A Speculative Bubble in Commodity Futures 

Prices? Cross-Sectional Evidence 

by

Dwight R. Sanders, Scott H. Irwin, 

and Robert P Merrin and Robert P. Merrin

Suggested citation format: Suggested citation format:

Sanders, D. R., S. H. Irwin, and R. P. Merrin. 2009. “A Speculative Bubble in 

Commodity Futures Prices? Cross-Sectional Evidence.” Proceedings of the 

NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, 

and Market Risk Management. St. Louis, MO. 

[http://www.farmdoc.uiuc.edu/nccc134].
A Speculative Bubble in Commodity Futures Prices? 

 Cross-Sectional Evidence 

Dwight R. Sanders

Scott H. Irwin 

Robert P. Merrin* 

Paper presented at the NCCC-134 Conference on Applied Commodity Price 

Analysis, Forecasting, and Market Risk Management 

St. Louis, Missouri, April 20-21, 2009 

Copyright 2009 by Dwight R. Sanders, Scott H. Irwin, and Robert P. Merrin. All rights reserved. 

Readers may make verbatim copies of this document for non-commercial purposes by any means, 

provided that this copyright notice appears on all such copies. 

*

Dwight R. Sanders is an Associate Professor of Agribusiness Economics at Southern Illinois 

University, Carbondale, Illinois. Scott H. Irwin is the Laurence J. Norton Chair of Agricultural and 

Consumer Economics at the University of Illinois, Urbana, Illinois. Robert P. Merrin is a Ph.D. 

student in the Department of Finance at Universiteit Maastricht, Netherlands.
A Speculative Bubble in Commodity Futures Prices? 

 Cross-Sectional Evidence 

 

Practitioner’s Abstract 

Recent accusations against speculators in general and long-only commodity index funds in 

particular, include: increasing market volatility, distorting historical price relationships, and 

fueling a rapid increase and decrease in commodity inflation. Some researchers have argued that 

these market participants—through their impact on market prices—may inadvertently prevented the 

efficient distribution of food aid to deserving groups. Certainly, this result—if substantiated—

would counter the classical argument that speculators make prices more efficient and thus improve 

the economic efficiency of the agricultural and food marketing system. Given the very important 

policy implications, it is crucial to develop a more thorough understanding of long-only index funds 

and their potential market impact. Here, we review the criticisms (and rebuttals) levied against 

(and for) commodity index funds in recent U.S. Congressional testimonies. Then, additional 

empirical evidence is added regarding cross-sectional market returns and the relative levels of 

long-only index fund participation in 12 commodity futures markets. The results suggest that index 

fund positions across futures markets have no impact on relative price changes across those 

markets. The empirical results provide no evidence that long-only index funds impact commodity 

futures prices. 

Key Words: Commitment’s of Traders, index funds, commodity futures markets 

Introduction 

The rapid increase in commodity futures prices from 2005 through 2008—coupled with greater 

overall trade volumes and larger positions held by long-only commodity index funds—have fueled 

a debate as to whether a price “bubble” existed in these markets. The debate has found its way to 

the halls of the U.S. Congress, where legislators are considering measures to curb “excessive” 

speculation (e.g., Lieberman 2008). The “pro-bubble” side is largely supported by certain hedge 

fund managers, some policy-makers, and commodity end-users (e.g., Gheit 2008; Masters 2008; 

Masters and White 2008). On the other side, a number of academic economists are skeptical of the 

bubble theory, citing a lack of empirical evidence (e.g., Krugman 2008; Pirrong 2008; Sanders and 

Irwin 2008). As an alternative, the “anti-bubble” contingency suggests that prices are driven by 

fundamental factors that have pushed commodities to higher—and perhaps permanently higher—

price levels (see Irwin and Good 2009). 

The outcome of the bubble debate has immediate economic consequences for the U.S. commodity 

futures industry, trading volumes, and potentially the pricing efficiency of commodity futures 

markets. In the bigger picture, the debate has potentially large ramifications with regard to the 

marketing, distribution, and pricing food products. After finding some evidence linking speculation 

and price behavior, Robles, Torero, and von Braun (2009) come to the following conclusion: 
2

“The excess price surges caused by speculation and possible hoarding could have severe 

effects on confidence in global grain markets, thereby hampering the market’s performance 

in responding to fundamental changes in supply, demand, and costs of production. More 

important, they could result in unreasonable or unwanted price fluctuations that can harm 

the poor and result in long-term, irreversible nutritional damage, especially among children” 

(p. 7). 

Given the gravity of this accusation, it is important that researchers carefully weigh the empirical 

evidence regarding the impact of speculation on commodity prices. 

In this paper, we will review the existing evidence and arguments for a speculative commodity 

bubble. Then, additional empirical evidence will be presented, where we specifically investigate 

the statistical relationship between long-only index funds and futures prices. Primarily, direct 

causal relationships will be examined by using a cross-sectional (market) regression tests over 

alternative horizons. Using Commodity Index Trader data from the Commodity Futures Trading 

Commission (CFTC) for 12 markets, simple regression tests will be used to test for a systematic 

relationship between index fund positions and returns across markets. The results provide another 

piece of evidence in this important debate impacting the food production and distribution system. 

The Arguments 

It Must be a Bubble!

Masters (2008) has led the bubble charge in Congressional hearings, painting the activity of index 

funds as akin to the infamous Hunt brothers’ cornering of the silver market. He blames the rapid 

increase in overall commodity prices from 2006-2008 (Figure 1) on institutional investors’ embrace 

of commodities as an investable asset class. Masters’ evidence is limited to anecdotes and the 

graphical correlation between money flows and prices (Figure 2). 

Other authors seem to work under the null hypothesis that speculators have an undesirable and 

somewhat unexplainable impact on market prices. For instance, Robles, Torero, and von Braun 

(2009) simply claim that “Changes in supply and demand fundamentals cannot fully explain the 

recent drastic increase in food prices” (p. 2). Similarly, a study by the Agricultural and Food Policy 

Center (2008) declares that the “...large influx of money into the markets, typically long positions, 

has pushed commodities to extremely high levels” and also show a graphical depiction of 

investment funds in the Goldman Sachs Commodity Index (p. 32). 

While this body of evidence is generally applauded by Congressional members and easily absorbed 

by the public, it glosses over the inherent complexity in futures markets and the dynamics of 

trading. Moreover, the lack of rigorous statistical methods generally brings out skepticism in 

academic circles. 

What Bubble? 

While casual bubble arguments are deceptively appealing, they do not generally withstand close 

examination. Irwin, Sanders, and Merrin (2009) present three “logical inconsistencies” in the 
3

arguments made by bubble proponents as well as five instances where the bubble story is not 

consistent with observed facts. Here, we will review these arguments and add some empirical 

evidence to their arguments. 

The first “logical inconsistency” within the bubble argument is that money flows are the same as 

demand. With equally informed market participants, there is no limit to the number of futures 

contracts that can be created at a given price level. Index fund buying is no more “new demand” 

than the corresponding selling is “new supply.” Thus, money flows in themselves do not 

necessarily impact prices. Second, there is no evidence that index investors distort futures and cash 

markets. Index investors do not participate in the futures delivery process or the cash market where 

long term equilibrium prices are discovered. Index investors are purely involved in a financial 

transaction using the futures markets, and they do not engaged in the purchase or hoarding of the 

cash commodity. Hence, to draw a parallel with the Hunt brother’s corner of the silver market is 

flawed. Lastly, the blanket categorization of speculators as wrongdoers and hedgers as victims of 

their actions is mistaken. Many hedgers speculate and some speculators also hedge. It is not clear 

that there is an easily identified “bad guy.” Market dynamics are complex, and it is not easy to 

understand the interplay between the varied market participants and their motivations for trading. 

In their rebuttal of the bubble theory, Irwin, Sanders, and Merrin (2009) also identify five areas 

where the bubble story is not consistent with the observed facts. First, as Krugman (2008) asserts, 

if a bubble raises the market price of a storable commodity above the true equilibrium price, then 

stocks of that commodity should increase (much like a government imposed price ceiling can create 

a surplus). Stocks were declining, not building, in most grain markets over 2005-2008, which is 

inconsistent with Krugman’s depiction of a price bubble in these markets. 

Second, theoretical models that show uninformed or noise traders impacting market prices rely on 

the unpredictable trading patterns of these traders to make arbitrage risky (e.g., De Long, Shleifer, 

Summers, and Waldmann 1990). Because the arbitrage—needed to drive prices to fundamental 

value—is not riskless, noise traders can drive a wedge between market prices and fundamental 

values. Importantly, index fund buying is very predictable. That is, index funds widely publish 

their portfolio (market) weights and roll-over periods. Thus, it seems highly unlikely that other 

large rational traders would hesitate to trade against an index fund if they were driving prices away 

from fundamental values. 

Third, if index fund buying drove commodity prices higher. Then, markets without index funds 

should not have seen prices advance. Again, the observed facts are inconsistent with this notion. 

Irwin, Sanders, and Merrin (2009) show that markets without index fund participation (fluid milk 

and rice) and commodities without futures markets (apples and edible beans) also showed price 

increases over the 2006-2008 period. This would suggest that there were other macroeconomic 

factors potentially influencing commodity prices. 

Fourth, speculation is not excessive when correctly compared to hedging demands. Working (1960) 

argued that speculation must be gauged relative to hedging needs. Utilizing Working’s speculative 

“T-index,” Sanders, Irwin, and Merrin (2008) demonstrate that the level of speculation in nine 

commodity futures markets from 2006-2008 (adjusting for index fund positions) was not excessive. 

Indeed, the levels of speculation in all markets examined were within the realm of historical norms. 
4

Across most markets, the rise in index buying was more than off-set by commercial (hedger) 

selling. 

The fifth and final observable fact—and the focus of this paper—revolves around the impact of 

index funds across markets. A priori, there is no reason to expect index funds to have a differential 

impact across markets. That is, if index funds can inflate prices, they should have a uniform impact 

across markets. It is difficult to rationalize why index fund speculation would impact one market, 

but not another. One would expect markets with the highest concentration of index funds positions 

to show the largest price increases. However, simple observation suggests that from 2006-2008, 

futures markets with the highest concentration of index fund positions (livestock markets) showed 

little or no increase, while those markets with the smallest index fund participation (grains and 

oilseeds) saw the largest price increases (Irwin, Sanders, and Merrin, 2009). Fortunately, this 

observable fact lends itself to a formalized test. 

Here, we evaluate the impact of index fund positions on the cross-section of market returns, 

providing a more rigorous evaluation of this point. Specifically we empirically test the relationship 

between index fund positions and returns across 12 commodity futures markets. The null 

hypothesis that index fund positions do not impact returns across futures markets is tested using 

data from the CFTC’s Commodity Index Traders Report and standard cross-sectional time series 

regressions. 

Data 

Commodity Index Traders (CIT) Report 

Starting in 2007, the Commodity Futures Trading Commission (CFTC) began reporting the 

positions held by index traders in 12 agricultural futures markets in the Commodity Index Traders 

(CIT) report, as supplement to the traditional Commitments of Traders (COT) report. According to 

the CFTC, the index trader positions reflect both pension funds that would have previously been 

classified as non-commercials as well as swap dealers who would have previously been classified as 

commercials hedging OTC transactions involving commodity indices. 

The CFTC readily admits that this classification procedure has flaws and that “...some traders 

assigned to the Index Traders category are engaged in other futures activity that could not be 

disaggregated....Likewise, the Index Traders category will not include some traders who are 

engaged in index trading, but for whom it does not represent a substantial part of their overall 

trading activity” (CFTC 2008). Regardless, the data are an improvement over the more heavily 

aggregated traditional COT classifications, and they should provide a good, albeit imperfect, 

measure of index trader activity. 

The CIT data are released in conjunction with the traditional COT report showing combined futures 

and options positions. The index trader positions are simply removed from their prior categories 

and presented as a new category of reporting traders. The CIT data includes the long and short 

positions held by commercials (less index traders), non-commercials (less index traders), index 

traders, and non-reporting traders. 
5

The CIT data are available weekly for the period covering January 3, 2006 through December 30, 

2008 (157 weekly observations). The reports show traders’ holdings as of Tuesday’s market close. 

To correspond with these release dates, the Tuesday-to-Tuesday log-relative returns are collected 

for nearby futures contracts. Markets included in the analysis are as follows: corn, soybeans, 

soybean oil, Chicago Board of Trade (CBOT) wheat, Kansas City Board of Trade (KCBOT) wheat, 

cotton, live cattle, feeder cattle, lean hogs, coffee, sugar, and cocoa. 

Index Trader Participation 

One of the primary concerns expressed by the industry is the magnitude of the commodity index 

activity. In a Barron’s article, one analyst quipped that “index funds account for 40% of all bullish 

bets on commodities...the index funds hold about $211 billion worth of bets on the buy side of U.S. 

markets.” (Epstein 2008). Presumably, the sheer size of the index fund positions may allow them to 

distort prices or price relationships across markets. 

Sanders, Irwin, and Merrin (2008) document that index traders do make up a surprisingly large 

portion of certain markets. In particular, index traders are over 20% of the open interest in live 

cattle, lean hogs and CBOT wheat. Moreover, index funds can have a disproportionate presence on 

the long side of the market, stemming from the fact they are “long-only.” To illustrate, Figure 3 

shows the percent of the long positions held by index traders in the cross section of markets 

examined. 

It is clear from Figure 3 that the portion of long positions held by index funds in each market can 

vary widely. In the cocoa market, 12% of the long positions were held by index funds while over 

45% of the long positions in the live cattle futures market were held by index funds. As suggested 

by Masters (2008) and Masters and White (2008), if index fund buying indeed represents new 

demand and thereby influences market prices, it is reasonable to expect a larger impact in those 

markets with relatively larger proportion (demand) of index traders. If not, then it becomes very 

difficult to rationalize how or why index buying would have a different impact across markets. If 

index fund buying truly represents new demand, then it should have an impact across all markets. 

Position Measures 

Index trader positions are measured in two ways to capture both the relative size of the index fund 

positions and to gauge their activity in each market. First, the relative size of the index funds is 

measured by simply using the percent of long positions held in each market (see Figure 3). This 

serves as a gauge of the relative size of index traders in each market. The percent of long positions 

is simply calculated as the number of long positions held by index traders divided by the total 

number of long positions within the market. 

Following Sanders, Boris, and Manfredo (2004), the percent net long (PNL) is also used to measure 

the position of traders with in a market, 

(1) 

t t

t t

t CIL CIS

CIL CIS PNL

+

− Commodity Index =
6

where, CIL is commodity index long positions and CIS is commodity index short positions. 

Changes in the PNL for commodity index funds are used to capture shifts in their positions within 

each market. These two measures—percent of long positions and change in the PNL—are used to 

test for the potential impact of index traders across markets. 

Cross-Sectional Tests 

Fama-MacBeth Cross-Sectional Regressions 

The relationship between index fund positions and subsequent market returns can be expressed in a 

cross-sectional regression, 

(2) Ri,t+1= α + βPositionsi,t + ei,t 

The null hypothesis of no market impact is that the slope coefficient,β, equals zero. An alternative 

bubble hypothesis would suggest that β>0, such that large index fund positions in market i portend 

relatively large subsequent returns in that market. In all regressions, the data are organized such 

that the causal linkages run from the positions at time t to the subsequent returns in time t+1. 

Therefore, a finding that β≠0 suggests that index positions precede market returns. 

Fama and MacBeth (1973) propose one method of estimating β in (2) that is still widely used in the 

literature (see Campbell, Lo, and MacKinley 1997). With the Fama-MacBeth regression procedure, 

equation (2) is estimated independently for each time period, t=1,2,3,...T, and across the 

i=1,2,3,...,N markets. The average of the estimated β’s is calculated for the T regressions, and it 

has a standard error of 1/T1/2. To illustrate, Figure 4 shows one of the T regressions at the quarterly 

horizon. In this particular quarter (t=third quarter, 2008), markets that had a relatively large portion 

of long positions held by index traders indeed saw relatively stronger returns in the subsequent 

period, consistent with a bubble-like impact across markets. Other observations, such as shown in 

Figure 5 (t=second quarter, 2007), show a negative relationship between index fund positions and 

returns. The Fama-MacBeth procedure essentially averages the cross-sectional slope coefficient 

across all of the time series observations and tests if the average is different from zero. The FamaMacbeth procedure is conducted for weekly, monthly, and quarterly horizons resulting in 156, 35, 

an 11 cross-sectional observations, respectively. 

The Fama-MacBeth results are presented in Table 1 for equation (2), where positions are 

represented by the percent of long positions held by index traders. Somewhat surprisingly, the 

average slope coefficient at each horizon is negative; albeit, not statistically different from zero. 

These results would suggest that if anything, markets with relatively large index trader positions 

tend to have relatively smaller price increases in subsequent time periods. 

Table 2 presents the results for the Fama-MacBeth regressions where the independent variable is the 

change in index fund’s PNL position. Here, the results are similar to those in Table 1 at the 

monthly and quarterly horizon, where the slope coefficients are negative and not statistically 

different from zero. A slightly different result is observed at the weekly horizon, where the slope 

coefficient is positive. But, it is still not statistically different from zero. Collectively, the Fama-
7

MacBeth regressions provide no evidence that index positions cause differential returns across 

commodity futures markets. 

Cross-Sectional Time Series Regressions

Standard cross sectional time series methods are another approach to estimating equation (2). 

However, this method provides greater flexibility than the Fama-MacBeth approach in terms of 

model specification because time series dynamics can also be modeled. Sanders, Irwin, and Merrin 

(2009) document some low-order positive autocorrelation in commodity futures returns. Consistent 

with that finding, we specify the following model. 

(3) Ri,t+1= α + βPositionsi,t +θRt + ei,t 

Where returns in market i at time t+1 are a function of index fund positions and returns from the 

previous period. White’s correction for cross-sectional heteroskedasticity is used to obtain 

consistent standard errors in (3). 

Table 3 presents the cross sectional time series estimations using index funds’ percent of long 

positions as the independent variable. The results are very consistent with the Fama-MacBeth 

regression results in Table 1. The estimated slope coefficients are negative at each horizon, but they 

are not statistically different from zero. So, again, there is no evidence that the cross-market 

variation in returns is related to long-only index fund positions. Notably, the returns do tend to 

show some low order positive autocorrelation, θ>0. 

Equation (3) is also estimated with the change in the index fund’s PNL as the explanatory variable 

(Table 4). At the monthly and quarterly horizons, the estimated beta coefficients are still negative 

and not statistically different from zero. However, as in Table 2, the one week horizon has a 

positive and marginally significant estimated beta coefficient (p-value =0.06). So, there is some 

very modest statistical evidence that the weekly cross-sectional returns may be positively related to 

the preceding week’s change in the index fund positions. The results are only marginally 

significant, so they do not provide overwhelming statistical evidence. But, they do raise the 

possibility that impacts may be found over some shorter horizons or with alternative position 

measures. 

Collectively, the empirical results generally fail to reject the null hypothesis that there is no crossmarket relationship between returns and the size of index fund positions. The lone exception is a 

marginal rejection (p-value = 0.06) at the one week horizon using the shift in the PNL as the 

explanatory variable. Otherwise, the relationship between cross market index positions and returns 

tends to be negative as opposed to positive. It is difficult to reconcile these results with the 

assertion that index fund buying is new demand in each market. 

Summary and Conclusions 

This research examines the cross-market correlation between market returns and positions held by 

long-only index funds. Irwin, Sanders, and Merrin (2009) state that one of the “inconsistent facts” 
8

in the bubble explanation for the recent increase and decline in commodity markets is that the price 

increases (decreases) did not occur uniformly across all markets. That is, if index funds drove 

prices higher, then they should have had an impact in all of the markets, not just select ones. 

Here, we provide an empirical test of this inconsistent fact. Specifically, we test if the relative size 

of index fund positions is correlated to subsequent returns across markets. A bubble scenario would 

suggest that returns are positively correlated with relative positions sizes across markets. However, 

using both Fama-MacBeth and traditional cross sectional tests, the null hypothesis of no crosssectional impact is only rejected in one of twelve models. 

The evidence that index fund positions impact returns across markets is scant. This leaves a big 

hole in the bubble argument. If index fund buying represents new demand that drives prices higher, 

then why doesn’t the new demand have an impact in each market? Like all theories, the bubble 

theory purported in the popular press should be evaluated based on its ability to predict. In the case 

of predicting cross sectional returns, the theory falls short. 

Assertions that speculators drive prices beyond fundamental value and disrupt important 

humanitarian efforts have been the spark for recent legislative proposals designed to curb 

speculation in commodity futures markets. Given the importance of speculators in a well 

functioning marketplace it is important the policy-makers consider the entire body of evidence with 

regard to the impact of speculators on market prices. 

The vast majority of empirical evidence presented by academic researchers fails to find any 

relationship between positions held by large traders and subsequent price behavior (e.g., Bryant, 

Bessler, and Haigh 2006; Gorton, Hayashi, and Rouwenhorst 2007; Sanders, Irwin, and Merrin 

2009). Those that do find some evidence (e.g., Robles, Torero, and Braun 2009) often use nonstandard techniques or data (e.g., cash or index prices). Therefore, even though the arguments made 

by bubble proponents are intuitively appealing to the non-economist, they do not stand on a firm 

empirical footing. While rigorous empirical studies cannot preclude a speculative impact on 

commodity prices, the body of evidence certainly does not support that conclusion. Legislators and 

public policy commentators would be well-served to let this evidence guide their actions. 

References 

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11

Table 1. Fama MacBeth Regression Results for Percent of Long Positions. 

p-value

Horizon α β β=0 Ν Τ

Week 0.0018 -0.0112 0.2087 12 156

Month 0.0067 -0.0461 0.2520 12 35

Quarter 0.0311 -0.1663 0.2139 12 11

Table 2. Fama MacBeth Regression Results for Change in Percent Net Long. 

p-value

Horizon α β β=0 Ν Τ

Week -0.0010 0.1086 0.1443 12 156

Month -0.0102 -0.2161 0.2294 12 35

Quarter -0.0165 -0.0811 0.7273 12 11
12

Table 3. Cross Sectional Time Series Results for Percent of Long Positions. 

p-value

Horizon α β θ β=0 Ν Τ

Week Coefficent 0.0026 -0.0136 0.0803 0.1392 12 156

T-Statistic 0.94 -1.48 2.63

Month Coefficent 0.0100 -0.0538 0.0926 0.2026 12 35

T-Statistic 0.79 -1.28 1.50

Quarter Coefficent 0.0351 -0.1724 0.1943 0.1468 12 11

T-Statistic 0.95 -1.46 1.81

Table 4. Cross Sectional Time Series Results for Change in Percent Net Long. 

p-value

Horizon α β θ β=0 Ν Τ

Week Coefficent -0.0010 0.0796 0.0784 0.0614 12 156

T-Statistic -1.07 1.87 2.55

Month Coefficent -0.0058 -0.1320 0.1184 0.2032 12 35

T-Statistic -0.56 -1.27 0.83

Quarter Coefficent -0.0190 -0.0903 0.2571 0.6917 12 11

T-Statistic -1.28 -0.40 2.36
13

Figure 1. CRB Commodity Index, 2006-2009 

150

170

190

210

230

250

270

Jan-06

Apr-06

Jul-06

Oct-06Jan-07

Apr-07

Jul-07

Oct-07Jan-08

Apr-08

Jul-08

Oct-08Jan-09

Apr-09

Jul-09

Oct-09

Month

Index

Figure 2. GSCI Spot Index Price Index vs. Index Speculator Assets

Source: Masters and White (2008)
14

Figure 3. Percent of Long Positions Held by Index Funds, December 31, 20007. 

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

Cocoa

Corn

Beanoil

KC Wheat

Coffee

SoybeansFdr. Cattle

Cotton

Sugar

Wheat

Hogs

Cattle

Percent of Long Positions

Figure 4. Cross-Sectional Regression, t=Third Quarter, 2008. 

CT

KC

SE

CC

FC

LC

LH

W

BO S

W

C

y = 0.4117x - 0.361

R2 = 0.081

-50%

-45%

-40%

-35%

-30%

-25%

-20%

-15%

-10%

-5%

0%

15% 20% 25% 30% 35% 40% 45% 50%

Percent of Long Positions

Return 
15

Figure 5. Cross-Sectional Regression, t=Second Quarter, 2007. 

C

W

S

BO

W

LH

LC

FC

CC

SE

KC

CT

y = -0.7007x + 0.2072

R2 = 0.3089

-15%

-10%

-5%

0%

5%

10%

15%

20%

25%

30%

5% 10% 15% 20% 25% 30% 35% 40% 45%

Percent of Long Positions

Return