ௧ିଵ ൅ ݀̅	݋ܥ݊݋ݎݐ݈ݏ௧ ൅ ݁௧, (6) 

where 	ܨ݈ݏݓ݋௧ is the position change of a given trader group for that commodity, and the controls 

are the same as in Tables 3 and 4. We report the estimated ܾത coefficients for various trader groups. 

Consistent with Stoll and Whaley (2010), there are only weak correlations between CIT position 

changes and contemporaneous commodity futures returns. Hedge funds display a strong positive 

correlation while commercial hedgers display a negative correlation, which is consistent with the 

finding of Buyuksahin and Robe (2010) linking hedge fund trading to commodity futures returns. 

For commodities in energy and metals, CIT position changes are even negatively correlated 

with returns. As we discussed before, this might be due to the netting problem in the measurement 
25

of CIT positions. Singleton (2012) and Hamilton and Wu (2013) use CIT positions in agricultural 

commodities as an alternative to infer CIT positions in crude oil, as CIT positions in individual 

commodities should all reflect investors’ portfolio allocations to the commodity class. This 

approach is appealing because, as we discussed before, the twelve agricultural commodities are less 

exposed to the netting problem. Motivated by this consideration, we use the aggregated CIT 

position changes in the twelve agricultural commodities as a measure of CIT flows in all 

commodities including those in energy and metals. To the extent that aggregation averages out 

noise, aggregate CIT flows may contain more information about CIT flows than flows measured for 

individual commodities.23

Table 9 (the last major column) re-produces estimates of ܾത by substituting aggregated CIT 

position changes in the twelve agricultural commodities, 	ܨ݈ݏݓ݋஺ீ,௧, in place of own-commodity 

CIT flows 	ܨ݈ݏݓ݋௧ . Based on this aggregate measure of CIT flows, the negative CIT position 

correlations with prices in energy and metals disappear with several of the correlations turning 

positive. This contrast confirms the appeal of using aggregated CIT flows in agricultural 

commodities to measure CIT position changes in individual commodities. Nevertheless, across the 

board, the correlations between the aggregated CIT flows and individual commodity returns are still 

weak, with only seven out of the nineteen commodities displaying marginally significant and 

positive correlations. 

Our theoretical framework in Section 1 highlights the need to differentiate whether financial 

traders initiate the trades or trade to accommodate other traders in order to properly identify any 

relationship between their position changes and price changes. Failing to differentiate these two 

cases introduces a simultaneity bias since position changes stemming from trades initiated by 

financial traders should be positively correlated with price changes while those stemming from 

accommodating other traders should be negatively correlated with price changes. In other words, 

equation (6) is an endogenous regression. 

 

23 Singleton (2012) uses the Masters (2008) imputation method, which draws on information from agricultural 

commodities that are only in one of the GSCI or DJ-UBS indices, to impute CIT oil positions. Hamilton and Wu (2013) 

expand this to use a linear combination of all 12 agricultural commodities with a weighting determined by maximizing 

the in-sample fit of observed CIT positions from the Supplemental CIT report. We follow the spirit of this exercise but 

choose a simple weighting based on market prices determined as of December 2006, which avoids contaminating our 

exercise with information from the sample. 
26

Motivated by our previous analysis, we condition on VIX changes to analyze the correlations 

between CIT position changes and price changes. Table 10 reports results from a two-stage analysis. 

First, we extract the component of the aggregate CIT flows in agricultural commodities predicted by 

changes in the VIX, ܨ݈ݏݓ݋஺ ෣ ீ,௧, using a first-stage regression analogous to equation (5): 

ܨ݈ݏݓ݋஺ீ,௧ ൌ ܽ ൅ ܾ	Δܸܫܺ௧ ൅ ܿ	݀ܨ௧ିଵ ൅ ݀	݋ܥ݊݋ݎݐ݈ݏ௧ ൅ ߳௧. (5A) 

Then, we estimate ܾത for CITs by substituting 	ܨ݈ݏݓ݋஺ீ,௧ in equation (6) with its component 

predicted by the VIX changes ܨ݈ݏݓ݋஺ ෣ ீ,௧ and by excluding Δܸܫܺ௧ from equation (6). 24

Econometrically, this is a classic two-stage least squares regression where equation (5A) is the first 

stage regression, equation (6) is the endogenous second-stage regression, ܨ݈ݏݓ݋஺ீ,௧ is the 

endogenous regressor, and Δܸܫܺ௧ is the excluded instrument. We compute two-stage least squares 

standard errors with an adjustment for serial correlation applying the Newey-West (1987) 

construction of the covariance matrix with four lags. Economically, this procedure looks for a 

correlation between price changes and the portion of CIT position changes related to the VIXinduced fluctuations in risk tolerance of CIT traders during the crisis. 

Table 10 reports the estimated ܾത from the second stage as well as tests of the null hypothesis 

that ܾൌ0 from the first stage. The first column reports results for CIT position changes measured 

by the aggregate CIT flows in the twelve agricultural commodities. Conditioning on the VIX yields 

economically and statistically significant correlations between CIT position changes and price 

changes in the second stage across fifteen out of all nineteen commodities. Even for the remaining 

four commodities, the correlations are also positive albeit not significant. Across all commodities, 

the magnitude of the estimated ܾത is also much larger relative to the corresponding value reported in 

the last column of Table 9 without conditioning on the VIX. 

Econometrically, note that the correlation between the VIX changes and CIT position changes 

in the first stage is modest, with the F-statistics reported in Table 10 varying between 6 and 10 and a 

partial R-squared (unreported) across all commodities averaging to 0.1. An insufficiently strong 

correlation in the first stage may lead to significant size distortions in second stage hypothesis tests 

(Stock, Wright and Yogo, 2002) as well as to inconsistent and significantly more finite-sample 

 

24 We subsume the lagged VIX change, Δܸܫܺ௧ିଵ, in the controls for both stages. 
27

biased estimates of ܾത (Bound, Jaeger and Baker, 1995). To alleviate this weak instrument problem 

in the first stage, we further aggregate position changes of both CITs and hedge funds in the twelve 

agricultural commodities to obtain a measure of aggregate flows of all financial traders. As shown 

earlier, hedge fund positions also moved significantly with the VIX during the crisis. The second 

column of Table 9 shows that the aggregate flows of financial traders display much stronger 

correlations with the VIX in the first stage with the F-statistics in the 25-30 range. Importantly, the 

magnitudes of the correlations between aggregate flows of financial traders and price changes 

remain largely unchanged relative to the correlations of the CIT flows and price changes. This 

pattern suggests that the positive correlations between CIT flows and price changes are not 

spuriously induced by the weak instrument problem. 

Economically, we should not over-interpret these correlations as price impacts of CITs. Changes 

in the VIX might have affected not only CITs but also hedgers, violating the exclusion restriction 

that would be necessary to interpret these as true estimates of price impacts. Nevertheless, one can 

view these correlations as upper bounds. At a minimum, Table 10 cautions against using 

unconditional correlations between CIT position changes and price changes to infer market impacts 

of CIT trading, as conditioning on the VIX reveals much greater correlations than previously 

documented. 

4 Conclusion 

Financial traders sold positions in response to rises in the VIX as prices fell during the recent 

financial crisis, with hedgers taking the other side. This evidence suggests that there was a flow of 

risk away from financial traders back towards hedgers. Much as warm air flows towards cool air, 

this convective risk flow reallocates risk from the groups less able to bear the risk to the groups 

more able to bear risk. Analyzing such a risk flow confirms the market impact of CIT traders 

conditional on trades initiated by them, and motivates future research in extending the long-standing 

hedging pressure theory of commodity futures markets to incorporate time-varying risk capacities 

of financial traders. 
28

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31

Figure 1: Net Commodity Exposures 

This figure plots the daily net notional value of positions held by the different trader groups. Panel A computes notional values using 

contemporaneous nearby prices adjusted for inflation (CPI All-Items, Urban Consumers, non-seasonally adjusted) to real December 2006 

prices. Panel B computes notional values using fixed nearby prices as of December 15, 2006. 

Panel A 

Panel B 

−100000 −50000 0 50000 100000

Notional $ (M)

Jan01

Jul01

Jan02

Jul02

Jan03

Jul03

Jan04

Jul04

Jan05

Jul05

Jan06

Jul06

Jan07

Jul07

Jan08

Jul08

Jan09

Jul09

Jan10

Jul10

Jan11

Jun11

CITs

Hedge Funds

Hedgers

Hedger−HF

CIT−HF

Others

Exposure defined as Net Position(t) x Front Month Price(t), Real Dec2006 $

Net Commodity Exposure

−100000 −50000 0 50000 100000

Notional $ (M)

Jan01

Jul01

Jan02

Jul02

Jan03

Jul03

Jan04

Jul04

Jan05

Jul05

Jan06

Jul06

Jan07

Jul07

Jan08

Jul08

Jan09

Jul09

Jan10

Jul10

Jan11

Jun11

CITs

Hedge Funds

Hedgers

Hedger−HFs

CIT−HFs

Others

Exposure defined as Net Position(t) x Front Month Price(15Dec2006)

Net Commodity Exposure
32

Figure 2: Net Commodity Exposure by Sector 

This figure plots the daily net notional value of positions held by the different trader groups across each commodity sector. Notional values were prices as of December 15, 2006. 

−30000 −20000 −100000 10000 20000

Notional $ (M)

Jan01 Jul01 Jan02 Jul02 Jan03 Jul03 Jan04 Jul04 Jan05 Jul05 Jan06 Jul06 Jan07 Jul07 Jan08 Jul08 Jan09 Jul09 Jan10 Jul10 Jan11 Jun11

CITsHedge FundsHedgersHedger−HFsCIT−HFsOthers

Grains

−50000 5000 10000

Notional $ (M)

Jan01 Jul01 Jan02 Jul02 Jan03 Jul03 Jan04 Jul04 Jan05 Jul05 Jan06 Jul06 Jan07 Jul07 Jan08 Jul08 Jan09 Jul09 Jan10 Jul10 Jan11 Jun11

CITsHedge FundsHedgersHedger−HFsCIT−HFsOthers

Livestock

−10000 −50000 5000 10000

Notional $ (M)

Jan01 Jul01 Jan02 Jul02 Jan03 Jul03 Jan04 Jul04 Jan05 Jul05 Jan06 Jul06 Jan07 Jul07 Jan08 Jul0

CITsHedge FundsHedgersHedger−HFsCIT−HFsOthers

Softs

−40000 −200000 20000 40000

Notional $ (M)

Jan01 Jul01 Jan02 Jul02 Jan03 Jul03 Jan04 Jul04 Jan05 Jul05 Jan06 Jul06 Jan07 Jul07 Jan08 Jul08 Jan09 Jul09 Jan10 Jul10 Jan11 Jun11

CITsHedge FundsHedgersHedger−HFsCIT−HFsOthers

Energy

−20000 −100000 10000 20000

Notional $ (M)

Jan01 Jul01 Jan02 Jul02 Jan03 Jul03 Jan04 Jul04 Jan05 Jul05 Jan06 Jul06 Jan07 Jul07 Jan08 Jul08 Jan09 Jul09 Jan10 Jul10 Jan11 Jun11

CITsHedge FundsHedgersHedger−HFsCIT−HFsOthers

Metals

Exposure defined as Net Position(t) x Front Month Price(15Dec2006)

Net Commodity Exposure
33 

Table 1: Trader Characteristics 

We report the number of traders and trader characteristics by year and trader category. Each 

summary statistic is a cross-sectional statistic of a measure that is an average over a year for each 

trader. Panel A reports the number of traders, Panel B reports the cross-sectional median total 

dollar notional net position for each category, Panel C reports the average of the number of 

commodities to which a trader has exposure, while Panel D reports the average percentage of 

contracts long. Panel E reports the persistence in our annual categorization. For confidentiality 

reasons, the number of traders for CIT-HF and Hedger-HFs are concealed as they are very small. 

Panel A: Number of Traders 

Ranking Year Population CIT C.Hedger Hedge Fund Others 

2000 4822 4 810 324 3672 

2001 4576 4 857 334 3369 

2002 4729 6 953 391 3363 

2003 4990 6 1075 466 3424 

2004 5376 9 1169 567 3610 

2005 5197 9 1208 688 3267 

2006 5664 12 1453 874 3293 

2007 5629 12 1483 974 3123 

2008 5667 15 1503 1089 3027 

2009 5148 20 1332 1082 2686 

2010 5699 18 1465 1116 3072 

Panel B: Median Notional Net Position, 15Dec2006 Indexed Contract Prices $M 

Ranking Year Population CIT C.Hedger Hedge Fund Others 

2000 0.026 549.758 -2.434 0.806 0.070 

2001 0.014 527.124 -1.056 -0.039 0.055 

2002 0.005 315.939 -2.970 1.712 0.046 

2003 0.023 482.972 -2.482 2.394 0.056 

2004 -0.008 352.938 -3.265 0.720 0.000 

2005 -0.181 1893.471 -3.626 0.110 -0.041 

2006 -0.103 1737.572 -4.760 0.192 0.000 

2007 -0.191 2678.250 -5.569 0.368 -0.024 

2008 -0.291 2335.372 -5.301 0.200 -0.014 

2009 -0.261 1746.668 -5.084 0.438 -0.054 

2010 -0.242 2332.411 -7.128 1.362 -0.013 

Panel C: Average Number of Commodities with Any Exposure 

Ranking Year Population CIT C.Hedger Hedge Fund Others 

2000 1.257 14.925 1.246 2.364 1.147 

2001 1.268 15.070 1.215 2.427 1.148 

2002 1.263 15.629 1.210 2.442 1.113 

2003 1.289 16.383 1.210 2.387 1.131 

2004 1.328 16.345 1.204 2.543 1.128 

2005 1.373 16.795 1.194 2.593 1.113 

2006 1.415 17.626 1.200 2.571 1.101 

2007 1.480 18.493 1.243 2.634 1.111 

2008 1.502 17.627 1.239 2.410 1.174 

2009 1.549 16.227 1.208 2.506 1.161 

2010 1.574 16.713 1.242 2.594 1.216 

 
34 

Table 1, Continued 

Panel D: Average Percentage of Contracts Long 

Ranking Year Population CIT C.Hedger Hedge Fund Others 

2000 0.522 0.860 0.394 0.599 0.545 

2001 0.528 0.858 0.449 0.512 0.550 

2002 0.521 0.842 0.400 0.609 0.546 

2003 0.520 0.863 0.392 0.646 0.542 

2004 0.495 0.894 0.367 0.587 0.522 

2005 0.465 0.873 0.352 0.545 0.489 

2006 0.469 0.887 0.323 0.563 0.506 

2007 0.452 0.893 0.295 0.581 0.484 

2008 0.448 0.873 0.299 0.549 0.483 

2009 0.452 0.880 0.301 0.575 0.473 

2010 0.445 0.872 0.250 0.604 0.477 

Panel E: Persistence in Annual Categorization, 2000-2009 

 CIT C.Hedger Hedge Fund Others 

Pr(Category(t+1)=X | Category(t)=X) 0.91 0.86 0.85 0.66 

Pr(Category(t+1)=X | Category(t)=X, Alive(t+1)=True) 0.93 1.00 1.00 1.00 
35 

Table 2: Time Series Summary Statistics for Flows and Returns 

We report summary statistics for 19 indexed commodities at a weekly frequency. Indexed contract returns 

are expressed in basis points, while flows are expressed as notional dollar values ($M) normalized using 

indexed contract prices on December 15, 2006. Panel A reports summary statistics for the period 

September 15, 2008 onwards. Panel B reports the ratio of trader flow volatilities for September 15, 2008 

onwards, as well as for the periods January 1, 2006 to September 15, 2008 and January 1, 2001 to January 

1, 2006. 

Panel A: Summary Statistics, 15Sep2008-01Jun2011 

Sector Commodity Mean SD SD SD SD SD T 

Indexed 

Contract 

Return 

Indexed 

Contract 

Return Flow: CIT Flow: HF 

Flow: 

C. Hedger 

Flow: 

Other 

Unclass. 

Grains 

Chicago Wheat -14.5 547.3 103.7 194.6 141.7 132.4 142 

Corn 13.5 566.7 177.6 457.1 391.1 204.3 142 

Kansas City Wheat 4.8 509.3 28.8 78.7 92.8 44.3 142 

Soybeans 23.9 422.3 121.6 429.7 386.7 254.9 142 

Soybean Oil 10.1 441.3 49.2 150.6 139.7 104.9 142 

Livestock 

Feeder Cattle -1.3 222.4 16.8 55.5 28.4 35.5 142 

Lean Hogs -28.2 370.8 50.2 105.7 52.6 113.2 142 

Live Cattle -14.1 209.9 81.9 194.0 118.3 141.5 142 

Softs 

Cocoa 12.6 447.2 17.3 53.1 38.8 41.5 142 

Coffee 40.3 454.9 68.8 204.5 171.7 108.5 142 

Cotton #2 68.4 532.2 62.9 125.4 67.3 145.6 142 

Sugar #11 50.6 615.4 98.0 136.5 160.3 158.2 142 

Energy 

Crude Oil -27.7 619.5 851.8 1173.7 388.5 755.3 142 

Heating Oil -6.4 566.1 288.1 567.0 264.2 526.9 142 

Natural Gas -99.3 655.3 512.5 834.1 244.8 637.2 142 

RBOB Gasoline 16.7 616.7 156.8 519.3 279.4 479.9 142 

Metals 

Copper 29.0 486.0 121.7 267.8 78.8 253.0 142 

Silver 98.0 573.4 123.7 216.6 63.1 143.4 142 

Gold 49.4 295.9 493.4 741.5 182.9 627.0 142 

Panel B: Ratio of Flow Volatilities, Grain/Livestock/Softs Average 

Period: HF/CIT 

C. Hedger

/CIT Other/CIT 

HF 

/C. Hedger 

15Sep2008-01Jun2011 2.6 2.0 1.8 1.4 

01Jan2006-15Sep2008 3.5 2.6 2.4 1.5 

01Jan2001-01Jan2006 6.3 4.2 4.2 1.6 
36 

Table 3: Commodity Returns and the VIX 

We report coefficients from a weekly regression of commodity returns as the left-hand side variable on contemand one lag of changes in the VIX as right hand side variables, controlling for lagged commodity returns, changes in the BDI index, changes in the Baa credit spread, and changes in inflation compensation. For w

soybeans, soybean oil, and cotton, we also include the 12-month percentage change in projected world dstocks, and US production. Each row reports coefficients for a different commodity and each set of columcoefficients for different sample periods. For brevity, only the coefficient on the contemporaneous change reported. Coefficients are reported where returns are in percentage points and the change in VIX is standardistandard deviation. We use the Newey and West (1987) construction for standard errors with four lags. */**/

significant at the 10%, 5% and 1% levels, respectively. 

Coefficient on Contemporaneous ΔVIX (1-SD) 

Post-Crisis Pre-Crisis 

15Sep2008-01Jun2011 01Jan2010-01Jun2011 01Jan2006-15Sep2008 01Jan2001

T=142 Weeks T=74 Weeks T=141 Weeks T=262 

Returns (%) Coef. t-statistic Coef. t-statistic Coef. t-statistic Coef. Grains 

Chi W -2.700 [-7.37]*** -2.416 [-3.70]*** -0.105 [-0.20] 0.190 Corn -1.991 [-3.83]*** -1.968 [-4.71]*** -0.298 [-0.71] -0.042 KC W -2.457 [-6.94]*** -2.395 [-3.96]*** -0.086 [-0.15] 0.287 Soybeans -1.569 [-4.26]*** -1.350 [-3.35]*** -0.050 [-0.14] 0.052 Soyb Oil -1.792 [-4.99]*** -1.341 [-3.97]*** -0.082 [-0.24] -0.149 

Livestock 

F Cattle -0.973 [-3.91]*** 0.018 [0.11] 0.128 [0.52] 0.121 L Hogs -0.397 [-1.17] -0.997 [-2.39]** 0.035 [0.12] -0.340 L Cattle -0.848 [-4.94]*** -0.214 [-1.14] -0.103 [-0.40] 0.169 

Softs 

Cocoa -0.922 [-2.35]** -0.339 [-0.77] -0.847 [-1.71]* -0.176 Coffee -1.259 [-4.07]*** -1.177 [-2.27]** -0.574 [-1.76]* 0.085 Cotton -1.596 [-5.98]*** -1.112 [-1.85]* -0.219 [-0.62] -0.262 Sugar -1.167 [-2.10]** -1.652 [-2.19]** -0.141 [-0.34] 0.583 

Energy 

Oil -2.020 [-3.77]*** -1.364 [-2.65]*** 0.050 [0.14] -0.193 Heat Oil -1.786 [-3.78]*** -1.004 [-2.58]*** 0.176 [0.46] -0.385 Nat Gas -1.554 [-2.53]** -0.891 [-1.16] -0.140 [-0.25] -0.678 Gas -1.526 [-2.49]** -1.225 [-2.92]*** 0.244 [0.58] 

Metals 

Copper -1.576 [-3.95]*** -1.763 [-5.11]*** -1.060 [-1.83]* -0.493 Gold -0.518 [-1.17] -0.253 [-0.79] -0.294 [-0.63] -0.045 Silver -1.434 [-2.39]** -1.190 [-1.52] -1.025 [-1.51] -0.217 

Average R-Squared 22.67% 25.41% 10.22% 3.9
37 

Table 4: Positions Changes and the VIX 

We report coefficients from a weekly regression of position changes as the left-hand side variable on contemporaneous and on

as right hand side variables, controlling for lagged commodity returns, percentage changes in the BDI index, changes in the Ba

in inflation compensation. For wheat, corn, soybeans, soybean oil, and cotton, we also include the 12-month percentage demand, US stocks, and US production. Each row reports coefficients for a different commodity, and each column reports cogroups. The sample period in Panel A is September 15, 2008 through June 1, 2011. Panel B reports results for January 1, 22008, while Panel C reports results for January 1, 2001 through January 1, 2006. Coefficients are standardized to standard standard deviation of VIX changes. For brevity, only the term on the contemporaneous change in VIX is reported. We use 

construction for standard errors with four lags. */**/*** denotes significant at the 10%, 5% and 1% levels, respectively. 

Panel A: Post-Crisis, 15Sep2008-01Jun2011 (T=142 Weeks) 

Coefficient on Contemporaneous ΔVIX (1-SD) 

CITs Hedge Funds Comm. Hedgers Other Un

Flows (σ) Coef. t-statistic Coef. t-statistic Coef. t-statistic Coef. Grains 

Chi W -0.177 [-2.50]** -0.219 [-2.73]*** 0.295 [3.92]*** 0.183 

Corn -0.161 [-1.82]* -0.166 [-1.78]* 0.134 [1.61] 0.190 KC W -0.191 [-2.34]** -0.133 [-1.67]* 0.222 [2.70]*** 0.004 Soybeans -0.206 [-2.12]** -0.169 [-1.82]* 0.176 [2.24]** 0.211 Soyb Oil -0.093 [-0.96] -0.145 [-1.70]* 0.179 [1.81]* 0.151 

Livestock 

F Cattle -0.087 [-1.39] -0.069 [-0.84] 0.181 [2.31]** -0.037 

L Hogs -0.179 [-1.08] -0.059 [-1.02] -0.003 [-0.04] 0.208 L Cattle -0.372 [-2.71]*** -0.058 [-0.67] 0.175 [2.41]** 0.158 

Softs 

Cocoa -0.112 [-1.08] 0.003 [0.04] 0.040 [0.75] 0.063 

Coffee -0.383 [-3.69]*** -0.137 [-1.63] 0.210 [2.73]*** 0.202 Cotton -0.203 [-2.07]** -0.192 [-2.39]** 0.222 [2.69]*** 0.250 Sugar -0.284 [-2.35]** -0.151 [-1.77]* 0.144 [2.28]** 0.243 

Average R-Squared 12.68% 15.75% 15.85% 10.
38 

Table 4, continued 

Panel B: Pre-Crisis, 01Jan2006-15Sep2008 (T=141 Weeks) 

Coefficient on Contemporaneous ΔVIX (1-SD) 

CITs Hedge Funds Comm. Hedgers Oth

Flows (σ) Coef. t-statistic Coef. t-statistic Coef. t-statistic Coef. Grains 

Chi W 0.133 [1.25] 0.215 [2.17]** -0.161 [-1.34] -0.246 

Corn 0.024 [0.22] -0.038 [-0.41] 0.086 [1.05] -0.087 KC W 0.117 [1.62] 0.086 [0.65] -0.126 [-0.86] 0.007 Soybeans 0.116 [1.02] 0.026 [0.26] -0.038 [-0.39] -0.160 Soyb Oil 0.102 [1.11] -0.048 [-0.50] 0.061 [0.80] -0.044 

Livestock 

F Cattle 0.171 [1.12] -0.118 [-1.69]* 0.010 [0.13] 0.077 

L Hogs -0.195 [-1.71]* -0.116 [-1.48] -0.115 [-1.01] 0.269 L Cattle -0.014 [-0.16] -0.007 [-0.06] 0.111 [1.23] -0.052 

Softs 

Cocoa 0.078 [0.70] -0.181 [-2.07]** 0.163 [1.33] 0.108 

Coffee -0.062 [-0.64] 0.001 [0.01] 0.032 [0.35] 0.012 Cotton -0.007 [-0.05] 0.018 [0.20] -0.058 [-0.68] 0.041 Sugar -0.209 [-1.78]* -0.069 [-0.63] 0.071 [0.53] 0.096 

Average R-Squared 9.02% 16.77% 13.91% 

Panel C: 01Jan2001-01Jan2006 (T=262 Weeks) 

Coefficient on Contemporaneous ΔVIX (1-SD) 

CITs Hedge Funds Comm. Hedgers Oth

Flows (σ) Coef. t-statistic Coef. t-statistic Coef. t-statistic Coef. Grains 

Chi W 0.001 [0.02] -0.001 [-0.02] -0.028 [-0.46] 0.039 

Corn 0.096 [1.71]* -0.013 [-0.22] 0.001 [0.02] 0.005 KC W -0.099 [-1.54] 0.111 [1.77]* -0.103 [-1.80]* -0.023 Soybeans 0.152 [2.80]*** -0.011 [-0.21] -0.017 [-0.25] -0.004 Soyb Oil 0.017 [0.39] -0.028 [-0.50] 0.035 [0.57] 0.044 

Livestock 

F Cattle 0.028 [0.75] 0.085 [1.11] -0.018 [-0.27] -0.069 

L Hogs 0.027 [0.55] -0.010 [-0.17] -0.043 [-0.84] 0.031 L Cattle -0.029 [-0.61] 0.067 [1.13] -0.126 [-2.43]** 0.082 

Softs 

Cocoa 0.023 [0.69] -0.113 [-1.87]* 0.070 [1.04] 0.108 

Coffee -0.014 [-0.26] 0.002 [0.04] -0.023 [-0.42] 0.022 Cotton 0.057 [1.32] 0.048 [0.83] -0.022 [-0.33] -0.052 Sugar -0.033 [-0.65] 0.145 [1.87]* -0.160 [-2.17]** -0.081 

Average R-Squared 2.52% 12.84% 9.64% 
39 

Table 5: Financial Distress and CIT Position Changes 

We report coefficients from a weekly account-level panel regression of CIT position changes as the left-hand side 

variable on changes in the VIX, an indicator for whether the trader has a CDS spread above the median for the 

week, and an interaction between the two, controlling for the lagged log of absolute notional position size in the 

commodity, lagged commodity returns, percentage changes in the BDI index, changes in the Baa credit spread, 

and changes in inflation compensation. For wheat, corn, soybeans, soybean oil, and cotton, we also include the 

12-month percentage change in projected world demand, US stocks, and US production. Each row reports 

coefficients for a different commodity. The sample period is September 15, 2008 through June 1, 2011. 

Coefficients are standardized to standard deviations in flows per one standard deviation of VIX changes. 

Standard are clustered at the week level (T=142). */**/*** denotes significant at the 10%, 5% and 1% levels, 

respectively. 

CDS Hi/Lo Change in VIX (1-SD) Interaction 

RSquared 

Flows (σ) Coef. t-statistic Coef. t-statistic Coef. t-statistic 

Grains Chi W -0.116 [-2.52]** -0.006 [-0.31] -0.085 [-2.19]** 0.020 

Corn -0.001 [-0.01] 0.005 [0.21] -0.110 [-2.35]** 0.025 

KC W 0.083 [1.44] -0.040 [-0.94] -0.060 [-1.34] 0.012 

Soybeans -0.037 [-0.66] -0.055 [-1.62] 0.017 [0.28] 0.017 

 Soyb Oil -0.053 [-0.88] -0.026 [-1.02] 0.015 [0.27] 0.008 

Livestock F Cattle -0.041 [-0.65] 0.007 [0.1] -0.051 [-0.83] 0.006 

L Hogs -0.118 [-2.23]** -0.033 [-1.2] -0.018 [-0.3] 0.019 

 L Cattle -0.096 [-2.02]** -0.091 [-2.50]** -0.029 [-0.42] 0.020 

Softs Cocoa -0.050 [-0.81] 0.006 [0.09] -0.050 [-0.46] 0.003 

Coffee -0.116 [-2.37]** -0.028 [-0.75] -0.159 [-2.98]*** 0.037 

Cotton -0.068 [-1.38] 0.019 [0.59] -0.120 [-1.87]* 0.026 

 Sugar -0.080 [-1.53] 0.000 [0.02] -0.124 [-1.99]** 0.018 

 
40 

Table 6: Commercial Hedger Sub-Groups 

We report coefficients from a weekly regression of position changes as the left-hand side variable on 

contemporaneous and one lag of changes in the VIX as right hand side variables, controlling for lagged 

commodity returns, percentage changes in the BDI index, changes in the Baa credit spread, and changes in 

inflation compensation, for trader subgroups. For wheat, corn, soybeans, soybean oil, and cotton, we also include 

the 12-month percentage change in projected world demand, US stocks, and US production. Each row reports 

coefficients for a different commodity, and each column reports coefficients for different trader groups. The 

sample period is September 15, 2008 through June 1, 2011 (T=142 weeks). Coefficients are standardized to 

standard deviations in flows per one standard deviation of VIX changes. For brevity, only the term on the 

contemporaneous change in VIX is reported. We use the Newey and West (1987) construction for standard errors 

with four lags. */**/*** denotes significant at the 10%, 5% and 1% levels, respectively. 

Comm. Hedgers, Long Comm. Hedgers, Short 

Flows (σ) Coef. t-statistic Coef. t-statistic 

Grains 

Chi W 0.150 [1.96]** 0.285 [3.87]*** 

Corn 0.000 [0.00] 0.141 [1.71]* 

KC W 0.008 [0.11] 0.280 [3.21]*** 

Soybeans 0.003 [0.04] 0.177 [2.07]** 

Soyb Oil 0.138 [1.58] 0.148 [1.62] 

Livestock 

F Cattle 0.060 [1.04] 0.159 [2.04]** 

L Hogs 0.124 [1.37] 0.011 [0.17] 

L Cattle -0.097 [-1.41] 0.230 [3.04]*** 

Softs 

Cocoa 0.015 [0.24] 0.047 [0.70] 

Coffee 0.130 [1.71]* 0.246 [2.71]*** 

Cotton -0.125 [-1.75]* 0.237 [2.74]*** 

Sugar -0.153 [-2.25]** 0.164 [2.45]** 

Average R-Squared 8.52% 14.57% 

 
41 

Table 7: Analysis of Commitment of Traders Data 

We report coefficients from a weekly regression of position changes as the left-hand side variable on contemporaneous anthe VIX as right hand side variables, controlling for lagged commodity returns, percentage changes in the BDI index, ch

spread, and changes in inflation compensation. For wheat, corn, soybeans, soybean oil, and cotton, we also include th

change in projected world demand, US stocks, and US production. Each row reports coefficients for a different commoreports coefficients for different trader groups from the Commitment of Traders reports. The sample period is September 

1, 2011 (T=142 weeks). Coefficients are standardized to standard deviations in flows per one standard deviation of VIX only the term on the contemporaneous change in VIX is reported. We use the Newey and West (1987) construction for st

lags. */**/*** denotes significant at the 10%, 5% and 1% levels, respectively. 

Coefficient on Contemporaneous ΔVIX (1-SD) 

Disaggregated COT Report 

Producers Swap Dealers Managed Money Other Non-Comm. 

Flows (σ) Coef. t-statistic Coef. t-statistic Coef. t-statistic Coef. t-statistic Grains 

Chi W 0.270 [3.41]*** -0.078 [-1.20] -0.176 [-2.10]** -0.115 [-1.89]* 

Corn 0.179 [2.18]** -0.075 [-1.13] -0.166 [-1.83]* -0.088 [-1.07] KC W 0.221 [3.04]*** -0.111 [-1.44] -0.119 [-1.44] -0.142 [-1.95]* Soybeans 0.216 [2.55]** -0.117 [-1.33] -0.178 [-2.07]** 0.025 [0.33] Soyb Oil 0.168 [2.09]** -0.058 [-0.89] -0.156 [-1.80]* 0.123 [1.40] 

Livestock 

F Cattle 0.167 [2.35]** -0.066 [-1.00] -0.089 [-1.20] -0.083 [-1.38] 

L Hogs 0.063 [0.88] -0.063 [-0.47] -0.087 [-1.50] 0.167 [1.86]* L Cattle 0.177 [2.42]** -0.216 [-2.47]** -0.093 [-1.15] -0.014 [-0.19] 

Softs 

Cocoa 0.066 [0.90] -0.045 [-0.51] 0.041 [0.49] -0.166 [-3.42]*** 

Coffee 0.209 [2.60]*** -0.235 [-2.53]** -0.151 [-1.81]* -0.008 [-0.11] Cotton 0.257 [3.28]*** -0.140 [-1.39] -0.158 [-1.88]* -0.064 [-0.88] Sugar 0.222 [3.31]*** -0.153 [-1.75]* -0.140 [-1.69]* 0.140 [2.00]** 

Average R-Squared 16.92% 10.59% 16.30% 8.04% 
42 

Table 8: Positions Changes and the Implied Volatility of the Financial Sector 

We report coefficients from a weekly regression of position changes as the left-hand side variable on contem

and one lag of changes in the implied volatility of options on the Financial Select Sector SPDR ETF as righvariables, controlling for lagged commodity returns, percentage changes in the BDI index, changes in the spread, and changes in inflation compensation. For wheat, corn, soybeans, soybean oil, and cotton, we also i12-month percentage change in projected world demand, US stocks, and US production. Each row reports cfor a different commodity, and each column reports coefficients for different trader groups. The sample September 15, 2008 through June 1, 2011. Coefficients are standardized to standard deviations in flowstandard deviation of changes in implied volatility. For brevity, only the term on the contemporaneous implied volatility is reported. We use the Newey and West (1987) construction for standard errors with 

*/**/*** denotes significant at the 10%, 5% and 1% levels, respectively. 

Coefficient on Contemporaneous ΔImplied Vol. (1-SD) 

CITs Hedge Funds Comm. Hedgers Other Un

Flows (σ) Coef. t-statistic Coef. t-statistic Coef. t-statistic Coef. Grains 

Chi W -0.150 [-1.89]* -0.215 [-3.51]*** 0.253 [4.11]*** 0.237 

Corn -0.202 [-2.25]** -0.038 [-0.43] 0.066 [0.92] 0.143 KC W -0.139 [-1.56] -0.100 [-1.58] 0.175 [2.32]** -0.030 Soybeans -0.278 [-4.11]*** -0.104 [-1.16] 0.134 [1.69]* 0.191 Soyb Oil -0.186 [-2.35]** -0.141 [-1.36] 0.134 [1.15] 0.202 

Livestock 

F Cattle -0.018 [-0.29] -0.069 [-0.82] 0.132 [2.15]** 0.008 

L Hogs -0.207 [-1.11] -0.081 [-1.25] 0.044 [0.64] 0.179 L Cattle -0.376 [-2.65]*** -0.030 [-0.30] 0.194 [2.91]*** 0.121 

Softs 

Cocoa -0.218 [-1.66]* 0.024 [0.33] 0.036 [0.77] 0.070 

Coffee -0.411 [-5.35]*** -0.070 [-0.86] 0.198 [1.91]* 0.101 Cotton -0.177 [-1.75]* -0.216 [-2.24]** 0.109 [1.10] 0.256 Sugar -0.300 [-2.55]** -0.147 [-1.23] 0.135 [1.94]* 0.275 

Average R-Squared 12.62% 15.24% 14.81% 10.5
43 

Table 9: Returns and Position Changes 

We report coefficients from a weekly regression of returns as the left-hand side variable on contemporaneous position chavariable, controlling for lagged commodity returns, percentage changes in the BDI index, changes in the Baa credit spreacompensation. For wheat, corn, soybeans, soybean oil, and cotton, we also include the 12-month percentage change in p

stocks, and US production. Each row reports coefficients for a different commodity, and each column reports coefficients for 

right-most column measures position changes as the aggregated position change across all agricultural commodities, where codollar quantity using fixed prices as of December 15, 2006. The sample period is September 15, 2008 through June 1, 2011. C

to percentage points in returns per one standard deviation in position changes. For brevity, only the term on the contempreported. We use the Newey and West (1987) construction for standard errors with four lags. */**/*** denotes significant at respectively. 

Coefficient on Contemporaneous Position Changes (1-SD) 

CITs Hedge Funds Comm. Hedgers Other Unclassified 

Returns (%) Coef. t-statistic Coef. t-statistic Coef. t-statistic Coef. t-statistic Grains 

Chi W 0.262 [0.54] 3.261 [8.96]*** -3.850 [-10.17]*** -1.555 [-2.66]*** 

Corn -0.298 [-0.53] 3.335 [9.18]*** -3.186 [-9.65]*** -1.341 [-3.51]*** KC W 1.222 [2.83]*** 2.692 [9.09]*** -3.058 [-8.86]*** -0.699 [-1.19] Soybeans 0.017 [0.04] 2.445 [7.14]*** -2.468 [-10.58]*** -1.361 [-3.94]*** Soyb Oil -0.186 [-0.53] 2.043 [7.04]*** -2.120 [-10.13]*** -1.245 [-2.82]*** 

Livestock 

F Cattle 0.317 [1.83]* 0.970 [5.44]*** -0.824 [-4.90]*** -0.268 [-1.63] 

L Hogs 0.454 [1.58] 1.422 [3.61]*** 0.083 [0.19] -2.113 [-8.56]*** L Cattle 0.291 [1.19] 0.824 [4.49]*** -1.114 [-7.06]*** -0.160 [-0.76] 

Softs 

Cocoa 1.302 [2.43]** 2.179 [5.67]*** -2.456 [-7.16]*** -2.569 [-6.97]*** 

Coffee 1.073 [2.27]** 3.244 [9.74]*** -3.205 [-10.14]*** -2.450 [-8.26]*** Cotton 0.035 [0.07] 1.658 [4.39]*** -1.665 [-3.37]*** -1.582 [-3.46]*** Sugar -0.918 [-2.04]** 2.910 [4.87]*** -1.521 [-3.16]*** -2.165 [-5.23]*** 

Energy 

Oil -1.613 [-3.73]*** 2.070 [6.03]*** -0.352 [-1.07] -0.521 [-0.83] 

Heat Oil -0.876 [-2.30]** 2.566 [6.50]*** -1.001 [-2.98]*** -2.613 [-4.61]*** Nat Gas 0.063 [0.12] 2.470 [5.12]*** -2.730 [-4.77]*** -3.313 [-8.37]*** Gas -1.002 [-2.32]** 2.219 [5.22]*** -0.622 [-1.48] -2.442 [-5.47]*** 

Metals 

Copper 0.346 [1.14] 2.308 [7.88]*** -2.191 [-4.88]*** -2.367 [-7.99]*** 

Gold -0.760 [-2.96]*** 1.820 [6.17]*** -1.516 [-4.57]*** -1.560 [-5.25]*** Silver -0.041 [-0.07] 1.638 [3.25]*** -2.180 [-6.00]*** -1.924 [-4.15]*** 

Average R-Squared 14.83% 32.70% 30.65% 25.97% 
44 

Table 10: Returns and VIX-Predicted Flows 

We report coefficients from a two-stage least squares regression of returns as the left-hand side variable on contemporaneous aggregate 

flows into grains, livestock, and softs as the right hand side variable, where trader flows have been predicted in a first-stage using the 

contemporaneous change in VIX. We control for lagged commodity returns, percentage changes in the BDI index, changes in the Baa 

credit spread, changes in inflation compensation, and the lagged change in VIX in both stages. For wheat, corn, soybeans, soybean oil, and 

cotton, we also include the 12-month percentage change in projected world demand, US stocks, and US production as controls. The 

sample period is September 15, 2008 through June 1, 2011. Coefficients are reported where returns are in percentage points and trader 

flows are standardized to one-standard deviation during the post-crisis period. Standard errors are calculated using a two-stage leastsquares standard error with a Newey-West four-lag correction for serial correlation and are reported below the coefficient in brackets. 

First-stage F-tests and p-values of the null hypothesis that the VIX does not affect trader flows are reported as well, with p-values in 

parentheses. */**/*** denotes significant at the 10%, 5% and 1% levels, respectively. 

CITs Total Financial Positions 

Second-stage First-stage Second-stage First-stage 

Returns (%) coefficient F and p-value coefficient F and p-value 

Grains 

Chi W 7.830 10.30 8.263 25.50 

[2.94]*** (0.002)*** [4.66]*** (0.000)*** 

Corn 6.147 8.05 5.771 26.70 

 [2.12]** (0.005)*** [3.31]*** (0.000)*** 

KC W 7.147 6.19 7.111 24.40 

[2.25]** (0.014)** [4.34]*** (0.000)*** 

Soybeans 4.917 5.79 4.469 23.70 

 [1.83]* (0.018)** [3.40]*** (0.000)*** 

Soyb Oil 5.264 6.06 4.502 29.80 

 [1.96]** (0.015)** [4.02]*** (0.000)*** 

Livestock 

F Cattle 2.531 8.57 2.427 28.20 

[2.23]** (0.004)*** [2.93]*** (0.000)*** 

L Hogs 1.176 6.49 1.038 25.50 

 [1.14] (0.012)** [1.32] (0.000)*** 

L Cattle 2.128 11.00 2.024 29.70 

[2.83]*** (0.001)*** [3.70]*** (0.000)*** 

Softs 

Cocoa 2.719 6.17 2.402 25.10 

[1.66]* (0.014)** [2.02]** (0.000)*** 

Coffee 3.703 6.30 3.223 27.40 

 [2.19]** (0.013)** [3.82]*** (0.000)*** 

Cotton 4.679 6.39 4.084 24.90 

[2.34]** (0.013)** [3.58]*** (0.000)*** 

Sugar 3.424 6.29 3.033 27.10 

 [1.42] (0.013)** [2.00]** (0.000)*** 

Energy 

Oil 5.863 6.78 5.186 30.80 

[2.32]** (0.010)** [3.49]*** (0.000)*** 

Heat Oil 5.019 7.21 4.368 34.20 

 [2.30]** (0.008)*** [3.37]*** (0.000)*** 

Nat Gas 4.563 6.20 4.032 27.70 

[2.08]** (0.014)** [2.45]** (0.000)*** 

Gas 4.445 6.50 3.852 30.10 

[1.57] (0.012)** [2.39]** (0.000)*** 

Metals 

Copper 4.621 6.29 4.076 26.90 

 [2.10]** (0.013)** [3.72]*** (0.000)*** 

Gold 1.549 6.32 1.323 27.20 

[0.92] (0.013)** [1.11] (0.000)*** 

Silver 4.239 6.53 3.718 27.00 

 [1.44] (0.012)** [2.03]** (0.000)***