COMMODITY INDEX INVESTING AND COMMODITY 

FUTURES PRICES1

 

by 

Hans R. Stoll and Robert E. Whaley 

Owen Graduate School of Management 

Vanderbilt University 

Nashville, TN 37203 

September 10, 2009 

1

 This research was supported by a grant from Gresham Investment Management LLC. 
1 

COMMODITY INDEX INVESTING 

AND COMMODITY FUTURES PRICES 

Recently, commodity index investing has come under attack. A Staff Report by 

the U.S. Senate Permanent Subcommittee on Investigation (hereafter, the “subcommittee 

report”) “...finds that there is significant and persuasive evidence to conclude that these 

commodity index traders, in the aggregate, were one of the major causes of ‘unwarranted 

changes’—here increases—in the price of wheat futures contracts relative to the price of 

wheat in the cash market.” (See subcommittee report (2009, p.2).) The purpose of this 

study is to provide a comprehensive evaluation of whether commodity index investing is 

a disruptive force not only in the wheat futures market in particular but in the commodity 

futures market in general. 

The study has four main sections. In the first, we examine the practice of 

commodity index investing, beginning with an explanation of the economic rationale for 

including a commodity index investment in institutional portfolios such as those of 

pension funds and university endowments. The rationale is simple. The returns of 

commodity index investments are uncorrelated with the returns of traditional assets such 

as stocks and bonds, and, therefore, provide a significant opportunity to reduce the risk of 

traditional investment portfolios. This diversification opportunity together with the 

advent of deep and highly active commodity futures markets has led to considerable 

growth in commodity index investment over the past decade. Commodity index products 

have a variety of forms including managed funds, ETFs, ETNs, and OTC return swaps. 

Many are benchmarked to well-diversified and transparent commodity indexes like the 

Standard & Poor’s–Goldman Sachs Commodity Index (S&P-GSCI) and the Dow JonesUBS Commodity Index (DJ-UBSCI) and nearly all of them are based on passive, longonly, fully collateralized commodity futures positions. Based on the composition of these 

indexes, CFTC Commitments of Traders (COT) Supplemental reports that include the 

positions of Commodity Index Traders (CIT), and futures prices from the respective 

exchanges, we estimate the total commodity index investment in the U.S. is currently 

about $174 billion, which is roughly consistent with the CFTC (2008) estimate of $161 

billion. About 24% of commodity index investors are index funds, 42% institutional 
2 

traders, 9% Sovereign wealth funds, and 25% retail investors holding exchange-traded 

commodity index products. 

The second section focuses on the general issue of whether commodity index 

investing “causes” futures price changes. Since commodity index investing involves a 

portfolio of commodities, we include a broad range of commodities in our analyses. Six 

analyses are performed. First, we examine the co-movements of futures prices for 

commodities known to be part of commodity index investing programs. Since the 

commodity index investing involves the simultaneous purchase of a portfolio of 

commodities, we should expect to see a high degree of contemporaneous correlation in 

futures price movements through time. Second, we examine the co-movements of futures 

prices known not to be part of commodity index investing programs. If non-index 

commodity futures prices behave like index commodity futures, commodity index 

investing is unlikely the cause. Third, we examine prices of five spot commodities that do 

not have futures contracts listed on them. Again, if spot commodities with no futures 

contracts and hence no involvement in commodity index investment programs have 

similar price behavior to index commodity futures, flows into commodity index 

investment portfolios are unlikely the cause. Fourth, we examine the impact of futures 

prices resulting from the periodic futures contract rolls that are necessary to mimic wellknown commodity indexes such as the S&P-GSCI and DJ-UBSCI. In a roll month, the 

nearby futures contracts are sold and the second nearby contracts are purchased. If 

commodity index investing has futures price impact, the return of the second nearby 

futures contract should exceed the return of the nearby contract. Fifth, we examine 

whether the demand for long commodity index portfolios (measured by changes in open 

interest) “causes” futures prices to rise and vice versa. To test for causality, we examine 

whether weekly futures returns are related to lagged flows into commodity index 

investing. Sixth, we examine the contemporaneous relation between weekly futures 

returns and the flows of speculators and commodity index traders during periods when 

commodity index traders are known to be entering and exiting the market. 

The third section focuses specifically on the Chicago Board of Trade’s wheat 

futures contract market, which is at the heart of the subcommittee report analysis. We 

begin by showing how the definition of the basis used in the subcommittee report 
3 

exaggerates the degree of divergence between the futures and cash prices. After 

correcting for the methodological problems, we show that the wheat futures price did not 

always converge in the 2006-2009 period, particularly in late 2008. We then go on to 

examine the CBT’s wheat convergence over a longer period of time and show that wheat 

has failed to converge in periods when the amount of commodity index investing is 

known to be negligible. In addition, we examine the convergence behavior of the CBT’s 

corn and soybean futures contracts over the same period and find that, while neither corn 

nor soybeans have had as great of divergence as wheat, grain commodity futures in 

general seem to experience convergence anomalies at the same points in time. Finally, we 

address the issue whether the failure of the wheat futures price to converge to the cash 

price has any meaningful economic consequences and show that the CBT’s wheat futures 

remains an effective tool for managing the price risk of wheat. 

In the fourth and final section, we summarize our main conclusions. In brief, we 

conclude: (a) commodity index investment is not speculation, (b) commodity index rolls 

have little futures price impact, and inflows and outflows from commodity index 

investment do not cause futures prices to change, and (c) failure of the wheat futures 

price to converge to the cash price at the contract’s expiration has not undermined the 

futures contract’s effectiveness as a risk management tool. 
4 

I. Commodity Index Investing 

Commodity index investing refers to the practice of buying baskets of 

commodities, albeit synthetically, to diversify an investment portfolio. The purpose of 

this section is to provide the backdrop for the analyses contained in the next two sections. 

This section has five parts. In the first, we provide the motives of commodity index 

investing. In the second, we discuss common forms in commodity index funds including 

managed funds, exchanged-traded funds and notes, as well as commodity return swaps. 

We also show how the demand for commodity index investment flows through to the 

commodity futures market. The third part then discusses two common benchmarks for 

commodity index portfolios. Just as the S&P 500 and Russell 1000 indexes serve as wellknown benchmarks for the stock market, the S&P-GSCI and DJ-UBSCI serve as wellknown benchmarks for the commodity market. These indexes also serve as reference 

assets in OTC commodity swaps. The fourth section describes in detail how we go about 

measuring the notional value of commodity index investing and the flow of funds into 

commodity index portfolios. The key source of data is the Commitment of Traders (COT) 

reports published weekly by the CFTC. These data serve as the basis of our analysis in 

Sections II and III of this report. The final section describes the results of a special call 

survey of swap dealers and commodity index funds conducted by the CFTC in June 2008 

to understand better the nature of commodity futures trading and, in a sense, audit the 

information provided in its weekly Supplemental reports. 

A. Motives for commodity index investing 

Markowitz (1952), who is considered the father of “modern portfolio theory,”1

 

developed a decision-making framework within which investors decide their investment 

portfolio allocations by considering the expected return and expected risks of all possible 

combinations of risky assets. The investor’s investment goal, he argues, is to identify the 

set of portfolios that maximize expected return for a given level of risk,2 so-called 

“efficient portfolios.” Then, based on the investor’s risk tolerance, a particular portfolio 

with its unique set of allocation weights is chosen from the efficient set. 

                                                           

1

 Based on this work, Markowitz received the Nobel prize in economics in the year 2000. 2

 The same set of portfolios is identified by minimizing risk for a given level of expected return. 
5 

Traditionally, the investments considered by institutional investors included only 

stocks, bonds, and cash. The reason is, of course, that these asset classes had deep and 

liquid markets with relatively low trading costs. Over the decades since the inception of 

modern portfolio theory, trading costs in all markets including stocks and bonds fell, 

thereby promoting market liquidity and depth and the advent of so-called “alternative 

investments.” One such alternative investment is physical commodities. Its appeal is 

driven not by the promise of high expected returns. Indeed, the expected return of this 

asset class is closely tied to the expected rate of inflation, which is not typically high. The 

primary advantage of including commodities in an investment portfolio is that 

commodity returns are relatively uncorrelated with the returns of traditional asset classes. 

The absence of correlation is attributable in part to inflation. During periods of rising 

inflation, traditional asset categories like stocks and bonds languish and perform poorly. 

Commodities, on the other hand, generally perform well. Increased demand for goods 

and services (i.e., rising inflation) usually implies increased demand for the commodities 

used in the production of those goods and services (i.e., commodity returns). In other 

words, holding commodities in an investment portfolio is risk-reducing, induced in part 

from the fact that a commodity futures position is an inflation hedge.3

 

B. Forms of commodity index investing 

Prior to the development of deep and liquid exchange-traded futures markets, 

physical commodities were seldom included in investment portfolios. The reason is 

simple. Physical commodities such as grain or crude oil are costly to buy and sell as well 

as store. After accounting for trading and storage costs, the expected returns from 

commodity investments were so low they outweighed the diversification benefits. What 

made commodity investment a viable asset class was the growth in trading volume of 

exchange-traded commodity futures contracts. During the period 1998 through 2007, the 

trading volume in exchange-traded commodity futures and futures options experienced a 

                                                           

3

 The diversification advantage of commodity investment is featured prominently in the promotional 

materials for commodity index funds. A description of PIMCO’s Commodity Real Return Fund, for 

example, says “Because the performance of stocks and bonds can be affected by similar market factors, 

diversifying into non-correlated assets, or assets that have returns that are impacted by differing market 

factors such as commodities, may offset losses, hence reducing portfolio risk.” 
6 

five-fold increase, with growth spread fairly uniformly across underlying asset 

categories.4 

With deep and liquid commodity futures contracts, the returns of physical 

commodities can be generated synthetically. In place of buying a physical commodity 

such as wheat, we buy an equivalently-sized futures position and place the cash that we 

would have spent on the physical commodity in money market instruments. In an 

efficiently-functioning marketplace, the rate of return and risk of the fully-collateralized 

futures position should be the same as the underlying commodity. 

Trading commodity futures seems to have replaced one problem (i.e., the 

illiquidity and costs of trading in the commodity market directly) with another (i.e., most 

institutional investors do not have the sophisticated trading operations necessary to 

manage a diversified commodity index portfolio using futures contracts).5

 The solutions 

were twofold—commodity index funds and commodity return swaps. With commodity 

index funds, institutional investors pool their commodity investment with a single fund 

manager and the manager agrees to manage the portfolio in a manner that mimics a welldiversified commodity index portfolio benchmark. With OTC commodity return swaps, 

institutional investors do similarly by entering an agreement to receive the rate of return 

on a specified commodity index portfolio and posting the investment funds as collateral. 

In both cases, the investment is passive in the sense that there is no attempt to beat the 

market through market timing or identifying under-priced commodities. The trading rules 

for index replication are well-defined, with expiring futures contract positions rolled into 

new contract positions on a pre-determined basis. The specific allocations to the different 

commodity futures are also pre-determined, with the weights varying by the importance 

of the commodity in the marketplace (e.g., the physical production of the commodity) 

and the liquidity of the futures contracts written on the commodity. This practice has 

become known as commodity index trading although the expression is a misnomer. 

Trading carries with it a connotation of buying and selling of securities or commodities, 

hoping to make a quick profit. Given the buy-and-hold, fully-collateralized nature of this 

investment allocation, a more accurate term is commodity index investing. 

                                                           

4

 See CFTC (2008, p.8). 

5

 Indeed, many institutional traders are barred from trading futures contracts. 
7 

Diversifying traditional investment portfolios with commodity investment has 

been practiced by large institutional investors such as pension funds and endowment 

funds for more than a decade, and the practice continues to grow. In recent years, an 

attempt has been made to capture the individual investor demand for commodity-like 

investment using exchange-traded funds and notes. Exchange-traded funds (ETFs) are 

like mutual fund shares that trade on a stock exchange and are structured in such a way 

that the price of the shares reflects the value of the index upon which it is based. 

Commodity-based exchange-traded notes (ETNs) are debt securities whose price is 

linked to an underlying index. On the maturity date of the note, the issuer of the note 

promises to pay the holder of each share of the note the value of a specified commodity 

index less a management fee. 

Figure I-1 is a schematic showing the relation between the institutional and 

individual demand for commodity index portfolios and the supply of commodity index 

portfolio replication contracts as provided by the commodity futures market. In general, 

institutions channel their commodity index investment to managed funds or OTC swap 

agreements. Individuals, on the other hand, generally have only exchange-traded 

commodity index products in their investment opportunity set. Managed funds, OTC 

swap dealers, and exchange-traded funds are then required to provide the return of a 

commodity index benchmark. The OTC dealer does so directly by buying commodity 

futures contracts to hedge its short commodity exposure. Managed funds and exchangetraded funds can, like the OTC swap dealer, synthetically replicate the returns of a 

commodity index using futures contracts, or they can simply enter into an agreement with 

a commodity swap dealer that provides such returns, whichever is cheaper. In the latter 

case, the swap dealer, again, hedges the demand from commodity funds directly in the 

futures market. The sizes of the leftmost and rightmost boxes in Figure I-1 are identical. 

The demand for expected return/expected risk characteristics of commodity index 

portfolios equals the supply of those characteristics with fully-collateralized positions in 

the futures market. While the conduits for gathering the commodity exposure may vary, 

the effect is the same. 
8 

Figure I-1: Schematic of the relation between the demand for commodity index portfolio 

products and the supply of commodity index replication contracts by the futures market.

The vehicles for commodity index investment include managed funds, OTC swaps, and exchangetraded products. 

Demand for commodity

index portfolio investment

Vehicles for

commodity index

investment

Supply of commodity

index replication contracts

OTC swap

dealers

Exchange‐

traded

products

Managed

funds

Institutions

Individuals

Commodity futures

markets

C. Commodity index portfolios 

Up to this point, the term “commodity index portfolio” has been used in a generic 

sense. Over the past decade, two commodity indexes have emerged as industry 

benchmarks—the Standard and Poor’s–Goldman Sachs Commodity Index (S&P-GSCI) 

and the Dow Jones–UBS Commodity Index (DJ-UBSCI).6

 The S&P-GSCI index is the 

oldest commodity index with its price levels dating back to August 1989. Its weights are 

determined on the basis of world production of the underlying commodities. Because the 

index is designed to be “tradable,” futures markets representing each particular 

commodity are deep and liquid. Data for the DJ-UBSCI are available dating back to 

October 1991. Dow emphasizes the tradability of its index by placing higher weights on 

commodities with highly active futures markets. To avoid overexposure to any particular 

                                                           

6

 This index was formerly known as the Dow Jones–AIG Commodity Index or DJ-AIGCI. 
9 

commodity, Dow limits sector investment to 33% of the index. Conversely, no 

commodity included in the index can constitute less than 2% of its market value. 

Both the S&P-GSCI and the DJ-UBSCI are reasonably well-diversified. Table I-1 

shows the market value weights of the commodities in the index as of July 2009. The 

S&P-GSCI weights are actual market value weights as of the close of trading on July 14, 

2009. The DJ-UBSCI weights are the targets market value weights for the index set by 

Dow Jones at the beginning of the year. The S&P-GSCI has 24 different commodities 

included in it, compared to the DJ-UBSCI’s 19. That is not to say that the S&P index is 

better diversified than the DJ index, however. Over the period January 3, 2000 through 

August 10, 2009, the annualized standard deviation of the daily total returns of the S&PGSCI was 25.9%, compared with 17.8% for the DJ-UBSCI index. The reason is that the 

S&P-GSCI, as noted above, is production-weighted and therefore very heavily in the 

energy sector, with 68% of its market value coming from crude oil, crude oil products, 

and natural gas. The DJ-UBSCI, on the other hand, limits its exposure in any one 

commodity sector to 33%. The energy sector is the largest, and, as the table shows, is at 

its cap. Agricultural commodities such as grains and livestock account for nearly as large 

a portion at 29%. Differences in the weights assigned to each commodity make the 

indexes less than perfect substitutes. During the period January 3, 2000 through August 

10, 2009, the correlation between their daily returns was 0.918. Also included in the table 

are the exchange where the specific commodity futures contracts used in the indexes are 

traded and the futures ticker symbol. 
10 

Table I-1: Market value weights of the commodities in the S&P-GSCI and DJ-UBSCI 

commodity indexes as of July 2009. 

S&P - GSCI DJ - UBSCI

Sector Commodity Exchange Ticker Actual weights Target weights

Agriculture Cocoa CSC CC 0.40%

Agriculture Coffee "C" CSC KC 0.76% 2.97%

Agriculture Corn CBT C 3.55% 5.72%

Agriculture Cotton #2 NYC CT 1.19% 2.27%

Agriculture Wheat (Kansas) KCBT KW 0.82%

Agriculture Soybean oil CBT BO 2.88%

Agriculture Soybeans CBT S 2.64% 7.60%

Agriculture Sugar CSC SB 2.33% 2.99%

Agriculture Wheat (Chicago) CBT W 3.90% 4.80%

Energy Oil (Brent crude) IPE LO 13.25%

Energy Oil (WTI crude) NYM CL 37.51% 13.75%

Energy Oil (GasOil) IPE QS 4.54%

Energy Oil (#2 Heating) NYM HO 4.19% 3.65%

Energy Natural gas NYM NG 4.14% 11.89%

Energy Oil (RBOB)1 NYM RB 4.75% 3.71%

Industrial metals Aluminum (High grade primary) LME AH 2.33% 7.00%

Industrial metals Copper LME CA 3.22% 7.31%

Industrial metals Lead LME PB 0.45%

Industrial metals Nickel LME NI 0.78% 2.88%

Industrial metals Zinc (Special high grade) LME ZS 0.60% 3.14%

Livestock Feeder cattle CME FC 0.61%

Livestock Lean hogs CME LH 1.51% 2.40%

Livestock Live cattle CME LC 3.19% 4.29%

Precious metals Gold CMX GC 3.01% 7.86%

Precious metals Silver CMX SI 0.32% 2.89%

Total weights 99.99% 100.00%

Total number of commodities 24 19

S&P - GSCI DJ - UBSCI

Sector Actual weights Target weights

Agriculture 15.59% 29.23%

Energy 68.38% 33.00%

Industrial metals 7.38% 20.33%

Livestock 5.31% 6.68%

Precious metals 3.33% 10.75%

Total 99.99% 100.00%

1

Both the S&P-GSCI and DJ-UBSCI rolled from the NYM's unleaded gasoline futures contract (HU) to the RBOB 

gasolines futures contract (RB) in 2006.
11 

Unlike stock indexes whose membership stays relatively constant through time, 

the composition of commodity price indexes changes as futures contracts expire. Before 

this happens, the nearby futures contracts in a particular commodity are sold and more 

distant futures contracts are purchased. For the S&P-GSCI and DJ-UBSCI, the hedge roll 

period is defined as the fifth through ninth business days of a month. During this five-day 

“roll period,” the index mechanically rolls from one contract to the next at a uniform 

rate.7 In general, the next out contract will be the second nearby contract, however, for 

certain commodities, the second nearby may have insufficient liquidity for the roll, in 

which case the third or fourth nearby contract may be used. Both Standard and Poor’s and 

Dow Jones have made deliberate judgments regarding the specific calendar months to use 

in each commodity futures market, and these are summarized in Table I-2. The table 

entries designate what calendar month is held in the index at the beginning of the month. 

Consider the February entry for the CBT’s wheat futures contract. The number 3 

indicates that the March futures contract is included in the index at the beginning of 

February (in both the S&P-GSCI and DJ-UBSCI indexes). The fact that the March entry 

is 5 indicates that the May futures is included in the index at the beginning of March, so 

the wheat futures position is rolled from the March to the May contract months during the 

February roll period. Note that, for most commodities, S&P-GSCI and DJ-UBSCI roll 

contracts in the same manner. For some commodities, however, the roll patterns are 

different. With crude oil (CL) and natural gas (NG), the DJ-UBSCI does not use the 

even-numbered contract months, presumably due to greater trading activity and market 

depth in the odd-numbered months. 

                                                           

7

 Spreading the trades over a five-day period mitigates the price impact in the futures, as does the public 

disclosure of the mechanical trading rules. 
12 

Table I-2: Timing of futures contracts rolls for the S&P-GSCI and DJ-UBSCI 

commodity indexes. Rolls are executed at a uniform rate over the fifth through ninth 

business days during the month. The numbers in the table designate the futures contract 

month in the index as of the beginning of the month (e.g., the CBT wheat contracts are 

rolled from the March contract to the May contract in February each year for both the 

S&P-GSCI and DJ-UBSCI). 

Panel A: S&P-GSCI

Ticker Exchange Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec.

W CBT 3 3 5 5 7 7 9 9 12 12 12 3

KW KCBT 3 3 5 5 7 7 9 9 12 12 12 3

C CBT 3 3 5 5 7 7 9 9 12 12 12 3

S CBT 3 3 5 5 7 7 11 11 11 11 1 1

KC CSC 3 3 5 5 7 7 9 9 12 12 12 3

SB CSC 3 3 5 5 7 7 10 10 10 3 3 3

CC CSC 3 3 5 5 7 7 9 9 12 12 12 3

CT NYC 3 3 5 5 7 7 12 12 12 12 12 3

LH CME 2 4 4 6 6 7 8 10 10 12 12 2

LC CME 2 4 4 6 6 8 8 10 10 12 12 2

FC CME 3 3 4 5 8 8 8 9 10 11 1 1

HO NYM 2 3 4 5 6 7 8 9 10 11 12 1

QS IPE 2 3 4 5 6 7 8 9 10 11 12 1

XB NYM 2 3 4 5 6 7 8 9 10 11 12 1

CL NYM 2 3 4 5 6 7 8 9 10 11 12 1

LO IPE 3 4 5 6 7 8 9 10 11 12 1 2

NG NYM 2 3 4 5 6 7 8 9 10 11 12 1

LA LME 2 3 4 5 6 7 8 9 10 11 12 1

LP LME 2 3 4 5 6 7 8 9 10 11 12 1

LL LME 2 3 4 5 6 7 8 9 10 11 12 1

LN LME 2 3 4 5 6 7 8 9 10 11 12 1

LX LME 2 3 4 5 6 7 8 9 10 11 12 1

GC CMX 2 4 4 6 6 8 8 12 12 12 12 2

SI CMX 3 3 5 5 7 7 9 9 12 12 12 3

Panel B: DJ-UBSCI

Ticker Exchange Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec.

W CBT 3 3 5 5 7 7 9 9 12 12 12 3

BO CBT 3 3 5 5 7 7 12 12 12 12 1 1

C CBT 3 3 5 5 7 7 9 9 12 12 12 3

S CBT 3 3 5 5 7 7 11 11 11 11 1 1

KC PIT 3 3 5 5 7 7 9 9 12 12 12 3

SB PIT 3 3 5 5 7 7 10 10 10 3 3 3

CT PIT 3 3 5 5 7 7 12 12 12 12 12 3

LH CME 2 4 4 6 6 7 8 10 10 12 12 2

LC CME 2 4 4 6 6 8 8 10 10 12 12 2

HO NYM 3 3 5 5 7 7 9 9 11 11 1 1

XB NYM 3 3 5 5 7 7 9 9 11 11 1 1

CL NYM 3 3 5 5 7 7 9 9 11 11 1 1

NG NYM 3 3 5 5 7 7 9 9 11 11 1 1

LA LME 3 3 5 5 7 7 9 9 11 11 1 1

HG CMX 3 3 5 5 7 7 9 9 12 12 12 3

LN LME 3 3 5 5 7 7 9 9 11 11 1 1

LX LME 3 3 5 5 7 7 9 9 11 11 1 1

GC CMX 2 4 4 6 6 8 8 12 12 12 12 2

SI CMX 3 3 5 5 7 7 9 9 12 12 12 3
13 

D. Notional value of commodity index investments 

Measuring the total notional value of commodity index investment is critical in 

developing an understanding of the relation between net flows into commodity index 

programs and price movements in the underlying commodity markets. Measuring the 

value of commodity index investment, in its many forms, can be problematic. While 

detailed information about exchange-traded commodity funds and notes is available, 

detailed information about managed funds and OTC swap agreements is not. But, since 

demand for commodity index portfolios must equal supply (as shown in Figure I-1), we 

can use information from the futures markets to infer not only the size of the commodity 

investment market, but also the inflows and outflows from the market. Below we 

describe how such inferences can be made. 

1. Commitment of Trader reports 

The timeliest source of information regarding commodity index investing in the 

U.S. is the Commitments of Traders (COT) reports published weekly by the Commodity 

Futures Trading Commission (CFTC). These reports show the aggregate trader positions 

in certain futures and options markets. The COT reports contain a breakdown of each 

Tuesday’s open interest for markets in which 20 or more traders hold positions equal to 

or above the reporting levels established by the CFTC. Trader position information is 

collected daily from reporting firms, clearing members, futures commission merchants, 

and foreign brokers. Reporting firms are required to file daily reports of the futures and 

option positions of traders who hold positions above specific reporting levels set by 

CFTC regulations. If, at the daily market close, a reporting firm has a trader with a 

position at or above the Commission’s reporting level in any single futures month or 

option expiration, it must report that trader’s entire position in all futures and options 

expiration months in that commodity, regardless of size. The aggregate of all traders’ 

positions reported to the Commission usually represents 70 to 90 percent of the total open 

interest in any given market. The reporting levels are adjusted from time to time as the 

nature of trading in a particular market evolves. The CFTC’s current reporting levels are 

shown in Table I-3. In the wheat futures and options contract market, for example, trader 

positions of 150 contracts or more are reported to the CFTC each day. 
14 

Table I-3: Reporting levels of selected U.S. futures contracts as set by the 

Commodity Futures Trading Commission as of July 5, 2006. If, at the daily market 

close, a trader has a position at or above the CFTC’s reporting level in any single futures 

month or option expiration, his/her broker must report the entire position in all futures 

and options expiration months in that commodity, regardless of size. 

Number of 

Sector Commodity contracts

Agriculture Cocoa 100

Agriculture Coffee 50

Agriculture Corn 250

Agriculture Cotton 100

Agriculture Frozen concentrated orange juice 50

Agriculture Oats 60

Agriculture Rough rice 50

Agriculture Soybean meal 200

Agriculture Soybean oil 200

Agriculture Soybeans 150

Agriculture Sugar No. 11 500

Agriculture Sugar No. 14 100

Agriculture Wheat 150

Energy Crude oil, sweet 350

Energy Natural gas 200

Energy No. 2 Heating oil 250

Energy Unleaded gasoline 150

Industrial metals Copper 100

Industrial metals Gold 200

Livestock Feeder cattle 50

Livestock Lean hogs 100

Livestock Live cattle 100

Precious metals Platinum 50

Precious metals Silver bullion 150

Three different COT reports are released every Friday at 3:30 p.m. Eastern time. 

The Futures-only reports have the longest history and are available electronically dating 

back to the beginning of 1986. The Futures-only report contains a breakdown of the open 

interest by commodity contract market. The report shows open interest separately by 

reportable and non-reportable positions. By definition, reportable positions are for large 

traders. Conversely, non-reportable positions are those of small traders. Reportable 

positions are then broken down by long and short commercial and noncommercial 

holdings and spreading. The CFTC staff classifies a trader as commercial or 
15 

noncommercial when the trader’s position first exceeds the commodity’s reportable level. 

A trading entity8 generally gets classified as a commercial if the CFTC Form 40 that it is 

required to file with the Commission states that the entity is “...commercially engaged in 

business activities hedged by the use of futures or options markets.” In order to ensure 

that traders are classified with accuracy and consistency, the Commission staff reviews 

this self-classification and may reclassify a trader if the staff has additional information 

about the trader’s use of the markets. Spreading measures the extent to which each 

noncommercial trader holds equal long and short futures positions. 

The Options-and-Futures reports, available electronically since 1995, contain the 

same fields as the Futures-only reports, except that open interest includes not only futures 

but also futures options contracts. In aggregating across open positions, option open 

interest is converted to a futures-equivalent basis using delta factors supplied by the 

exchanges. Long-call and short-put open interest are converted to long futures-equivalent 

open interest, and short-call and long-put open interest are converted to short futuresequivalent open interest. 

Most important from our standpoint is the CFTC’s Supplemental report. Since 

2006, the CFTC has reported the holdings of commodity index traders (CIT) separately 

from the standard noncommercial and commercial categories for 12 agricultural and 

livestock commodity futures.9

 To understand how this works, consider Figure I-2. The 

bar on the left shows the total long open interest of noncommercial and commercial 

traders as reported in the Futures and Options report. We are considering long open 

interest because commodity index traders (CIT, as labeled by the CFTC), are generally 

long-only. In the Options-and-Futures report, CIT positions were intermingled with other 

noncommercial (i.e., speculators) and commercial (i.e., traditional hedgers) traders. 

                                                           

8

 Note that it is the trader that is classified, not each individual transaction. 9

 See CFTC (2006, pp. 9-10). 
16 

Figure I-2: Schematic of reapportioning of the open interest reported in the CFTC 

Commitment of Traders reports for long noncommercial and commercial traders into 

speculator, commodity index trader, and hedger categories. 

Noncommercials

(i.e.,speculators

and commodity

index traders

with direct

investmentin

futuresmarket)

Commercials

(i.e.,traditional

hedgers andOTC

swap dealers

hedging

commodity index

exposure)

Speculators

Traditional

hedgers

Commodity index

traders(i.e.,

funds with direct

investmentin

futuresmarkets

andOTC swap

dealers hedging

commodity index

exposure)

In the Supplemental report, the total long open interest of noncommercial and 

commercial traders remains the same, however, the noncommercial category is 

partitioned into speculators and commodity index traders, and the commercial category is 

partitioned into traditional hedgers and commodity index traders. The commodity index 

traders classified as noncommercials are managed funds, pension funds, ETFs and ETNs, 

and other institutional investors seeking a long commodity index exposure. The 

commodity index traders classified as commercial are financial institutions such as OTC 

swap dealers who sell commodity index return swaps to institutional investors and then 

hedge by taking long positions in commodity futures. 

To illustrate the mechanics of Figure I-2, the open interest figures reported in the 

Options and Futures (OF) and Supplemental (S) reports for the CBT’s wheat futures 
17 

contract market on June 30, 2009. They are displayed in Panel A of Table I-4. On 

Tuesday, June 30, 2009, the total open interest, reported in both the OF and S reports, 

was 383,387 contracts. Reported in the second row of Panel A are the open positions of 

long noncommercial traders (i.e., long speculators and long commodity index traders 

with direct positions in the futures market). The number drops from 80,569 in the OF

report to 43,416 in the S report. The difference, 37,153, is the number of contracts of 

traders who are long noncommercials engaged in commodity index investing and is part 

of the total open interest of all long commodity index traders for that day, 170,256, as 

reported in the second last row of Panel A. Providing this breakdown of the 

noncommercial category is critical. Traditionally, the traders in the noncommercial 

category have been characterized as “speculators” by default since the traders in the 

commercial category are hedgers. But, with the advent and growth of commodity index 

investing, this characterization is misleading. Commodity index investors are not 

speculators. They do not take a directional view on commodity prices. They simply buyand-hold futures contracts to take advantage of the risk-reducing properties they provide. 

Speculators, on the other hand, have a directional view, and take long (or short) positions 

accordingly. The Supplemental report now tells us the difference. On this day, 43,416 of 

the 80,569 long noncommercials were long speculators and 37,153 were long commodity 

index traders. 

One of the more interesting results shown in Table I-4 (and in the Supplemental 

reports in general) is that the OTC swap dealers are by far the largest group of 

commodity index traders. To see this, note first that the total open interest of long 

commercials, as reported in the OF report is 176,016 contracts. After long commodity 

index traders are pulled from this category, the S report shows 44,944 contracts remain. 

This means that, of the 176,016 long commercial contracts, 131,072 were held by OTC 

swap dealers who are hedging short positions in commodity index rate-of-return swaps 

by going long the underlying futures contracts. 
18 

Table I-4: Selected fields from the CFTC’s Options and Futures and Supplemental 

reports for CBT’s wheat options and futures on June 30, 2009. Data are obtained 

from the web link, 

http://www.cftc.gov/marketreports/commitmentsoftraders/cot_historical.html. 

Panel A: Data reported in CFTC reports

From Futures From 

and Options Supplemental

Market participant position report report

Total open interest 383,387 383,387

Noncommercials 80,569 43,416

Spreaders 97,271 95,240

Commercials 176,016 44,944

Small traders 29,532 29,532

Commodity index traders 170,256

Panel B: Reconciliation between reports in number of contracts

Source of CIT trades Contracts Percent of total

Noncommercials 37,153 21.8%

Spreaders 2,031 1.2%

Commercials 131,072 77.0%

Small traders 0 0.0%

Total 170,256 100%

Long-only open interest

Long-only open interest

Panel B summarizes the results. Of the 170,256 long open interest categorized as 

commodity index trader (CIT) contracts, 37,153 or 21.8% are direct positions in the 

futures market by commodity index funds like managed funds, ETFs, and ETNs, and 

77.0% are indirect positions conveyed through the hedging activities of OTC swap 

dealers.10 In other words, in the wheat market on June 30, 2009, commodity index 

investing through return swaps in the OTC market was more than 3.5 times higher than 

through funds. 

To see the relative trading activity across commodities and through time, we 

compute the ratio of CIT swap trading to CIT direct investments for each commodity 

each week and then average across commodities each week during the period January 

2006 through June 2009. Figure I-3 shows the results. Early in the period, the lion’s share 

                                                           

10 The 2,031 accounted for by spreaders is inconsequential for our purposes. In most weeks, the number 

appears in the Supplemental report as 0. 
19 

of CIT positions was held by swap dealers, seven times more than by direct investment. 

Over the three and a half year period, however, the ratio has dropped as a result of the 

growth in managed commodity funds, ETFs, and ETNs. 

Figure I-3: Ratio of commodity index investing through commodity swaps to direct 

commodity index investing during the period January 2006 through June 2009.

Computed from data in the weekly CFTC Commitments of Trader Option and Futures 

and Supplemental files and futures prices from the CBT, CME, CSC, KCBT and NYC. 

Twelve agricultural and livestock commodity futures are tracked. Ratios are computed 

for each commodity each week, and are averaged across commodities. 

0

1

2

3

4

5

6

7

8

20060103 20070103 20080103 20090103

Ratio of swaps todirect trading

2. Monitoring commodity index investment 

The value of the COT reports in assessing not only the notional value of 

commodity index investment but also in determining the size of inflows and outflows 

quickly becomes apparent. In Figure I-4, we plot the notional value of CIT positions on a 

week-by-week basis from January 2006 through July 2009. Two lines are shown. The 

first is the actual dollar value of long-only commodity index trader positions each week. 

This number is computed in two steps. First, we take the reported open interest for each 

commodity, multiply by its contract denomination, and then multiply by the futures price. 

Since the COT data does not specify futures contract months, we use the nearby futures 

contract price for all reported open interest. Second, we sum across the notional values of 

each commodity to determine the notional value of all contracts. At the beginning of 

2006, the notional value of commodity index investing in these 12 agricultural and 
20 

livestock commodity futures is shown to be about $20 billion. The activity grew steadily 

through the beginning of 2008 to a level of about $50 billion, and then spiked up to $70 

billion and stayed there for a few months. Then, as precipitously as it spiked up, it fell 

back to a level of at about $30 billion by the beginning of 2009. 

On face appearance, this evidence appears to suggest that commodity index 

investing rose dramatically during the period and then backed off. But, part of it is 

illusion. Fact of the matter is that commodity prices rose precipitously in 2008. To 

separate growth in prices from inflows into commodity index investing, we again 

compute notional value, but this time using the commodity futures prices on the first date 

in the figure, January 3, 2006. A different picture emerges, as is shown in Figure I-4. 

Commodity investment begins at a level of $20 billion in 2006, rises at slow steady rate 

through mid-2008, peaks at about $40 billion, slowly falls through the beginning of 2009, 

and begins to rise again. Overall the figure is instructive in at least two ways. First, while 

commodity index investing doubled over the two-year period from January 2006 to 

January 2008, it did not more than triple, as indicated by the red line in the figure. Great 

care must be taken in separating price movements from net flows. Second, the growth in 

commodity index investment is steady. Decisions regarding commodity index investment 

are very deliberate asset allocation decisions made by institutions trying to manage risk. 

As such, they take place slowly through time. 
21 

Figure I-4: Notional value (NV) of long-only CIT open interest using 

contemporaneous and January 3, 2006 futures prices during the period January 

2006 through June 2009. Notional value for each commodity is computed by taking the 

product of the long-open interest of the long-only commodity index traders reported in 

the weekly CFTC Commitments of Trader Supplemental report, the contract 

denomination, and the nearby futures contract price. The notional values are then 

summed across commodities to determine total notional value of commodity index 

investing. Twelve agricultural and livestock commodity futures are tracked. 

0

10

20

30

40

50

60

70

80

20060103 20070103 20080103 20090103

Notinal value of CIT positions in USD 

billions

NV at current price NV at 1/2006 prices

Documenting an increase in long-only commodity index investing in isolation, 

however, can be deceiving. While Figure I-4 does show that long-only commodity index 

investing doubled from 2006 to 2008, it did not increase relative to the total open interest 

in the market. Both grew at about the same rate. To see this, consider Figure I-5 which 

shows the average ratio of long-only CIT open interest to total open interest across 

commodities each week. At the beginning of 2006, commodity index traders accounted 

for about 26% of the total long open interest of a typical commodity. In June 2009, the 

number was only slightly higher at about 30%. The figure at the bottom shows short 

commodity index positions relative to total open interest. The line at the bottom of the 

figure shows that short-only commodity index investing activity is negligible through the 

beginning of 2008, and then begins to increase. The increase is attributable in part, no 

doubt, to a new generation of exchange-traded funds based on the inverse return of 

commodity indexes. It may also be attributable to certain institutional investors shorting 
22 

futures against their long commodity index investment to reduce over-exposure to certain 

sectors.11 

Figure I-5: Percentage of total open interest held by long and short commodity 

index traders during the period January 2006 through June 2009. Data are from 

weekly CFTC Commitments of Trader Supplement files. Twelve agricultural and 

livestock commodity futures are tracked. Percentages are average of ratios across 

commodities by week. 

0%

5%

10%

15%

20%

25%

30%

35%

0%

5%

10%

15%

20%

25%

30%

35%

20060103 20070103 20080103 20090103

Long Short

Table I-5 contains the average ratios of the weekly long CIT positions to total 

positions by commodity across the 184 weeks in the January 2006 through June 2009 

period. The single highest ratio is for the CME’s lean hog market where CIT positions 

account for an average of 42.51% of total open interest, ranging from a low of 30.89% to 

a high of 51.42%. The CBT’s wheat market is next highest with CIT positions accounting 

for 41.15% of total open interest. The lowest ratio is for the CSC’s cocoa contracts where 

CIT positions are about 12.5% of total. Based on the information provided in Table I-1, 

this should not be surprising. Only the S&P-GSCI holds cocoa, and its allocation is 

0.40%. 

                                                           

11 Suppose that a pension fund currently has a return swap linked to the S&P-GSCI and that the price of 

crude oil has recently spiked upward. To make the swap have the return properties of a more diversified 

commodity index exposure, the pension fund choose to sell crude oil futures contracts against the swap. 
23 

Table I-5: Percentage of total open interest held by long commodity index traders 

during the 184-week period January 2006 through June 2009. Data are from weekly 

CFTC Commitments of Trader Supplement files. Twelve agricultural and livestock 

commodity futures are tracked. 

Commodity Exchange Average Minimum Maximum

Wheat CBT 41.15% 32.05% 51.78%

Wheat KCBT 22.03% 12.30% 32.66%

Corn CBT 22.98% 16.68% 30.07%

Soybeans CBT 25.31% 19.77% 30.58%

Soybean meal CBT 23.34% 17.84% 31.70%

Cotton No.2 NYC 31.59% 21.13% 43.94%

Lean hogs CME 42.51% 30.89% 51.42%

Live cattle CME 38.97% 27.24% 47.22%

Feeder cattle CME 25.08% 14.09% 35.16%

Cocoa CSC 12.47% 6.19% 19.70%

Suger No.11 CSC 27.77% 15.50% 37.69%

Coffee C CSC 24.21% 18.89% 34.61%

long commodity index traders

Percent of open interest held by

Table I-6 contains the notional value of the open interest of commodity index 

traders as of the close of trading on June 30, 2009. The figures reported for each 

commodity are computed as the product of open interest, contract denomination, and the 

6/30/09 futures price. The total market value of $36.3 billion is the value of commodity 

index open interest across the 12 commodities followed by the CFTC in the 

Supplemental reports. This value can be used to estimate the total market value of all 

commodity index investing. If we assume that all commodity index investing in the 

Supplemental reports is based on the S&P-GSCI,12 for example, and then use the fact that 

the 12 commodities account for 20.90% of the market value of the S&P-GSCI (see Table 

1), the total notional value of commodity index investing is $36.3/0.2090 or $173.8 

billion. The implied index weight for the CBT’s wheat futures contract, for example, is 

2.53% if all commodity index investing is linked to the S&P-GSCI. Table I-1 shows that 

this compares to Standard and Poor’s actual weight for this wheat futures contract, 

3.90%, which is reported in Table I-1. 

                                                           

12 Generally speaking, more commodity index funds are benchmarked against the S&P-GSCI than the DJUBSCI. The Dow index, however, is gaining in popularity because it is better diversified. 
24 

Table I-6: Total market value of contracts outstanding for the 12 commodity futures 

reported in the CFTC’s Supplemental file on June 30, 2009. The market value 

outstanding is the product of the total open interest, the contract denomination, and the 

nearby futures contract price. 

Notional value of

Ticker contracts Percent of total

Commodity Exchange symbol outstanding notional value S&P - GSCI DJ - UBSCI

Wheat CBT W 4,392,604,800 12.09% 2.53% 4.34%

Wheat KCBT KW 794,089,062 2.19% 0.46% 0.78%

Corn CBT C 6,102,931,900 16.79% 3.51% 6.03%

Soybeans CBT S 9,522,782,500 26.21% 5.48% 9.41%

Soybean oil CBT BO 1,388,780,190 3.82% 0.80% 1.37%

Cotton No.2 NYC CT 1,788,173,650 4.92% 1.03% 1.77%

Lean hogs CME LH 1,437,941,664 3.96% 0.83% 1.42%

Live cattle CME LC 3,116,530,872 8.58% 1.79% 3.08%

Feeder cattle CME FC 377,819,475 1.04% 0.22% 0.37%

Cocoa CSC CC 4,530,623 0.01% 0.00% 0.00%

Suger No.11 CSC SB 5,550,311,894 15.27% 3.19% 5.49%

Coffee C CSC KC 1,862,498,156 5.13% 1.07% 1.84%

Total 36,338,994,786 20.90% 35.92%

Implied index weights

E. Special call survey of swap dealers and index traders 

The CFTC’s Commitment of Traders Supplemental reports are very useful to the 

extent that they provide timely (i.e., weekly) snapshots of the level of commodity index 

investing. They have two weaknesses, however. First, they cover only 12 of the 33 U.S. 

exchange-traded commodity futures markets that are used in the construction of the welldiversified commodity index portfolios. Second, the CIT positions contain error. As 

noted earlier, the long-only CIT open interest is drawn from the long-only open interest 

of noncommercials (e.g., index funds) and the long-only open interest of commercials 

(e.g., commodity swap dealers). The error arises from the manner in which the CFTC 

classifies traders as commercial or noncommercial and as index traders. 

As noted earlier, the CFTC staff classifies a trader as commercial or 

noncommercial when the trader’s position first exceeds the commodity’s reportable level. 

A wheat farmer is typically a hedger who sells futures to lock in the price of his future 

harvest and is therefore designated as a commercial. That same farmer may, from time to 

time, buy wheat futures to attempt to profit from his directional view that the wheat price 

will rise in the short run. This wheat position, too, would be designated as commercial. 
25 

At the same time, a trader may be classified as a commercial in some commodities and as 

a noncommercial in other commodities. 

The classification of a trader as an index trader is done in a similar manner. If the 

trader appears to be replicating a commodity index by establishing long positions in the 

constituent commodity futures markets and then rolling the positions forward from 

futures to futures using a fixed methodology, he/she is earmarked as an index trader even 

though he may be engaged in other futures activity. At the same time, the commodity 

index trader category will not include some traders who are engaged in index investing, 

but for whom it does not represent a substantial part of their overall trading activity. 

Due to the importance of measuring commodity index investing levels accurately, 

the CFTC issued a special call to large traders in June 2008. Specifically, they requested 

that 16 swap dealers known to have significant commodity index swap business, 13 swap 

dealers known not to have significant index swap business, and 14 commodity index 

funds (including asset managers and sponsors of ETFs and ETNs whose returns are based 

on a commodity index) provide detailed data about actual index investing for the monthends December 2007 through June 2008, and then on an ongoing basis thereafter. While 

they received the data in a timely fashion, their analysis of the data was limited to only 

four commodities and the quarters ending December 31, 2007 through June 30, 2008.13

We highlight some of the results for the quarter ending June 2008 in Table I-7. 

                                                           

13 The CFTC received that data after June 30, 2008, and were required to provide their staff report to 

Congress by September 15, 2008. Consequently, they limited their analyses to 4 of 33 commodities and 3 

of the 7 months of the data collected. 
26 

Table I-7: Summary of commodity index investing by the CFTC (2008) Staff Report 

on Commodity Swap Dealers and Index Traders with Commission 

Recommendations. The reported values are for June 30, 2008. 

Panel A: Notional amount of index open interest

Billions of Percent of Billions of Percent of

Category USD U.S. total USD U.S. total

All exchanges 200

U.S. exchanges 161 945 17.0%

NYMEX crude oil futures 51 31.7% 405 12.6%

CBT wheat futures 9 5.6% 19 47.4%

CBT corn futures 13 8.1% 74 17.6%

ICE cotton futures 3 1.9% 13 23.1%

Panel B: Percent of total commodity index open interest in U.S. by participant

Percent of

U.S. total

Index funds 24%

Institutional investors 42%

Sovereign wealth funds 9%

Other traders 25%

Total 100%

Panel C: Notional amount of commodity index open interest by commodity

Futuresequivalent Net CITs Total

CBT wheat futures 194,000 177,817 444,081

CBT corn futures 350,000 417,279 2,049,965

ICE cotton futures 73,000 104,580 377,877

Index trading only All futures open interest

Open interest

Among the special call survey results shown in Table I-7 is the total notional 

amount of commodity index investment. For the quarter ending June 30, 2008, it was 

$200 billion across all exchanges worldwide, with $161 billion being tied to commodities 

traded in U.S. markets regulated by the CFTC. The total number of index commodities 

represented in the $161 billion is 33, and the total open interest in these 33 markets is 

$946 billion. Commodity index investing, therefore, accounts for 17% of the open 

interest in the relevant commodity futures markets. While the CFTC had data on all 33 

commodity futures markets, they provided detail on only four as noted earlier. Index 

investing of crude oil futures accounts for 31.7% of all index investing, and 12.6% of all 

crude oil futures outstanding. Of the agricultural contracts, corn accounts for 8.1% of 
27 

index investing and 17.6% of all corn futures contracts outstanding. Wheat is next with 

only 5.6% of all index investing, but with 47.4% of all contracts outstanding. Apparently 

index investing has a more concentrated presence in the wheat market. 

Panel B breaks down index investing by market participant. Index funds account 

for 24% of the $161 billion of commodity index open interest in the U.S. An index fund 

is defined as a client/counterparty with a fiduciary obligation to match or track the results 

of a commodity index, including ETFs and ETNs based upon a commodity index. 

Institutional investors have the single largest presence at about 42%. These are pension 

funds, endowment funds, or other similar types of investors. Sovereign wealth funds, 

non-U.S. government entities such as a government investment company or a 

government-run pension fund, hold about 9%. Finally, the “other” category is about 25% 

and is largely made up of retail investors holding ETFs, ETNs, and similar instruments 

that are publicly traded. 

The final panel in Table I-7 compares the survey’s index position sizes with those 

reported in the CFTC’s Supplemental reports. The futures-equivalent of wheat reported in 

CFTC’s (2008) Staff Report is 194,000 contracts on June 30, 2008. The net position of 

the CIT category reported in the July 1, 2008 Supplemental Report was 177,817. For corn 

and cotton, the numbers were 350,000 vs. 417,279 and 73,000 vs. 104,580, respectively. 

While the differences between these estimates reinforce the importance of collecting the 

survey information on a monthly basis moving forward, the special call survey timeseries is currently too sparse and the number of commodities too small to serve as the 

basis of any meaningful empirical analysis. The CFTC’s COT Supplemental report data 

remain the premier source for accurate and timely measurement of commodity index 

investment. 

 
28 

II. Relation between commodity index investing and futures prices 

 The subcommittee report observes that both the level of commodity prices and the 

level of commodity index investing surged upward during the period 2006 and 2007 and 

concludes that the increased commodity index investing caused the futures price increase. 

This conclusion illustrates the well-known logical fallacy that correlation proves 

causation. Correlation does not imply causation; it is only a requirement for it. Among 

other things, to prove causation, one event must occur before the other. The 

subcommittee report presents no such evidence. 

The purpose of this section is to examine the relation between commodity index 

investing and futures prices. In all, six analyses are carried out. First, we examine the comovements of futures prices for commodities known to be part of commodity index 

investing programs. Since the commodity index investing involves the simultaneous 

purchase of a portfolio of commodities, we should expect to see a high degree of 

contemporaneous correlation in futures price movements through time. Second, we 

examine the co-movements of futures prices known not to be part of commodity index 

investing programs. If non-index commodity futures prices behave like index commodity 

futures during the investigation period, the conclusion that commodity index investing is 

the cause is undermined. Third, we examine prices of five spot commodities that do not 

have futures contracts listed on them. Again, if spot commodities with no futures 

contracts and, hence, no involvement in commodity index investment programs have 

price behavior similar to index commodity futures, flows into commodity index 

investment portfolios are unlikely the cause. Fourth, we examine the impact of futures 

prices resulting from the periodic futures contract rolls that are necessary to mimic wellknown commodity indexes such as the S&P-GSCI and DJ-UBSCI. In a roll month, the 

nearby futures contracts are sold and the second nearby contracts are purchased. If 

commodity index investing has futures price impact, the return of the second nearby 

futures contract should exceed the return of the nearby contract. Fifth, we examine 

whether the demand for long commodity index portfolios (measured by changes in open 

interest) “causes” futures prices to rise and vice versa. To test for causality, we examine 

whether weekly futures returns are related to lagged flows into commodity index 
29 

investing. Sixth, we examine the contemporaneous relation between weekly futures 

returns and the flows of speculators and commodity index traders during periods when 

commodity index traders are known to be entering and exiting the market. 

A. Price co-movements of index commodities 

 The first investigation focuses on daily returns of 18 different commodity futures 

that are included in the S&P-GSCI and DJ-UBSCI during the period January 2006 

through July 2009. Daily open, high, low, and settlement prices as well as trading volume 

and open interest for each futures contract are from the futures exchanges. The logic 

underlying this analysis is straightforward. Commodity index investing is a mechanical 

trading strategy based on a set of well-defined and well-known rules, as was laid out in 

the previous section. Net funds flowing into commodity index investments are 

immediately redeployed into the commodity index futures market through the 

simultaneous purchase of all index commodities. If the commodity index trades are large 

enough to push prices upward, the prices in all markets should move upward 

concurrently. Put differently, the returns of all futures contracts used in index replication 

should be highly correlated. 

 Table II-1 contains the contemporaneous correlation matrix computed from the 

daily returns of 18 commodity futures contracts commonly included in commodity index 

investing. Surprisingly, the levels of correlation are quite low. Consider the column 

labeled W, the CBT’s wheat futures contract. This wheat contract accounts for about 4% 

of well-diversified commodity indexes such as the S&P-GSCI and should be highly 

correlated with other futures that have a high weight in the index14 like natural gas (NG), 

live cattle (LC), and gold (GC). As seen in the table, the correlations are quite low—

0.134 (4% of the index), 0.178 (3%), and 0.197 (3%), respectively. The column labeled 

C, the CBT’s corn futures contract, provides similar results. This evidence suggests that 

either commodity index trades have little effect on futures returns (because they fail to 

induce contemporaneous price movements) or the commodity return variability is being 

driven by factors other than commodity index investing. 

 

                                                           

14 Recall the index weights are given in Table I-1. 
30 

Table II-1: Correlation in daily returns of 18 commodity futures included in the S&P-GSCI and DJ-UBSCI during the perio

July 2009. Ticker symbols are: CC cocoa, KC coffee, C corn, CT cotton, KW Kansas City wheat, BO soybean oil, W Chicago wheatoil, NG natural gas, RB RBOB oil, FC feeder cattle, LH lean hogs, LC live cattle, GC gold, and SI silver.

CC KC C CT KW BO S SB W CL HO NG RB FC LH 

CC 1KC 0.328 1

C 0.223 0.323 1

CT 0.217 0.376 0.393 1

KW 0.231 0.308 0.567 0.343 1

BO 0.291 0.348 0.587 0.440 0.479 1

S 0.288 0.353 0.661 0.382 0.493 0.811 1

SB 0.236 0.305 0.299 0.369 0.281 0.365 0.324 1

W 0.230 0.308 0.602 0.356 0.943 0.481 0.492 0.292 1

CL 0.269 0.264 0.335 0.284 0.298 0.532 0.425 0.305 0.303 1

HO 0.233 0.271 0.337 0.299 0.279 0.539 0.417 0.305 0.287 0.769 1

NG 0.091 0.119 0.190 0.130 0.116 0.246 0.218 0.173 0.134 0.304 0.352 1RB 0.210 0.243 0.328 0.271 0.266 0.527 0.422 0.282 0.274 0.697 0.777 0.300 1

FC 0.064 0.137 -0.063 0.114 -0.001 0.088 0.063 0.098 0.018 0.140 0.113 0.067 0.162 1

LH -0.003 0.157 0.017 0.112 0.073 0.088 0.071 0.023 0.059 0.052 0.023 0.001 0.031 0.197 1

LC 0.195 0.211 0.157 0.234 0.180 0.195 0.180 0.162 0.178 0.177 0.174 0.088 0.175 0.645 0.102 

GC 0.272 0.173 0.238 0.166 0.183 0.317 0.271 0.184 0.197 0.270 0.278 0.098 0.227 -0.022 0.013 

SI 0.313 0.276 0.332 0.258 0.271 0.420 0.379 0.265 0.272 0.314 0.332 0.148 0.280 0.073 0.020 
31 

 Table II-1 also confirms a number of obvious relations. The correlation between 

the returns of the wheat futures contract traded at the CBT (W) and the wheat futures 

contract traded at the KCBT (KW), for example, is 0.943. Since the underlying 

commodities are simply two different types of wheat, their price movements should be 

highly correlated. Crude oil (CL) returns are highly correlated with the returns of its 

processed products—0.769 for heating oil (HO) and 0.697 for gasoline (RB)—and 

soybeans (S) are highly correlated with soybean meal (BO), 0.811. 

 Figure II-1 displays the CBT and KCBT wheat futures prices that were used to 

generate the correlation coefficient, 0.943. In addition, the MGEX wheat futures prices 

are shown. The figure is interesting in a number of respects. First, over the first year and 

a half, the lines are virtually on top of one another. This means that the three grades of 

wheat are virtually perfect substitutes from a rate of return perspective. In mid-2007 

through the beginning of 2008, the prices of all wheat futures contract increase 

precipitously. During this interval, the CBT and KCBT futures prices remain close 

together; however, the MGEX price rises to a level 50% higher than the other two 

futures. The importance of this comparison is that the subcommittee report argues that the 

higher incidence of commodity index futures trading caused the abnormal price increase 

in wheat over this period. If such is the case, the CBT futures price should have risen to a 

level well in excess of the KCBT and MGEX contracts because the CBT contract is the 

primary contract used by commodity index traders in taking a wheat position. What the 

figure shows is that the behavior of the CBT price is like the KCBT price and well below 

the MGEX price—evidence that again suggests that the abnormal behavior is driven by 

factors other than commodity index investing. 
32 

Figure II-1: Daily index levels representing the nearby futures contract prices of the 

wheat futures contracts traded on the CBT, the KCBT, and the MGEX during the 

period January 2006 through July 2009. Futures prices are from CBT, KCBT, and 

MGEX. 

0

100

200

300

400

500

600

700

20060103 20070103 20080103 20090102

Chicago Kansas City Minneapolis

Figure II-2 displays the prices of several different agricultural futures that have 

significant weights in well-diversified commodity indexes. Again, in assessing these 

figures, recall that commodity index investing refers to buying (selling) all of these 

contracts simultaneously, so, if commodity index investing is responsible for the 

abnormal price increases, the abnormal price increases should be experienced together. 

As Figure II-2 shows, they are not. The price of corn begins its ascent in late 2006, levels 

off for most of 2007, and then rises quickly to a level nearly 3.5 times its January 2006 

price in June 2008. Wheat, like corn, experiences erratic price movements during this 

period. But, wheat’s crisis appears to have started earlier than corn and reached its 

maximum price three months earlier. Soybeans, too, seem to have experienced 

tumultuous times, rising in price by nearly 150% by June 2008. The general pattern of 

increasing and then decreasing of prices during this period of time undoubtedly 

contributes to the modest positive levels of correlation reported in Table II-1—0.602 for 

wheat versus corn, 0.492 for wheat versus soybeans, and 0.661 for corn versus soybeans. 

But, the fact that the price shifts are not contemporaneous suggests, yet again, that 

commodity index investing is not the culprit. 
33 

Figure II-2: Daily index levels representing the nearby futures contract prices of the 

agricultural futures contracts traded on the CBT and the CME during the period 

January 2006 through July 2009. Futures prices are from CBT and CME. 

0

100

200

300

400

20060103 20070103 20080103 20090102

CBT wheat CBT corn

CBT soybeans CME live cattle

Finally, the price behavior of the live cattle futures contracts (LC) is also 

displayed in Figure II-2. Relative to the grains, live cattle has little price movement at all 

over the four-year period. This suggests that whatever was happening in the grains 

market was specific to the grain market sector and did not carry over into the livestock 

sector. It also suggests that commodity index investing is unrelated to futures price 

movements. Live cattle accounts for nearly 4% of the popular commodity indexes. 

During a period when flows into commodity index funds doubled, the live cattle futures 

price barely budged. 

B. Price co-movements of index versus non-index commodities 

 Another way to gather evidence regarding the relation between commodity index 

investing and futures prices is to examine the co-movements in prices of like 

commodities that are and are not included in the index. We have already examined one 

such case in Figure II-1. The CBT’s wheat futures contract is used by commodity index 

investors to capture the returns of the physical commodity wheat. The KCBT’s wheat 

futures contract is used only in a minor way, and the MGEX’s wheat futures contract is 

not used at all. As noted earlier, the co-movements in price are highly correlated, with the 
34 

MGEX futures price rising the most. For the subcommittee report conclusion to hold, the 

reverse pattern should hold. Similar results can be found for other agricultural 

commodities. The CBT, for example, has futures markets in both soybeans and oats. The 

difference between the two contracts from our perspective is that soybeans is an index 

commodity while oats is not. Figure II-3 shows the price behavior of the nearby futures 

contracts for both commodities over the period January 2006 through July 2009. As the 

figure shows, there is a close correspondence between the price movements of the two 

commodities, often rising and falling in unison as is expected if they were both part of a 

commodity index investing program and such a program had a significant price impact. 

But, oats is not included in any of the popular commodity indexes and is therefore, by 

definition, unaffected by index investing. In other words, the price co-movement must be 

dominated by factors related to the agricultural commodities market rather than 

commodity index investing. 

Figure II-3: Daily index levels representing the nearby futures contract prices of 

soybean and oats futures contracts traded on the CBT during the period January 

2006 through July 2009. Futures prices are from CBT. 

0

100

200

300

20060103 20070103 20080103 20090102

CBT soybeans CBT oats

The precious metal contracts traded on the Comex offer another opportunity to 

make an index versus non-index comparison. Gold and silver are included in the S&PGSCI and DJ-UBSCI, and palladium and platinum are not. Figure II-4 shows their price 

behavior over the January 2006 through July 2009 investigation period. Again, the degree 
35 

of co-movement would seem to suggest that a common factor is influencing the prices of 

all of these commodities simultaneously. It cannot be commodity index investing, 

however, since palladium and platinum are not part of index programs. 

Figure II-4: Daily index levels representing the nearby futures contract prices of the 

precious metal futures contracts traded on the CMX during the period January 

2006 through July 2009. Futures prices are from CMX. 

0

100

200

300

20060103 20070103 20080103 20090102

Palladium Platinum Gold Silver

C. Price co-movements of commodities with no futures markets 

 Our final examination of price co-movements identifies three important cash 

commodities—coal, cobalt, and rhodium—that do not have futures markets and are not 

part of commodity index investing programs. Figure II-5 shows the weekly price 

behavior of these commodities over the period January 2006 through July 2009. Like so 

many other commodities shown in previous figures, there is a general price increase from 

the beginning of 2006 through the end of 2007. Prices then jump upward during the first 

half of 2008, and then fall. Again, commodity index investing cannot be the culprit, at 

least for these cash commodities, since these cash commodities are not part of index 

investing programs. Indeed, they do not even have commodity futures contracts listed on 

them. The price patterns appear to be a reflection of some common macro-economic 

event that affected many commodity sectors during the beginning of 2008. 
36 

Figure II-5: Spot prices of commodities with no futures markets and not included in 

commodity index portfolios during the period January 2006 through July 2009.

Weekly prices are from DataStream. 

0

100

200

300

400

20060106 20070106 20080106 20090105

Coal Cobalt Rhodium

D. Analysis of index rolls 

The first three analyses focused on commodity price co-movements and argued 

that their patterns are inconsistent with commodity index investing. Prices of index 

commodity futures contracts should move together, and they do not. Prices of index and 

non-index commodity futures should not move together, but they do. Prices of cash 

commodities with no futures markets and not included in commodity index investment 

programs should not move together, but they do. While the evidence that some other 

factor or set of factors is affecting commodity prices, the analysis would be more 

powerful if the futures returns were measured over an interval in which we know 

commodity index investing was being executed. One such interval of time is when 

commodity index funds and swap dealers must roll their futures positions from the 

nearby contract to the next nearby contract. Recall that the timing of such rolls for the 

S&P-GSCI and DJ-UBSCI, two indexes commonly used as benchmarks for commodity 

index funds and as a reference price in OTC commodity swap contracts, was provided in 

Table 1-2 of the last section. 

 In this investigation, we attempt to stack the cards in favor of finding that 

commodity index investing and futures returns are related. We do so by selecting the 
37 

eight commodity futures contracts that are in both the S&P-GSCI and DJ-UBSCI, and are 

also followed in the CFTC’s COT Supplemental reports. We require the commodity to be 

in both indexes in order to maximize the amount of index investing over the roll period. 

Both indexes have investment in the same commodity futures at the same time.15 For the 

CBT’s wheat futures contract, which is part of the sample, 3.90% of the market value of 

the funds/swaps is pegged to the S&P-GSCI and 4.80% of the market value of the 

funds/swap is pegged to the DJ-UBSCI. We also require the commodity to have open 

interest data in the CFTC’s Supplemental report to allow comparison between the 

numbers of contracts rolled in mimicking the diversified portfolio indexes and the total 

commodity index investing in a particular commodity. The eight commodity futures 

contracts used in our sample are listed in Table II-2. Five commodity futures are from the 

CBT, two are from the CSC, and one from the NYC. These eight commodities account 

for 19.1% and 33.0% of the market values of the S&P-GSCI and DJ-UBSCI indexes, 

respectively. 

Table II-2: Commodity futures contracts included in the S&P-GSCI and DJ-UBSCI 

commodity indexes and the CFTC’s Commitment of Traders Supplemental Reports 

during the period January 2006 through July 2009. Weight in commodity index is 

percent of market value of index accounted for by the commodity.

Commodity Ticker 

futures Exchange symbol S&P-GSCI DJ-UBSCI

Wheat CBT W 3.90% 4.80%

Corn CBT C 3.55% 5.72%

Soybeans CBT S 2.64% 7.60%

Cotton No.2 NYC CT 1.19% 2.27%

Lean hogs CME LH 1.51% 2.40%

Live cattle CME LC 3.19% 4.29%

Suger No.11 CSC SB 2.33% 2.99%

Coffee C CSC KC 0.76% 2.97%

Total 19.07% 33.03%

Weight in commodity index

 The methodology used to conduct the analysis is straightforward. Under the 

hypothesis that commodity index investing has no effect on the underlying futures prices, 

the expected futures return of the nearby contract over the interval from the close on the 

                                                           

15 For other commodity futures contracts, for example, the metal contracts traded on the London Metals 

Exchange, contract months are not always the same. 
38 

day before the first roll date (i.e., the fifth business day of the month) to the close on the 

last roll date (i.e., the ninth business day of the month) should be equal to the expected 

futures return of the second nearby contract. Under the alternative hypothesis that the 

commodity index roll has price impact in the futures market, the nearby futures return 

will be less than the second nearby futures return because of the selling pressure on the 

nearby contract and the buying pressure on the second nearby. Assuming the null 

hypothesis is rejected in favor of the alternative, we should also find that the price impact 

is larger the greater the amount of commodity index investing during the interval. 

 Table II-3 contains the results of the return tests by commodity. The returns are 

computed for the specific futures contracts rolled with the S&P-GSCI and DJ-UBSCI 

indexes. To understand the contents of the table, consider the wheat contract in the first 

row. Of the wheat futures contract rolls that occurred during the period January 2006 

through July 2009, the average return of the nearby futures contract (being rolled from) 

was –0.03% from the settlement on the fourth business day of the roll month to the 

settlement on the ninth day. Over the same interval of time, the average return on the 

second nearby contract (being rolled into) was 0.06%. Thus, the return differential is 

0.09%, less than one-tenth of one percent. Scanning down the column of return 

differentials for the different commodity futures, we find that all but one (soybeans) is 

positive, and three are significant in the statistical sense. In a practical sense, however, 

the roll returns and return differentials are not economically meaningful, on order of the 

typical bid/ask spreads observed in these markets.16 

What is so remarkable about finding little or no price impact in these commodity 

futures rolls is the sheer size of the futures positions being rolled. To measure the number 

of contracts being rolled, we use the lesser of (a) the number of nearby contracts sold 

(i.e., the reduction in the open interest of the nearby contract from the fourth through the 

ninth business days) and (b) the number of second nearby contracts purchased (i.e., the 

increase in the open interest of the second nearby contract from the fourth through the 

ninth business days). We then divide this number by the open interest of the nearby and 

second nearby futures contracts at the beginning of the roll period, and then average the 

                                                           

16 On average, a cost of one-half of the bid/ask spread unwinding the nearby futures contract and one-half 

the bid/ask spread buying the second nearby futures contract is expected on each roll. 
39 

ratios through time to get the results reported in Table II-3. For wheat, the number of 

contracts rolled increased the open interest of the second nearby futures contract by 

46.6%, and the futures price rose ever so slightly on average. For soybeans, 17.9% of the 

open interest of the first nearby contract was closed out and the futures price rose. Across 

the eight commodity futures reported in the table, the roll activity increased the open 

interest of the second nearby contract by an average of 39.21%. The last column of Table 

II-3 places the transaction size in a different manner. Specifically, the number of 

contracts being rolled is multiplied by the contract denomination and the futures price to 

determine the notional value of the trades. The values are high. For the CBT’s wheat 

futures contract, about $708 million of contracts are being rolled and the return 

differential is 0.09%. For soybeans, an average of about $1.1 billion of contracts is being 

rolled and the return differential is –0.16%. In all, these commodity futures markets 

absorbed $5.2 billion of trades over five days. Clearly, the futures market has an 

enormous capacity to absorb commodity index roll activity. 

Table II-3: Average nearby and second nearby futures contracts returns on commodity 

index roll dates during the period January 2006 through July 2009. Returns are computed 

over the interval from the fourth through the ninth business days each roll month. Futures price 

data are from the CBT, CME, CSC, and NYC. The return differential is defined as the second 

nearby return less the nearby futures return. An asterisk (*) denotes that the return differential is 

significant at the 5% probability level. 

Commodity Ticker No. of Nearby Second Return Nearby Second Notional

futures symbol rolls contract nearby differential contract nearby value

Wheat W 48 -0.0003 0.0006 0.0009 -0.2813 0.4664 707,528,382

Corn C 48 0.0008 0.0020 0.0012* -0.1806 0.2644 839,694,396

Soybeans S 48 0.0113 0.0098 -0.0016 -0.1787 0.3138 1,072,256,880

Cotton No.2 CT 39 -0.0026 -0.0026 0.0000 -0.3089 0.4358 488,603,550

Lean hogs LH 68 -0.0022 0.0015 0.0037 -0.2446 0.4528 342,776,056

Live cattle LC 58 -0.0023 -0.0002 0.0021* -0.2210 0.3759 646,944,744

Suger No.11 SB 39 -0.0013 0.0006 0.0019 -0.2759 0.3808 573,820,235

Coffee C KC 48 -0.0170 -0.0151 0.0019* -0.2953 0.4500 494,659,947

Average -0.0017 -0.0004 0.0013 -0.2483 0.3925 645,785,524

Total 5,166,284,190

Futures return Percent change in OI

 In order to qualify for inclusion in Table II-3, we required that the commodity 

futures be included in both the S&P-GSCI and DJ-UBSCI and that the commodity index 

rolls were from and to the same futures contract expirations so as to maximize the dollar 
40 

notional value of the commodity futures positions rolled in each roll period. Because the 

single commodity futures contract with the single largest presence in both the indexes 

(37.51% of the S&P-GSCI and 13.75% of the DJ-UBSCI) was eliminated as a result of 

index rolls being into different contract months (see Table I-1), the average returns of 

crude oil futures rolls within each index were measured separately. The results are 

reported in Table II-4. As the table shows, the notional value of the index rolls is 

extremely large, with the crude oil futures rolls of the S&P-GSCI index accounting for 

$4.1 billion in trading activity. In contrast, all eight commodity futures in Table II-3 

account for only $1 billion more. Interestingly, the return differential is positive and 

statistically significant for both indexes, despite the fact that the nearby futures return is 

(surprisingly) positive not negative. The size of the return differential for the S&P-GSCI 

oil futures is 26 basis points, larger than typical bid/ask spreads in the NYME crude oil 

futures market. Apparently the crude oil futures market shows the effects of price impact 

during the index roll period due to the sheer size of the notional value of the futures 

contracts being rolled. 

Table II-4: Average nearby and second nearby crude oil futures contract returns for the DJUBSCI and S&P-GSCI on commodity index roll dates during the period January 2006 

through July 2009. Indexes are listed separately since crude oil futures rolls do not involve the 

same contract months. Returns are computed over the interval from the fourth through the ninth 

business days each roll month. Futures price data are from the NYME. The return differential is 

defined as the second nearby return less the nearby futures return. An asterisk (*) denotes that the 

return differential is significant at the 5% probability level. 

Commodity Ticker No. of Nearby Second Return Nearby Second Notional

index symbol rolls contract nearby differential contract nearby value

DJ-UBSCI CL 57 0.0092 0.0147 0.0055* -0.0728 0.2512 957,223,557

S&P-GSCI CL 115 0.0066 0.0092 0.0026* -0.3360 0.5340 4,097,800,039

Futures return Percent change in OI

The price impact hypothesis also carries with it an assumption that the greater the 

amount of index investing the greater the price impact. To test whether there is a relation 

between the return differential and the amount of index investing, we regress the return 

differential on the number of contracts traded as part of the roll, that is, 

R Roll t CIT t t = α01 , + + α ε
41 

where Rt is the return differential and R CIT t, oll is the number of nearby futures contracts 

rolled into the second nearby contract. The results are reported in Table II-5. As the table 

shows, the slope coefficients vary randomly around 0 and are not significantly different 

from 0 for the eight futures contracts with common contract rolls. For these contracts, the 

magnitude of commodity index investing, at least as measured by the roll activity of the 

S&P-GSCI and DJ-UBSCI, appears to have no impact on futures prices. At the same 

time, the slope coefficient in the crude oil futures regression is positive and significant, 

indicating that the notional value of the roll varies directly with the relative futures price 

change. 

E. Causation tests 

 Considering the dollar value of commodity futures contracts trading hands in a 

concentrated period of time, the roll-period results are quite compelling. It is important to 

recognize, however, that the roll-period evidence is based on the rolling of existing longonly commodity index investment, not on new flows into long-only commodity index 

investment. Since we can measure flows into commodity index investment using 

differences in the long-only commodity index trader open interest reported in the CFTC’s 

weekly Commitment of Trader Supplemental reports, we have the opportunity to conduct 

a second analysis, complementary to the first analysis, of whether inflows in commodity 

index investing “cause” futures prices to rise. 

To provide a general sense for the analysis that we are about to conduct, consider 

Figure II-6. In the figure, the total notional value of the net commodity index investment 

in the CBT’s wheat futures contract in USD billions17 is shown in blue. Shown in red is 

the price of the CBT’s nearby wheat futures contract during the same period of time. As 

the figure shows, commodity index investment in wheat increased during the first few 

months of 2006, at which time there is little or no increase in the wheat futures prices. 

Commodity index investing in wheat then falls through the summer of 2006, at which 

time the futures price rises from about $4.00 a bushel to $5.50 a bushel. From the end of 

2006 through the summer of 2007, the wheat futures price increases at an alarming rate 

                                                           

17 The notional amount is computed as the net commodity index trader positions times the contract 

denomination times the futures price on January 3, 2006. 
42 

from $5.00 a bushel to over $9.00 a bushel—a whopping 80%! In the meantime, the level 

of commodity index investing in wheat falls from $3.3 billion to $3.0 billion. Other than 

the simultaneous decline in the commodity index investing and the wheat futures price in 

late 2008, there is little evidence to suggest any relation between commodity index 

investing and the futures price. The price behavior of wheat was erratic during the period 

from mid-2007 through the end of 2008; however, such bouts of volatility have been 

recorded in the wheat market for many years, well before the advent of commodity index 

investing. 

Table II-5: Summary of results for regressions of the return differential of the 

nearby futures contracts over the roll period on the number of contracts rolled 

during the period January 2006 through July 2009. Regression specification is 

R Roll t CIT t t = α01 , + + α ε

where Rt is the futures returns and R CIT t, oll is the number of nearby futures contracts 

rolled into the second nearby contract. For the crude oil futures, the two commodity 

indexes are listed separately since crude oil futures rolls do not involve the same contract 

months. Returns are computed over the interval from the fourth through the ninth 

business days each roll month. Futures price and open interest data are from the CBT, 

CME, CSC, NYC, and NYME. The return differential is defined as the second nearby 

return less the nearby futures return. The number of contracts rolled is the lower of the 

reduction in open interest of the nearby contracts and the increase of the open interest in 

the second nearby contract expressed in millions of contracts. 

Commodity Ticker No. of Adjusted

futures/index symbol rolls α0 α1 R2

Wheat W 48 0.00053 0.01294 -0.0161

Corn C 48 0.00088 0.00519 -0.0162

Soybeans S 48 -0.00336 0.06594 -0.0082

Cotton No.2 CT 39 0.00179 -0.09844 -0.0185

Lean hogs LH 68 0.00365 0.00336 -0.0151

Live cattle LC 58 0.00183 0.01388 -0.0172

Suger No.11 SB 39 0.00552 -0.07462 0.0111

Coffee C KC 48 0.00237* -0.03208 -0.0034

DJ-UBSCI crude oil CL 57 -0.00608 0.71844* 0.1829

S&P-GSCI crude oil CL 115 -0.00473 0.10730* 0.0713

Figures such as II-6 are useful in uncovering potential causality between two 

time-series. In the end, however, formal statistical tests are necessary. Here we use the 

Granger (1969) causality test to determine whether commodity index investing activity 
43 

(i.e., changes in open interest) causes futures price changes, as the subcommittee report 

concludes, and/or changes in futures prices cause changes in commodity index investing 

activity. The data underlying the analysis are the 12 different commodity futures 

contracts followed in the CFTC’s COT Supplemental report.18 The number of weekly 

CIT long open interest observations for each of the 12 commodities in the period January 

2006 through July 2009 is 184. 

Figure II-6: Total notional value of net open interest of commodity index traders in 

CBT’s wheat futures contracts and wheat futures price by week during the period 

January 2006 through June 2009. CIT data are from weekly CFTC Commitments of 

Trader Supplement files and futures prices are from the CBT. 

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0

200

400

600

800

1000

1200

1400

20060103 20061030 20070826 20080621 20090417

USD billions

Price in cents per bushel

Futures price Net OI CITs

 To determine whether inflows into commodity index investment “Grangercauses” futures returns, we perform two regressions. In the first, we regress futures 

returns on lagged futures returns, that is, 

R RR t t tt α0 11 2 2 αα ε = ++ + − − , 

and, in the second, we regress futures returns on lagged futures returns and lagged flows 

into commodity index investment, that is, 

R R R Flow Flow t t t CIT t CIT t t α0 1 1 2 2 3 ,1 4 ,2 αα α α ε =+ + + + + −− − − , 

                                                           

18 The significance of the Granger’s work is attested to by the fact he received the Nobel prize in economics 

in 2003.
44 

where Rt is the return of the futures contract and FlowCIT t, is the flow into commodity 

index investment in week t. If the addition of the lagged flow variables adds explanatory 

power, the flow variable “Granger-causes” the futures return. Then, to determine whether 

futures returns “Granger-cause” flows into commodity index investment, we perform two 

additional regressions. In the first, we regress commodity index investment flows on 

lagged flows, that is, 

Flow Flow Flow CIT t CIT t CIT t t , 0 1 ,1 2 ,2 α αα ε =+ + + − − , 

and, in the second, commodity index investment flows on lagged flows and lagged 

futures returns, that is, 

Flow Flow Flow R R CIT t CIT t CIT t t t t , 0 1 ,1 2 ,2 3 1 4 2 α α α αα ε =+ + + + + − − −−

If the addition of the lagged futures returns adds explanatory power, the futures returns 

“Granger-cause” flows into commodity index investment. 

The results of the Granger-causality tests are reported in Table II-6. Reported in 

the table are F-statistics (and their associated probability levels) corresponding to the 

hypothesis that commodity index investment flows “Granger-cause” futures returns and 

the hypothesis that futures returns “Granger-cause” commodity index investment flows. 

A probability level of less than 5% denotes Granger-causality. As the results indicate, 

there is scant evidence of causality in either direction. The only commodity for which 

commodity index investment flows “Granger-cause” futures returns is for cotton. But, the 

importance of this result is offset by the fact that, for Kansas City wheat, futures returns 

“Granger-cause” commodity index investment flows. Overall, the results of Table II-6 

refute the notion that investment flows affect futures prices.
45 

Table II-6: Granger causality tests of commodity index investing and futures returns using 

weekly changes in long open interest of COT Supplemental reports during the period 

January 2006 through July 2009. Long open interest from COT includes all open interest in any 

commodity index strategy on a weekly. Futures price data are from the CBT, KCBT, CME, CSC, 

and NYC. The asterisk (*) denotes significance at the 5% probability level. 

Commodity Ticker 

futures symbol F-statistic Probability F-statistic Probability

CBT wheat W 0.1701 0.844 0.5231 0.594

KCBT wheat KW 0.1591 0.853 3.1552 0.045*

Corn C 2.0958 0.126 2.0731 0.129

Soybeans S 2.7465 0.067 2.4341 0.091

Soybean oil BO 2.6072 0.077 0.5804 0.561

Cotton CT 5.8921 0.003* 0.2688 0.765

Live hogs LH 1.8891 0.154 0.3461 0.708

Live cattle LC 1.2080 0.301 0.2827 0.754

Feeder cattle FC 0.3767 0.687 0.2532 0.777

Cocoa CC 1.8098 0.167 1.3481 0.262

Sugar SB 0.1582 0.854 0.7935 0.454

Cotton KC 0.3346 0.716 0.5083 0.602

Flows to commodity index investment Futures returns

"Granger-cause" "Granger-cause"

futures returns flows to commodity index investment

Finally, it is worth noting that the notional values of the weekly flows into and out 

of commodity index investment pale by comparison to the roll-period flows. Table II-7 

reports both the average weekly flow into commodity index investment and the average 

absolute flow into commodity index investment by commodity. For the CBT’s wheat 

futures contract, the average flow was –$4.7 million, which means that each week during 

the sample period January 2006 through July 2009, commodity index investors pulled out 

an average of $4.7 million. This is consistent with Figure II-6, which shows the notional 

value of the open interest in wheat trending downward over the period. More important, 

perhaps, is the average absolute flow. On average, $84.7 million of CBT wheat futures 

flowed into or out of commodity index investment each week. Compare this with the 

$789 million reported in Table II-3 that flowed from the nearby futures to the second 

nearby futures over the roll period (about one week). And, compare the $134.1 million of 

CBT corn futures that flowed into or out of commodity index investment each week in 

the Granger-causality tests with the $1.2 billion that flowed from the nearby futures to the 

second nearby futures over the roll period. While both the roll-period and Granger-
46 

causality tests refute the notion of that commodity index investment flows “causes” 

futures price changes, the roll-period tests remain the most compelling.

Table II-7: Notional value of flows into commodity index investment based on 

weekly changes in open interest of commodity index traders during the period 

January 2006 through July 2009. 

Commodity Ticker Average Average

futures symbol flow absolute flow

CBT wheat W -4,724,614 84,709,738

KCBT wheat KW -403,232 21,840,000

Corn C -8,343,614 134,112,115

Soybeans S 3,897,936 123,718,218

Soybean oil BO 552,977 34,461,380

Cotton CT 2,131,847 47,204,598

Live hogs LH 1,162,131 43,650,751

Live cattle LC 6,165,122 55,355,084

Feeder cattle FC 613,472 11,258,944

Cocoa CC 1,401 158,379

Sugar SB 4,414,789 68,182,154

Cotton KC 3,105,938 37,226,547

Average 714,513 55,156,492

Weekly commodity index investment

F. Analysis of contemporaneous relation between returns and flows 

 With causality ruled out, we now turn to examining the contemporaneous relation 

between returns and flows using the CFTC’s COT Supplemental report data. While 

examining the contemporaneous relation between variables cannot determine causality, it 

does help characterize the relation between futures returns and the demands of 

speculators and commodity index investors to shed some light on the subcommittee 

report claim that commodity index investing has led to a permanent increase in the level 

of futures prices, and, through arbitrage between markets, asset prices. If the 

subcommittee report’s conclusion is correct, futures returns should be positively 

correlated with commodity index inflows but independent of commodity index outflows. 

In investigating such a relation, however, it is imperative to recognize that returns may 

also be correlated with the demands of other market participants, particularly speculators. 

To test the subcommittee report conclusion, we therefore perform the regression, 
47 

R Flow Flow d d Flow d Flow t Spec t CIT t t t Spec t t CIT t t =+ + + + + + α01 , 2 , 3 4 , 5 , α α αα α ε

where Rt is the futures returns and FlowSpec t, and FlowCIT t, are the weekly net inflows of 

speculators and commodity index traders as designated by the CFTC’s Supplemental 

report, respectively. If the inflows of speculators and commodity index traders are related 

to price increases and outflows are related to price reductions, the coefficients α1 (for the 

flows of speculators) and α2 (for the flows of commodity index investors) should be 

positive. But, these variables alone do not address the asymmetry in the relation. The 

subcommittee report concludes that it is only inflows by commodity index traders that 

matter. Outflows should have no effect on prices. To account for this asymmetry, we 

need to distinguish between commodity index inflows and outflows. We do this by using 

a dummy variable that takes on a value of 1 when , 0 FlowCIT t < and is 0 otherwise. In the 

event that the relation is symmetric, the coefficient α5 should be equal to 0, reflecting no 

asymmetry between inflows and outflows. In the event that commodity index trader 

inflows increase price but commodity index outflows do not, the coefficient α5 should 

have a value approximately equal to −α2 . 

Table II-8 contains the results of the regression for each of the 12 commodities in 

our sample. A number of interesting results emerge. First, the only relation that shows up 

as being consistent is the relation between the net flows of speculators and futures 

returns. Its coefficient α1 is positive and significant for all 12 commodities. The fact that 

the coefficient α4 is generally insignificant means that the relation between speculator 

net flows and returns does not depend on the direction of trading by commodity index 

traders. Second, the coefficient α2 varies in sign and is insignificant in all but two cases. 

This suggests that, after controlling for the effects of speculator demand, commodity 

index investor net flows have no relation to futures returns. Moreover, the fact that the 

coefficient α5 is insignificant across commodities means that there is no asymmetry in 

the effect of commodity index investor demand and futures returns. 
48 

Table II-8: Regressions of weekly futures returns on speculator and commodity index trader 

flows during the period January 2006 through July 2009. Regressions are performed by 

commodity using 183 time-series return observations. Regression specification is 

R Flow Flow d d Flow d Flow t Spec t CIT t t t Spec t t CIT t t =+ + + + + + α01 , 2 , 3 4 , 5 , α α αα α ε

where Rt is the futures returns and FlowSpec t, and FlowCIT t, are the weekly net inflows of 

speculators and commodity index traders as designated by the CFTC’s Supplemental report, 

respectively. The dummy variable, t d has a value of 1 when 0 FlowCIT < and is 0 otherwise. 

Ticker

symbol α0 α1 α2 α3 α4 α5 Adj. R2

W 0.005071 0.000227* 0.000095 -0.002073 0.00002 0.000078 0.3161

KW 0.009945 0.000438* -0.000071 -0.005979 -0.00012 0.000615 0.2228

C 0.015031* 0.000088* -0.000071 -0.016562 0.000069* 0.000115 0.4136

S -0.000471 0.000066* 0.000120* 0.008761 0.000001 0.000050 0.4494

BO 0.013171* 0.000171* -0.000053 -0.026055* 0.000024 -0.000103 0.3043

CT 0.008555 0.000113* -0.000015 -0.026466* -0.000010 -0.000164 0.2725

LH 0.000419 0.000157* 0.000054 0.012021 0.000017 0.000271 0.0996

LC -0.000022 0.000052* 0.000018 0.004957 -0.000007 0.000100 0.1716

FC -0.001975 0.000131* 0.000072 0.004513 -0.000028 0.000039 0.0818

CC 0.006443 0.027634* 0.054791 -0.001315 -0.005591 0.059701 0.2631

SB -0.003738 0.000089* 0.000028 0.016122 0.000053 0.000059 0.2330

KC 0.000818 0.000147* 0.000147* -0.004099 -0.000011 -0.000053 0.5782
49 

III. Wheat Futures Market 

The subcommittee report claims that commodity index investing not only has 

elevated the level of commodity prices in general but also has caused basis convergence 

problems in the CBT’s wheat market. The causality test results reported in the last section 

refute the former claim. The purpose of this section is to investigate the wheat 

convergence issue. The section has four parts. First, we correct two methodological flaws 

in the way the futures basis is measured in the subcommittee report. Not only is the cash 

price proxy used incorrect theoretically and biased downward empirically, but also the 

reported basis is inflated as a result of using non-delivery periods when the futures price 

should exceed the cash price. Nonetheless, after correcting for the methodological 

deficiencies, there is some evidence to suggest that the wheat futures price did not always 

converge in the 2006-2009 period, particularly in late 2008. Second, we examine the 

CBT’s wheat convergence over a period of time much longer than that used in the 

subcommittee report and show that wheat has failed to converge in periods when the 

amount of commodity index investing is known to be negligible. Third, we examine the 

convergence behavior of the CBT’s corn and soybean futures contracts over the same 

historical period and find that, while neither corn nor soybeans have as great of 

divergence as wheat (corn is close), grain commodity futures in general seem to 

experience convergence anomalies at the same points in time. Finally, and most 

importantly perhaps, we address the issue whether the failure of the wheat futures price to 

converge to the cash price has any meaningful economic consequences. We show that 

there is no evidence to suggest that the CBT’s wheat futures has become a less effective 

hedging tool. 

A. Basis measurement 

The “futures basis” or, simply, “the basis” is defined here as the futures price less 

the price of the underlying cash commodity. In a properly-functioning market with 

rational investors, the basis should converge to zero as the futures contract approaches 

expiration. The reason is simple. The futures contract is a binding agreement to deliver 

the underlying commodity at the futures expiration date at the futures price. If the futures 

price is above the cash price at expiration, a risk-free profit equal to the difference 
50 

between the futures and cash price can be earned by buying the cash commodity, selling 

the futures, and then delivering the commodity against its futures contract. If, on the other 

hand, the futures price is below the cash price, a risk-free profit equal to the difference 

between the cash price and the futures price can be earned by buying the futures, selling a 

forward contract to sell the cash commodity, and taking delivery on the futures contract 

to meet its forward obligation. The absence of “free-money” opportunities ensures prices 

converge. 

That is not to say, however, that the futures price must equal the cash price before 

expiration. Early in its life, the futures price can be thought of as the expected cash price 

at a future point in time. Since the cash market conditions in the future can differ from 

present conditions, the futures price may be quite different from the current cash price. 

As time passes and the futures contract nears its expiration date, the link between the 

futures price and the cash price becomes stronger as market participants begin to actively 

arbitrage between the futures and the underlying cash commodity. 

The subcommittee report analyzes the basis convergence of the CBT’s wheat 

futures contract and finds that, in recent years, the basis does not converge and that the 

futures price expires above the underlying cash price. At first blush, this finding flies 

against reason in that costless arbitrage should generate a risk-free profit—sell the 

futures, buy the cash commodity, and then deliver the commodity against its futures 

contract. Upon closer examination of the subcommittee report’s methodology for 

computing the basis, it becomes obvious that the failure to converge is driven, at least in 

part, by (a) using an inappropriate cash price, and (b) measuring the basis at times other 

than the delivery period when the futures price should exceed the cash price. 

1. Appropriateness of cash price 

The CBT’s wheat futures contract calls for the delivery of U.S. No. 2 Soft Red 

Wheat at one of a number of delivery locations including Chicago, Burns Harbor Indiana, 

Ohio river, Northwest Ohio (at a discount of 20 cents), and Mississippi River (at a 

premium of 20 cents).19 The price of the same grade of wheat at the different locations 

                                                           

19 See CBOT Rules and Regulations, Chapter 14. http://www.cbot.com/cbot/pub/page/0,3181,931,00.html
51 

will vary depending on local supply and demand conditions. Costless arbitrage governs 

the range of prices, however. If the cash price of wheat in Chicago is $3 a bushel and the 

cash price of the same grade of wheat in Toledo is $2.50, arbitragers will buy the wheat 

in Toledo, ship it to Chicago, and sell it at $3, thereby earning an arbitrage profit of $.50. 

The transportation cost of shipping the wheat from Toledo to Chicago would have to be 

factored in. Assuming that transportation costs are $.10 a bushel, an arbitrage profit of 

$.40 remains possible. Arbitrage activity will continue until the Chicago and Toledo 

wheat prices deviate by no more than the transportation cost. 

The subcommittee report attempts to circumvent the problem of identifying the 

appropriate cash price of the deliverable grade of wheat by using an index price of U.S. 

No. 2 Soft Red Wheat (SRWI) disseminated by Minneapolis Grain Exchange (MGEX). 

To understand why the SRWI is not an appropriate measure for the price of the cash 

commodity underlying the CBT’s wheat futures contract, we need to understand how the 

SRWI is computed. 

The SRWI is one of seven of grain cash price indexes created by the MGEX to 

serve as the underlying asset of cash-settled futures and options contracts. The seven 

daily spot price indexes for wheat, corn, and soybeans are: 

 Index Symbol

National corn index NCI 

National soybean index NSI 

Hard red winter wheat index HRWI 

Soft red winter wheat index SRWI 

Hard red spring wheat index HRSI 

Durum wheat index DWI 

Soft white wheat index WWI 

Each spot index is calculated daily and is the simple arithmetic average of posted elevator 

bids. The SRWI, for example, currently includes more than 600 bid price quotes for U.S. 

No. 2 Soft Red Wheat collected from elevators in 20 different states. Table III-1 contains 

the percent of the total number of elevators accounted for by each state as of July 8, 2008. 

Ohio and Illinois are highest, with 27.3% and 26.1% of total, respectively. Indiana, 

Missouri, Michigan and Wisconsin also have shares that are 5% or higher. Among these 

600 cash prices are only a handful that represent locations specified for delivery on the 
52 

CBT’s wheat futures contract. Of the prices at the different delivery locations, the 

appropriate one is the lowest, after accounting for transportation costs. The difference 

between that price and the SRWI can be significant and unpredictable, as we will 

demonstrate shortly. 

Table III-1: Percent of total number of elevators surveyed by state in the 

calculation of the MGEX’s Soft Red Wheat Index (SRWI) as of July 8, 2008.

Data are from MGEX web link, 

http://www.mgex.com/documents/SRWImap071608.pdf. 

Percent No. of

No. State of total elevators

1 Ohio 27.3% 156

2 Illinois 26.1% 149

3 Indiana 12.4% 71

4 Missouri 9.8% 56

5 Michigan 5.8% 33

6 Wisconsin 5.6% 32

7 Kentucky 3.5% 20

8 Arkansas 2.3% 13

9 Louisiana 1.4% 8

10 Georgia 1.4% 8

11 Tennessee 1.1% 6

12 Nine other states 3.3% 19

 To gauge the size and direction of the error resulting from the use of the SRWI in 

the subcommittee report, we collect daily cash prices for two delivery locations specified 

in the CBT’s wheat futures contract—West Chicago Terminal Elevators and West Toledo 

Terminal Mills—and compare them with the daily levels of the SRWI. The cash prices 

were obtained from the United States Department of Agriculture (USDA). Figure III-1 

plots the difference between the Chicago cash price and the SRWI as well as the Toledo 

cash price and the SRWI during the period January 3, 2000 through July 15, 2009.20 The 

price differences are quite remarkable. First, both the Chicago and Toledo price 

differences reveal that the SRWI is a downward biased estimate of the cash price of the 

wheat deliverable on the CBT’s contract. In general, the price difference are greater than 

0, indicating that the SRWI is too low and will give the appearance that there is no 

                                                           

20 The starting date was determined by the availability of historical data for the SRWI. 
53 

convergence when there, in fact, may be. Second, there is considerable variation in the 

price differences through time. Naturally, this variation obfuscates the meaning of the 

convergence behavior documented in the subcommittee report. Since the SRWI is neither 

a tradable commodity nor a commodity deliverable on the CBT’s wheat futures contract, 

it should not be used as a cash market proxy for a deliverable grade of wheat. 

Figure III-1: Daily price difference between Chicago wheat cash price and the 

SRWI and the Toledo wheat cash price and the SRWI during the period January 3, 

2000 through July 15, 2009. Data are from MGEX website and USDA. 

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

20000103 20020102 20040102 20060101 20080101

USD dollars per bushel

Chicago Toledo

Table III-2 provides more detail regarding the price differences through time. The 

results show that over the period January 3, 2000 through July 15, 2009, the cash price of 

deliverable cash commodities, that is, Chicago wheat and Toledo wheat, are 16.5 cents 

and 14.5 cents higher than the SRWI, respectively.21 Not surprisingly, the price 

differences are not uniformly higher for Chicago wheat versus Toledo wheat. While 

Chicago wheat tends to be higher, Toledo wheat is higher in 2004 and 2005 as well as 

2007 and 2008. In these years, Chicago wheat was cheaper to deliver than Toledo wheat 

ignoring transportation costs. Nevertheless, the price differences are uniformly positive 

across years, indicating that the use of the SRWI as a cash market proxy will overstate 

the size of the futures basis. It is hardly surprising, therefore, that the subcommittee 

                                                           

21 Part of the difference may be attributable to the fact that SRWI is based on bid prices rather than trade 

prices. 
54 

report, which uses the SRWI as a proxy for cash wheat, finds “... consistently elevated 

futures prices relative to the cash market.” In addition, the subcommittee report finds that, 

“... since 2006, the difference between Chicago wheat futures prices and cash prices has 

steadily increased.” (p. 114). This, too, is hardly surprising considering that the 

divergence between the cash prices of the deliverable commodities and the SRWI has 

increased in recent years. Assuming the purpose is to gauge actual basis convergence, the 

prices of tradable, deliverable cash commodities must be used.22

Table III-2: Average daily cash prices and cash price differences for Chicago wheat, Toledo 

wheat, and the SRWI during the period January 3, 2000 through July 15, 2009. Data are from 

MGEX website and USDA. 

No. of Chicago Toledo SRWI Chicago Toledo

Year days cash price cash price level less SRWI less SRWI

2000-2009 2,382 3.8299 3.8097 3.6645 0.1654 0.1452

2000 251 2.3168 2.1596 2.1278 0.1889 0.0317

2001 248 2.5823 2.4778 2.3863 0.1960 0.0916

2002 250 3.2124 3.1808 3.0657 0.1467 0.1151

2003 250 3.3991 3.2732 3.1814 0.2177 0.0918

2004 249 3.3594 3.3911 3.2957 0.0637 0.0954

2005 251 3.0124 3.0701 2.9911 0.0213 0.0790

2006 250 3.5835 3.4663 3.4504 0.1331 0.0159

2007 249 5.8495 6.0032 5.6707 0.1789 0.3326

2008 250 6.7493 6.9058 6.4612 0.2881 0.4446

2009 134 4.5954 4.4923 4.3281 0.2673 0.1641

Price differences

                                                           

22 Indeed, the fact that the SRWI is not tradable is likely the reason that the cash-settled wheat futures 

markets launched by the MGEX failed. In principle, cash-settled contracts should be more successful than 

delivery contracts like the CBT’s wheat futures. Costs of delivery (e.g., transportation costs) are avoided 

since the futures contract is simply marked-to-market at the cash index level at expiration. But, history has 

shown that cash-settled futures thrive only where some set of market participants can actively trade the 

underlying index and arbitrage between the futures and cash markets. Indeed, the idea of program-trading 

of the stocks underlying the S&P 500 index emanated from the desire to arbitrage between the markets. In 

the case of the SRWI, the underlying commodity basket is not practically tradable. Buying or selling one 

dollar of wheat in 600 delivery locations is hardly practical, even for the biggest grain merchants in the 

marketplace. Without active arbitrage between the markets, there is no assurance that the cash-settled 

futures is an effective hedging vehicle, and, without the presence of hedgers in the marketplace, futures 

contract markets die on the vine. 
55 

2. Timing of measurement 

Aside from using an incorrect proxy for the price of deliverable wheat, there is 

another issue that inflates the level of the basis reported in the subcommittee report. 

Specifically, in the subcommittee report, convergence is measured on a daily basis 

throughout the calendar year by subtracting the cash price from the nearby futures price.23

For most days during the year, however, the nearby futures price will lie above the cash 

price due to the carry costs of the underlying asset. Only during the delivery period (i.e., 

the first two weeks of the contract month) should the futures price equal the cash price. 

To clarify this point, recall that the CBT’s wheat futures contract has only five 

contract months in a given year—March, May, July, September, and December. This 

means that we are allowed only five short opportunities to measure convergence each 

year—during the delivery periods (i.e., the first two weeks) of the March, May, July, 

September and December futures contracts. On all other days during the year, the 

difference between the nearby futures contract and the cash price (i.e., the basis) should 

be different from 0. Since the analysis in the subcommittee report uses all days during the 

year, we should expect to see positive basis. 

To examine the convergence issue, we compute the average daily basis for the 

CBT’s wheat futures contract for each contract month during the sample period January 

2000 through July 2009. Note the delivery period for the wheat futures contract begins 

the first business day of the contract month, and the last day of trading for the contract is 

the business day before the 15th calendar day of the contract month, so the average basis 

is an average across about 10 days. Since we do not know the cheapest-to-deliver 

location for each contract expiration, we use the cash price of No. 2 Soft Red Winter 

Wheat deliverable in Chicago. The results are displayed in Figure III-2. 

                                                           

23 See subcommittee report (2009, p.116). 
56 

Figure III-2: Absolute basis between CBT’s wheat futures contract and the cash 

price of wheat deliverable in Chicago during the period January 3, 2000 through 

July 15, 2009. Futures data are from CME and cash data are from USDA. Average basis 

is computed over the daily levels observed during the contract delivery period. 

-0.50

0.00

0.50

1.00

1.50

2.00

2000 2002 2004 2006 2008 2010

USD dollars per bushel

Figure III-2 is directly comparable to Figure 26 on page 116 of the subcommittee 

report. The critical differences are that we are using the cash prices of the commodities 

that can actually be delivered on the CBT’s wheat futures contract and only intervals of 

time when the futures price should converge to the cash price. As expected, the basis 

shown in Figure III-2 is lower than that shown in the subcommittee report as a result of 

the downward bias of the cash proxy (i.e., the SRWI) discussed earlier and the inflated 

basis during non-delivery periods. Where the basis spiked at $2.25 per bushel in 2008 in 

the subcommittee report, our results show a level closer to $1.50. Our results are more 

comparable to Irwin, Garcia, Good, and Kunda (2009), who also use a deliverable grade 

of wheat to perform the basis computation. In place of taking an average of the basis over 

the delivery period, they use the basis on the first delivery date. And, in place of using 

Chicago wheat as the cash price, they use the price of wheat deliverable in the Toledo 

area. In summary, wheat futures prices do not appear to have converged in 2008, 

although the degree of divergence is not nearly as exaggerated as it appears in the 

subcommittee report. 
57 

B. History of convergence 

An inference drawn in the subcommittee report is that the lack of convergence in 

the wheat market in the 2006-2009 period, particularly in late 2008, is driven by an 

increase in commodity index investing. While we documented no increase in commodity 

index investing of the CBT’s wheat futures contract during the period, one could argue 

that the most dramatic increase in commodity index investing took place just prior to 

2006 and we are only seeing its effects registered now. A simple way of addressing this 

issue is to examine basis behavior over a longer history. In Figure III-3, we examine the 

basis behavior of wheat during delivery periods for all contracts traded during January 

1992 through July 2009. Because the dollar price of wheat varied dramatically over the 

sample period, we measure basis relative to the futures price on the last day of trading in 

order to gauge the levels on a common footing. Because we do not know for certain 

which delivery location is cheapest-to-deliver, we use two cash prices of wheat—Chicago 

and Toledo. 

Focusing in on Chicago wheat, note the following. First, the relative basis is fairly 

erratic throughout the sample period. From 1992 through 1998, the basis at expiration 

bounces between 0% and 10%. It then proceeds to increase, reaching a maximum level of 

22.6% for the September 1999 contract delivery period. From that point, the relative basis 

at contract expiration falls back down and hovers at a level just above 0%. Subsequently, 

it rises and spikes at 16.9% for the September 2006 contract and 21.6% for the September 

2008 contract. Based on the figure, it is fair to say that convergence has been an issue in 

the wheat market dating back at least to 1992. Generally, the level of basis stays within 

the 0-10% range; however, periodic spikes for the September contracts are noteworthy. 

Given that commodity index investing had a relatively small presence in the marketplace 

before 2004 and virtually no presence in the marketplace in the early 1990s, commodity 

index investing cannot be the cause of the basis instability noted throughout the period. 
58 

Figure III-3: Relative basis between CBT’s wheat futures contract and Chicago and 

Toledo wheat cash prices during the period January 1992 through July 2009.

Futures data are from CBT and cash prices are from USDA. Average basis is computed 

over the daily levels observed during the contract delivery period. Futures price for 

computing relative basis is the settlement futures price on the last day of trading. 

-10%

0%

10%

20%

30%

40%

50%

1992 1994 1996 1998 2000 2002 2004 2006 2008

Percent basis

Chicago Toledo

Figure III-3 is also useful in demonstrating the concept of cheapest to deliver. The 

average basis during the delivery period varies differently through time for Chicago and 

Toledo wheat. In some months, Chicago wheat has a basis closer to 0, while, in other 

months, Toledo does. The cheapest to deliver is the delivery location whose basis is 

closest to 0. So, where the basis spikes up for the September 2006 and September 2008 

contract expirations, it is of no relevance to assessing convergence. Convergence is only 

relevant for the cheapest to deliver commodity, and, in both of these months, Chicago 

wheat is cheaper. In addition, there is no assurance that Chicago wheat is cheapest. 

Delivery at a number of other locations is possible. The Chicago and Toledo cash prices 

were chosen because they are frequently the cheapest to deliver locations. 

C. Inter-commodity comparisons 

Several other agricultural commodities are included in the popular commodity 

indexes like the S&P-GSCI and DJ-UBSCI. The CBT’s corn futures, for example, has 

weights of 3.55% and 5.72% in the two indexes, respectively, while the CBT’s wheat 

futures has weights of 3.90% and 4.80%. To gauge whether the same type of basis 
59 

behavior has occurred for corn as for wheat, we examine basis convergence for all 

contract maturities from January 1992 through July 2009. The CBT’s corn futures 

contract calls for delivery in Chicago, Burns Harbor Indiana, Lockport Seneca (at a 

premium of 2 cents), Ottawa-Chillicothe (at a premium of 2.5 cents), and Peoria-Pekin (at 

a premium of 3 cents). Since we do not know the cheapest-to-deliver location through 

history, we choose three cash prices in and around the Chicago area—Chicago, Illinois 

River North of Peoria, and Illinois River South of Peoria. 

Figure III-4 shows the relative basis for the CBT’s corn futures in the delivery 

periods from January 1992 through July 2009. The observed basis behavior for corn is 

different in at least two ways from that of wheat—the cash prices at the delivery locations 

are very similar, as indicated by the fact the three lines are on top of each other in many 

contract months, and the range of oscillations is lower, with most contract months falling 

in the 0-10% range. But, in other ways, they remain similar. They do oscillate from 

delivery month to delivery month, and the degree of variation in the oscillations changes 

in similar ways through time, with modest variation occurring in 1992 through 1998, high 

variation occurring in 1999 through 2001, low variation in 2002 through 2005, and high 

variation again in 2006 through 2009. While convergence appears to be less of an issue 

for corn than for wheat, it also appears that these two agricultural markets are driven by 

similar market factors and the relative basis behavior is not entirely commodity specific. 

The CBT’s soybean futures is another agricultural commodity typically included 

in diversified commodity indexes. Its weights are 2.64% and 7.60% in the S&P-GSCI 

and DJ-UBSCI indexes, respectively. The CBT’s soybean futures contract calls for 

delivery in Chicago, Burns Harbor Indiana, Lockport Seneca (at a premium of 2 cents), 

Ottawa-Chillicothe (at a premium of 2.5 cents), Peoria-Pekin (at a premium of 3 cents), 

Havana-Grafton (at a premium of 3.5 cents), and St Louis and Alton (at a premium of 6 

cents). 
60 

Figure III-4: Relative basis between CBT’s corn futures contract and the cash 

prices of corn deliverable in (a) Chicago, (b) Illinois River North of Peoria, and (c) 

Illinois River South of Peoria during the period January 3, 2000 through July 15, 

2009. Futures data are from CBT and cash data are from USDA. Average basis is 

computed over the daily levels observed during the contract delivery period. Futures 

price for computing relative basis is the settlement futures price on the last day of 

trading. 

-10%

0%

10%

20%

30%

40%

50%

1992 1994 1996 1998 2000 2002 2004 2006 2008

Percent basis

Chicago North Peoria South Peoria

To gauge whether the basis behavior of soybeans is different from that of wheat 

and corn, we examine basis convergence for all soybean futures contract expirations 

during the period January 1992 through July 2009. Three cash prices are again used, with 

delivery locations in Chicago, Illinois River North of Peoria, and Illinois River South of 

Peoria. Figure III-5 shows the results. The soybean basis behavior shows greater 

convergence than both wheat and corn. The relative basis hovers just above 0 through 

most of the period. Again, the behavior is a little more erratic in 1999 through 2001 and 

in 2006 through 2009, suggesting a market-wide effect for grain commodities in 

general.24

                                                           

24 Recall that in Section II we showed the same seasonal behavior for the CBT’s oats futures contract, and 

oats in not included in commodity index investing programs. 
61 

Figure III-5: Relative basis between CBT’s soybean futures contract and the cash 

prices of soybeans deliverable in (a) Chicago, (b) Illinois River North of Peoria, and 

(c) Illinois River South of Peoria during the period January 3, 2000 through July 15, 

2009. Futures data are from CME and cash data are from USDA. Average basis is 

computed over the daily levels observed during the contract delivery period. Futures price 

for computing relative basis is the settlement futures price on the last day of trading. 

-10%

0%

10%

20%

30%

40%

50%

1992 1994 1996 1998 2000 2002 2004 2006 2008

Percent basis

Chicago North Peoria South Peoria

D. Economic consequences 

Up to this point, we have focused in on the issue of convergence in the wheat 

futures market, and, to be sure, there are instances in time, both before and after the 

introduction of commodity index investment, that the futures price exceeded the cash 

price of the deliverable commodity during the delivery period. But, the importance of the 

convergence issues presupposes that failure to converge has dire economic consequences. 

The subcommittee report certainly suggests that there are. 

“The increasing gap between the futures and cash prices (basis), together 

with the failure of convergence, have seriously impaired (emphasis added) 

the ability of farmers, grain elevators, grain merchants, grain processors, 

and others in the agriculture industry to use the Chicago wheat futures 

market to manage and reduce the price risks arising from their operations 

in the wheat market.” (See subcommittee report (2009, p. 113).) 

But, this claim is patently false. The fact of the matter is that convergence is of limited 

importance considering that most of these risk managers unwind their futures positions 

before the delivery period. The important issue is whether or not there is evidence to 

indicate that the Chicago wheat futures has become a less effective hedging vehicle. 
62 

To begin our assessment of the economic consequences of Chicago wheat’s 

failure to converge, we turn to the daily open interest of the CBT’s wheat futures 

contracts during the period January 1992 through July 2009. For each contract month 

during this period, we identify the maximum daily open interest during the contract’s life 

and the open interest on the first notice day of the contract (i.e., the last business day in 

the month preceding the delivery month. In Figure III-6, we plot these values. The results 

are quite revealing. While the maximum levels of open interest were high in 2006 and 

2007, at least relative to the levels observed before 2005, the open interest on the first 

notice day has remained relatively constant through time. Table III-3 contains the average 

open interest on the first notice day across contract months for the CBT’s wheat futures 

contract as well as the CBT’s corn and soybean futures contracts. As the table shows, the 

average number of futures contracts carried into the delivery month was 5,284 during the 

sub-period 1999-2004 and 5,583 in 2005-2009. These are small numbers, at least in a 

relative sense. The average ratio of open interest on the first notice day to the maximum 

open interest over the contract’s life over the period January 2005 through July 2009 is 

only 2.9%. This means that 97.1% of risk managers (e.g., farmers, grain elevators, grain 

merchants, grain processors, and others in the agriculture industry) have unwound their 

hedge positions before the delivery month.25 In other words, for the vast majority of 

wheat price risk managers, the issue of convergence is moot. 

What is relevant to the risk manager is the effectiveness of the CBT’s wheat 

futures at hedging commodity price risk over his/her hedging horizon. In practice, 

hedging effectiveness is measured by the adjusted R-squared from a regression of cash 

commodity returns on futures returns.26 The adjusted R-squared has a range from 0 to 1. 

A value near 0 indicates that the futures is an ineffective hedging tool, and a value near 1 

indicates that it is a very effective tool. 

                                                           

25 Of course, 100% of commodity index investors closed their positions weeks before the delivery month. 

26 See Whaley (2006, Ch.5). 
63 

Figure III-6: Maximum open interest and open interest on first notice day for 

CBT’s wheat futures contracts during the period January 1992 through July 2009.

Futures data are from CBT. 

0

50,000

100,000

150,000

200,000

250,000

300,000

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Open interest

Maximum First notice day

Table III-3: Average open interest of first notice day relative to maximum open 

interest over contract life and average open interest on first notice day for selected 

agricultural futures contracts during the period January 1999 through July 2009.

Futures data are from the CBT. 

Panel A: Open interest on first notice day

Subperiod Wheat Corn Soybean

1999-2004 5,284 22,611 11,299

2005-2009 5,583 26,638 12,591

Panel B: Open interest on first notice day relative to maximum open interest

Subperiod Wheat Corn Soybean

1999-2004 0.0667 0.1109 0.1581

2005-2009 0.0293 0.0557 0.1014

To assess whether the CBT’s wheat futures contract has become a less effective 

hedging vehicle in recent times, as is suggested by the subcommittee report, we examine 

the returns of cash wheat and wheat futures over all contract expirations during the period 

January 2000 through July 2009. To proxy for the cash price of wheat, we use the daily 

levels of the SRWI from the MGEX. The hedge horizon is set at 126 business days 

(about 6 months), and the hedge period ends on the first notice day of the futures contract 

month. The futures price data are from the CBT. For each futures contract expiration 
64 

during the sample period, we regress cash returns on futures returns and record the 

adjusted R-squared. Table III-4 summarizes the results. The table entries are the average 

adjusted R-squared values of the contract months in each year. As the table shows, the 

CBT’s wheat futures contract has been a highly effective hedging instrument throughout 

the 10-year period. For the sub-period 2006-2009, the adjusted R-squared is 0.949, which 

means that 94.9% of cash commodity price risk can be eliminated using the wheat futures 

contract. 

Table III-4: Average adjusted R-squared level of regression of daily cash wheat 

returns on daily wheat futures returns by contract month during the period 

January 2000 through July 2009. Cash returns are calculated from SRWI levels and 

were obtained from the MGEX website. Futures returns are calculated from CBT prices. 

Return regressions are for the last 126 business days before and including the contract’s 

first notice day. 

Adjusted

Subperiod R-squared

1999-2004 0.943

2005-2009 0.949

 
65 

IV. Summary of Main Conclusions 

The subcommittee report concludes that excessive speculation by commodity 

index investors has caused unwarranted increases in the price of wheat futures and has 

seriously impaired the contract’s effectiveness at being an effective risk management 

tool. This study questions the legitimacy of this conclusion and reaches three main 

conclusions. 

1) Commodity index investment is not speculation. 

Commodity index investment is passive, fully-collateralized, long-only 

investment by an institution or individual and is no different in principle from a stock 

index or bond index portfolio. Its fundamental contribution to investment management is 

in providing an effective diversification tool. 

2) Commodity index rolls have little futures price impact, and inflows and outflows 

from commodity index investment do not cause futures prices to change 

 The price of a commodity reflects the cost of supplying that commodity and the 

demand for it by consumers. Changes in the cost of production or in demand change the 

price. The futures price reflects the spot price expected in the future and hence reflects 

the supply and demand anticipated for the commodity. For commodities that are stored, 

arbitrage assures that the spot and futures price are linked. We conduct six analyses to 

determine whether investment in commodity futures, sometimes in large amounts, diverts 

futures prices from their fundamental value. The first argues that, if index traders were 

the dominant force in the commodity futures market, the prices of all futures contracts in 

the index would rise or fall together. We show that the correlation in futures returns is 

neither high nor uniform. In the second, we show that commodities not in an index are 

correlated with commodities in the index approximately to the same degree as 

commodities in the index are correlated with each other, which suggests that fundamental 

forces, not index investing, is the source of the correlation. Third, commodity prices rose 

in 2006 and 2007. If the increase was due to index investing, one would not expect a 

similar rise for commodities not in an index. The prices of coal, cobalt and rhodium—

commodities not in an index—also rose in price, however, which suggests the price rise 
66 

cannot be the result of commodity index investing. Fourth, commodity index investors 

that mimic the S&P-GSCI and DJ-UBSCI roll out of the nearby contract and into the next 

contract according to a known schedule. Given the hundreds of millions of dollars in 

futures trades being consummated at this time, this would be the most likely time to see 

price impact, both in the sale of the nearby futures and the purchase of the second nearby. 

The positive, but economically insignificant, price effects observed suggest that the 

futures markets are deep and fully capable of absorbing commodity index investment 

rolls for most commodity futures markets. A separate analysis for crude oil futures—the 

commodity futures with the single largest notional value in the indexes (but not rolled in 

the same way within the indexes)—shows a positive and significant return differential. 

Fifth, we analyze in a Granger causality framework the relation between investment 

flows of index traders as reported in the Supplement to the COT reports and subsequent 

price changes. There is no evidence that investment flows Granger-cause price changes 

or that price changes Granger-cause flows. Sixth, the Granger causality tests examine 

weekly lag effects, which may be too coarse a measure to see an impact. We also look at 

a contemporaneous relation between commodity futures returns and flows under the 

assumption that inflows have a different price effect than outflows. We find no indication 

that commodity index traders affect prices in this framework. Other traders, classified as 

speculators, do have an impact. 

3) Failure of the wheat futures price to converge to the cash price at the contract’s 

expiration has not undermined the futures contract’s effectiveness as a risk 

management tool. 

The subcommittee report concludes that commodity index investing is a major 

cause in the failure of the CBT’s wheat futures price to converge in the period 2006-

2009, with the futures price being particularly elevated in late 2008. What is surprising 

about this conclusion is that commodity index investing in wheat was actually falling, not 

rising, in 2008. To understand the CBT’s wheat price convergence more fully, we use a 

period of time much longer than that used in the subcommittee report and show that 

wheat has failed to converge in periods when the amount of commodity index investing is 

known to be negligible. We also examine the convergence behavior of the CBT’s corn 
67 

and soybean futures contracts over the same historical period and find that, while neither 

corn nor soybeans have had as great of divergence as wheat, grain commodity futures in 

general seem to experience convergence anomalies at the same points in time. Finally, we 

address the issue whether the failure of the wheat futures price to converge to the cash 

price has any meaningful economic consequences. We find none. For convergence to be 

an issue, significant numbers of futures contracts must be carried into the delivery month. 

In the period 2005-2009, only about 5,000 contracts remained open on the first notice day 

of the delivery month, less than 3% of the maximum open interest that the contract 

realized during its life. In other words, 97% of the risk managers who had been using the 

wheat futures to hedge have disposed of their positions before convergence becomes an 

issue. The cash price for delivering this small amount is subject to the vagaries of the 

delivery mechanism and the option of the short to choose the grade, location, and exact 

time of delivery. The apparent failure to converge does not reduce the effectiveness of the 

CBT’s wheat futures contract as a risk management tool, however, for we show that 

futures returns are highly correlated with the returns of a typical grade of wheat. 
68 

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