1

Index Investment and Financialization of Commodities  

Ke Tang† and Wei Xiong‡

Abstract 

This paper finds that concurrent with the rapid growing index investment in 

commodities markets since early 2000s, futures prices of different commodities in 

the US became increasingly correlated with each other and this trend was 

significantly more pronounced for commodities in the two popular GSCI and DJUBS commodity indices. This finding reflects a financialization process of 

commodities markets and helps explain the synchronized price boom and bust of 

a broad set of seemingly unrelated commodities in the US in 2006-2008. In 

contrast, such commodity price comovements were absent in China, which refutes 

growing commodity demands from emerging economies as the driver. 

 The synchronized rise and fall in prices of oil and a broad set of non-energy commodities in 

2006-2008 has stimulated increasing public attention to commodities markets. Figure 1 depicts 

the price appreciations of oil, wheat, soybeans, copper, cotton, and live cattle since 1991. In 

particular, there is heated debate in policy circles about whether speculation caused unwarranted 

increases in the cost of energy and food and induced excessive price volatility. Policy makers in 

the US and various European countries are actively considering measures to curb speculation. 

There are two opposing views. One of them attributes the boom-and-bust cycle to a simple 

matter of supply and demand, while the other stressing excessive speculation by index investors. 

According to the first view (e.g., Krugman (2008), Hamilton (2009), and Kilian (2009)), the 

rapid growth of emerging economies such as China propelled the quick increase of world 

demands and caused commodity prices to soar before the summer of 2008. Prices later fell 

sharply when the world recession caused demands to fade. The second view attributes the large 

volatility of commodity prices to distortions caused by large investment flow into commodity 

                                                           



 First draft: September 2009; this version: August 2010. We wish to thank Nick Barberis, Alan Blinder, Markus 

Brunnermeier, Ing-Haw Cheng, Campbell Harvey, Zhiguo He, Han Hong, Alice Hsiaw, Ralph Koijen, Pete Kyle, 

Arvind Krishnamurthy, Tong Li, Burt Malkiel, Bob McDonald, Lin Peng, Geert Rouwenhorst, Mark Watson, Philip 

Yan, Moto Yogo, Jialin Yu, and seminar participants at Commodity Futures Trading Commissions, Duke/UNC 

Asset Pricing Conference, Federal Reserve Bank of San Francisco, Kellogg School, NBER Summer Institute 

Workshop on Capital Markets and the Economy, Princeton University, and University of Texas-Dallas for helpful 

discussion and comments. 

† Renmin University of China, Email: ketang@ruc.edu.cn.

‡ Princeton University and NBER, Email: wxiong@princeton.edu. 
2

indices. According to a CFTC staff report (2008) and Masters (2008), the total value of various 

commodity index-related instruments purchased by institutional investors has increased from an 

estimated $15 billion in 2003 to at least $200 billion in mid-2008. A recent report by the US 

Senate Permanent Subcommittee on Investigations (2009) argues that the dramatic index 

investment flow had distorted prices of some commodities such as wheat. 

Despite the great public attention on the large increase of commodity price volatility in recent 

years, the concurrent economic transition of commodities markets precipitated by the rapid 

growth of index investment in commodities has gone unnoticed. Prior to early 2000s, despite 

liquid futures contracts traded on many commodities, commodity prices behaved differently 

from that of typical financial assets. Commodity prices provided risk premium for idiosyncratic 

commodity price risk (e.g., Bessembinder (1992) and de Roon, Nijman and Veld (2000)); and 

commodities had little price comovements with stocks (e.g., Gorton and Rouwenhorst (2006)) 

and with each other (e.g., Erb and Harvey (2006)). These aspects are in sharp contrast to the 

price dynamics of typical financial assets, which carry premium only for systematic risk and are 

highly correlated with market indices and with each other. This contrast indicates that 

commodities markets were partially segmented from outside financial markets and from each 

other. 

The tide changed in early 2000s, when the collapse of equity market in 2000 and the widely 

publicized discovery of a small negative correlation between commodity returns and stock 

returns led to a belief that commodity futures could be used to reduce portfolio risk. This belief 

allowed investment banks to successfully promote commodity futures as a new asset class for 

prudent investors. As a result, various instruments based on commodity indices have attracted 

billions of dollars of investment from institutional investors and wealthy individuals. The 

increasing presence of index investors precipitated a fundamental process of financialization 

amongst commodities markets, through which commodity prices became more correlated with 

prices of financial assets and with each other. In this paper, we analyze the effects of this 

financialization process. 

 We focus on the increased price comovements between different commodities after 2004, 

which is roughly the time when significant index investment started to flow into commodities 

markets, to identify the effects of growing commodity index investment. As index investors 

typically focus on strategic portfolio allocation between the commodity class and other asset 

classes such as stocks and bonds, they tend to trade in and out of all commodities in a chosen 

index at the same time (e.g., Barberis and Shleifer (2003)). As a result, their increasing presence 

should have a greater impact on commodities in the two most popular commodity indices – the 

Goldman Sachs Commodity Index (GSCI) and Dow-Jones UBS Commodity Index (DJ-UBS) –

than those off the indices. Consistent with this hypothesis, we find that futures prices of non-
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energy commodities became increasingly correlated with oil after 2004. In particular, this trend 

was significantly more pronounced for indexed commodities than for those off the indices. While 

this trend intensified after the world financial crisis triggered by the bankruptcy of Lehman 

Brothers in September 2008, its presence was already evident and significant before the crisis. 

There is also evidence of an increasing return correlation between commodities and Morgan 

Stanley emerging market equity index in recent years. This confirms the increasing importance 

of commodity demands from rapidly growing emerging economies in determining commodity 

prices. However, a closer comparison of commodity futures prices in China – the growth engine 

of emerging economies in the 2000s – with the synchronized boom-and-bust cycle in the US 

uncovers a sharp contrast. In 2006-2008, while futures prices of some commodities heavily 

imported by China, such as heating oil, copper, and soybeans, did experience similar rise and fall 

as those in the US; the prices of some others such as wheat, corn and cotton did not exhibit any 

pronounced cycle. Furthermore, the average return correlation among different commodities in 

China did not display any significant increase in recent years either. Taken together, demands 

from China may have contributed to the price boom and bust of some commodities, but unlikely 

to all commodities at the same time. 

Price comovements among different commodities had also been high in 1970s and early 

1980s. When the US economy was hit by persistent oil supply shocks and stagflation, the 

double-digit inflation rate and accompanied large inflation volatility coincided with a period of 

high return correlations among commodities (with an average around 0.3). In contrast, the 

increases of commodity return correlations in late 2000s were not only larger in magnitude (with 

an average correlation over 0.5) but also different in nature. They emerged when inflation and 

inflation volatility remained subdued throughout 2000s, and thus inviting explanations other than 

inflation. 

As a result of the financialization process, the price of an individual commodity is no longer 

simply determined by its supply and demand. Instead, commodity prices are also determined by 

a whole set of financial factors, such as the aggregate risk appetite for financial assets, and 

investment behavior of diversified commodity index investors. On one hand, the presence of 

these investors can lead to a more efficient sharing of commodity price risk; on the other hand, 

their portfolio rebalancing can spill over price volatility from outside to commodities markets 

and also across different commodities (e.g., Kyle and Xiong (2001)). While the data sample after 

2004 may be too short to give a reliable measure of changes in commodity risk premia, we are 

able to systematically examine the effects of growing index investment on commodity price 

volatility and comovements. 

Overall, our analysis shows that return correlations of commodities with stocks, the US 

dollar, and with each other have significantly increased in recent years. Volatility spillover has 
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also contributed to the large price volatility of commodities in 2008, during which indexed nonenergy commodities had larger price volatility than those off-index ones; this difference was 

partially related to the greater return correlations of indexed commodities with oil. These 

changes in commodity price dynamics have profound implications for a wide range of issues 

from commodity producers’ hedging strategies and speculators’ investment strategies to many 

countries’ energy and food policies. We expect these effects to persist as long as index 

investment strategies remain popular among investors. 

Our emphasis on price comovements of commodities is distinct from those in the literature 

on returns and risk premia of commodities, e.g., Fama and French (1987), Bessembinder (1992), 

de Roon, Nijman, and Veld (2000), Erb and Harvey (2006), Gorton, Hayashi, and Rouwenhorst 

(2007), Hong and Yogo (2009), and Acharya, Lochstoer, and Ramadorai (2009). These papers 

focus on the roles of macroeconomic risk, producers’ hedging incentives, and commodity 

inventories in determining cross-sectional and time-series properties of commodity risk premia. 

Our analysis corroborates with Pindyck and Rotemberg (1990) who find that common macro 

shocks cannot fully explain comovements in commodity prices between 1960 and 1985. In 

contrast to their study, our analysis focuses on connecting the large inflow of commodity index 

investment to the large increase of commodity price comovements in recent years by examining 

the difference in these comovements between indexed and off-index commodities. This 

identification strategy builds on the finding of Barberis, Shleifer and Wurgler (2005) that after a 

stock is added to S&P 500 index, its price comovement with the index increases significantly. 

Several recent papers, e.g., Buyuksahin, Haigh and Robe (2009) and Silvennoinen and Thorp 

(2010), also find that return correlation between commodities and stocks has gone up 

substantially during the recent financial crisis but not before. Different from these studies, our 

analysis highlights that the increase in commodity return correlations started long before the 

crisis and cannot be simply attributed to the crisis. Instead, we identify the role of index investors 

in linking different commodities markets with each other and with outside financial markets. On 

the latter dimension, our paper complements Etula (2009), who shows that the risk-bearing 

capacity of securities brokers and dealers is an important determinant of risk premia and return 

volatility in commodities markets. 

The paper is organized as follows. Section I provides some background information about 

commodities and commodity indices. Section II documents the increasing return correlations 

among different commodities in recent years. We discuss several economic mechanisms 

including the financialization process of commodities markets for explaining these increases in 

Section III, and examine these mechanisms in Section IV. Section V discusses volatility spillover 

caused by commodity index investment and Section VI concludes the paper. 
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I. Commodities and Commodity Indices 

We focus on commodities with active futures contracts traded in the US. There are 28 such 

commodities available in recent years. We obtain daily futures prices and open interests of these 

commodities from Pinnacle Data Corp.1

 Table 1 lists and classifies these commodities in five 

sectors: energy, grains, softs, livestocks, and metals.2 

The energy sector contains 4 commodities: WTI (West Texas Intermediate grade) crude oil, 

heating oil, gasoline, and natural gas. 3 Crude oil is the most important component in this sector 

as heating oil and gasoline are refined oil products, whose prices move closely with crude oil. 

The grain sector contains 9 commodities: corn, Chicago wheat, Kansas wheat, Minneapolis 

wheat, soybeans, soybean oil, soybean meal, rough rice, and oats. These grains are substitutes for 

each other as food for humans and animals.4 The soft sector is a mix of tropics that are grown 

primarily in tropical and subtropical regions. There are 6 commodities in this sector: coffee, 

cotton, sugar, cocoa, lumber and orange juice. We follow the common practice to classify them 

in one sector although the links between the softs are not as close as the links between 

commodities in other sectors. There are four commodities in the livestock sector: feeder cattle, 

lean hogs, live cattle and pork bellies. These commodities are substitutes for each other and are 

primarily used for human consumption. The metal sector contains 5 commodities: gold, silver, 

                                                           

1

 Futures contracts were also offered on some other commodities but were later terminated. As our analysis focuses 

on price comovements rather than commodity returns, survivorship bias is not a concern. 

2

 See Geman (2005) for a comprehensive description of these commodity sectors and distribution of the global 

supply and demand of each of the commodities. 

3

 The New York Mercantile Exchange (NYMEX) offers futures contracts on each of them with expirations in every 

month of a year. The WTI crude oil contracts specify a type of light and sweet oil (with 38-40◦

 API and 0.3% sulfur) 

to be delivered at Cushing, Oklahoma. These contracts are heavily traded and their prices are widely used as 

benchmarks for determining the prices of crude oil of different grades and at different locations. The Brent crude oil 

contracts specify a similar grade of oil to be delivered at Shetland Islands, UK. Their prices move closely with those 

of the WTI contracts. The demand and supply fluctuations in the local markets of North America and Europe could 

also cause some variations between the prices of Brent and WTI contracts. We do not include the Brent contracts in 

our sample to avoid potential complications from asynchronous daily closing prices of different commodities 

between the US and London markets. 

4 Soybeans are crushed to produce meal and oil. The three forms constitute the so-called “soybean complex”, each of 

which underlies futures contracts traded on Chicago Mercantile Exchange (CME). Corn is mostly used as animal 

feed, competing with wheat and soybean meal. In the recent years, corn is also used in the U.S. for producing 

ethanol and other alternative fuels. Wheat is traded on three exchanges: the CME, the Kansas City Board of Trade 

(KCBOT), and the Minneapolis Grain Exchange (MGE). Chicago wheat is a soft winter wheat, grown primarily in 

the central states. It is a low-grade wheat mostly used as livestock feed or as flour for cheap bread. Kansas wheat is a 

hard, red, winter wheat, grown primarily in the southern states, and is used mainly for human food. Minneapolis 

wheat is the highest-grade wheat, planted in the northern states. Rice is the second largest crop in planting acreage 

across the world after wheat. It is primarily used for human consumption. While oats are suitable for human 

consumption as oatmeal and rolled oats, its primary use is as livestock feed.
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copper, platinum and palladium.5 They are used both as investments and as inputs for industrial 

production. 

An increasingly popular investment strategy in the recent years is to invest in a basket of 

commodities following a certain commodity index. A commodity index functions like an equity 

index, such as the S&P 500, in that its value is derived from the total value of a specified basket 

of commodities. Each commodity in the basket is assigned a specified weight. Commodity 

indices typically build on the values of futures contracts, which are typically nearby contracts 

with delivery time longer than one month,6

 to avoid the cost of holding physical commodities. 

When a first-month contract matures and the second-month contract becomes the first-month 

contract, a commodity index specifies the so-called “roll” – i.e., replacing the current contract in 

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

to passive long positions in listed commodity futures contracts. By far the largest two indices by 

market share are the S&P Goldman Sachs Commodity Index (GSCI) and the Dow-Jones UBS 

Commodity Index (DJ-UBS) 7. There is also a proliferation of other smaller indices operated by 

other institutions, such as the Rogers International and Deutsche Bank Liquid Commodity 

Indices. These indices differ in terms of index composition, commodity selection criteria, rolling 

mechanism, rebalancing strategy, and weighting scheme.8 

Table 1 provides the weights of the GSCI and DJ-UBS indices in the 28 commodities traded 

in the US. Both indices incorporate a wide range of commodity futures. There are some 

commodities in neither index: Minneapolis wheat, soybean meal, rough rice, and oats in the 

grain sector; lumber and orange juice in the soft sector; pork bellies in the livestock sector; and 

platinum and palladium in the metal sector. These two indices use different selection and 

weighting schemes: GSCI is weighted by each commodity’s world production, while DJ-UBS 

relies on the relative amount of trading activity of a particular commodity. As a result, 

commodities in these indices tend to be large in terms of world production and liquid in terms of 

trading in the futures markets. The composition of these indices is stable and has stayed the same 

                                                           

5 We exclude several popular metals that are only traded in London, such as aluminum, lead, nickel, zinc, and tin , to 

avoid potential complications from asynchronous daily closing prices of different commodities between the US and 

London markets. 

6

 As shown in Gorton and Rouwenhorst (2006) and Hong and Yogo (2009), commodity futures contracts often 

become illiquid in the delivery month. This is because many traders are reluctant to deliver or accept delivery of the 

physical commodities 

7 The Dow Jones-UBS Commodity Index was also known as the Dow Jones-AIG Commodity Index before 2009.  

8

 See AIA Research Report (2008) for a detailed account of construction methods of various commodity indices. 
7

in the recent years. Furthermore, the joint set of GSCI and DJ-UBS indices also covers almost all 

of the commodities in other less popular indices.9

The energy sector carries a much greater weight than the other sectors in the GSCI and DJUBS indices. The four energy commodities listed in Table 1 add up to 58% of the GSCI and 39.6% 

of the DJ-UBS. WTI crude oil alone accounts for 40.6% of the GSCI. Since the commodities in 

the energy sector move closely with each other, we will use crude oil as a focal point in our later 

analysis to study price comovements of non-energy commodities with oil. 

II. The Increased Price Comovements of Commodities 

In this section, we provide some preliminary analysis of the price comovements of individual 

commodities. We illustrate the increased return correlations among seemingly unrelated 

commodities in recent years by plotting one-year rolling return correlations between oil and a 

selected commodity from each of the four non-energy sectors: soybeans from the grain sector, 

cotton from the soft sector, live cattle from the livestock sector, and copper from the metal sector. 

These commodities give a broad representation of non-energy commodities. We then construct 

the average return correlation among commodities. 

Since centralized trading makes futures contracts more liquid than physical commodities, 

futures prices are available for a larger set of commodities compared with spot prices. Therefore 

we choose to focus on futures prices of commodities for the most part of our analysis. In Section 

IV.D, we will also analyze correlations of spot returns, which are available only for a smaller set 

of commodities. 

For each commodity, we follow Gorton and Rouwenhorst (2006) and Erb and Harvey (2006) 

to construct a return index from rolling the first-month futures contract. More specifically, we 

construct a hypothetical investment position in the first-month futures contract of the commodity 

                                                            9

 Besides directly taking long positions in individual commodity futures contracts, investors can use three types 

of financial instruments to gain exposure to the return of a commodity index: commodity index swaps, exchange 

traded funds, and exchange traded notes. See the recent report by US Senate Permanent Subcommittee on 

Investigations (2009) for a detailed description of these instruments. A commodity index swap is, in essence, a 

financial instrument that pays a return based on the value of a specified index. A swap dealer, such as a bank or 

broker-dealer, typically offers a qualified investor the opportunity to purchase, for a fixed price, a swap whose value 

is linked, on any given date, to the value of the specified commodity index on that date. After selling a swap contract, 

the swap dealer will typically hedge its own exposure to the swap contract by purchasing the corresponding futures 

contracts in the commodity index. In the past few years, financial institutions have devised another type of 

instrument, known as exchange traded funds (ETFs), to mirror the performance of specified commodity indices. 

Unlike commodity index swaps, which are bilateral transactions between investors and swap dealers, ETFs are 

traded in exchanges like stocks. An ETF is typically structured so that the value of the ETF shares should reflect the 

value of the specified commodity index. A third commodity-based instrument involves exchange traded notes 

(ETNs). ETNs are designed and sold by financial institutions to permit retail investors to purchase shares of a debt 

security whose price is linked to that of a commodity index. 
8

on a fully collateralized basis. We hold the contract until the 7th calendar day of its maturity 

month before rolling into the next contract.10 The excess return of this hypothetical investment 

on a non-rolling day represents the excess futures return to the initial capital (as we can still earn 

interest on the capital): 

ܴ௜,௧ ൌ ln൫ܨ௜,௧,்భ൯ െ ln൫ܨ௜,௧ିଵ,்భ൯

where ܨ௜,௧,்భ is the date-t price of the first-month futures contract of commodity i with maturity 

date ܶଵ. On a rolling day, not only does the return incorporate the futures price change, but also 

the price ratio between the first-month contract and the second-month contract. 

We normalize the daily excess return from investing in the commodity in each one-year 

rolling window by its average return and return volatility: 

ܴ௜,௧

௡ ൌ ൣܴ௜,௧ െ ݉݁ܽ݊ሺܴ௜ሻ൧/ݐݏ݀ሺܴ௜ሻ. 

We then regress the normalized return ܴ௜,௧

௡ onto the normalized oil return ܴ௢௜௟,௧

௡ : 

ܴ௜,௧

௧,௢௜௟ܴߩ ൅ ߙ ൌ௡ 

 .௧,௜ߝ ൅ ௡

The estimated coefficient ρ is the return correlation between the two commodities. 

Figure 2 depicts the one-year rolling return correlations of oil with soybeans, cotton, live 

cattle, and copper together with the 95% confidence interval. 11 Panel A shows that from 1986 to 

2004, the return correlation between soybeans and oil moved around zero inside a narrow range 

between -0.1 and 0.2. Between 2004 and late 2009, the correlation steadily climbed up from 0.1 

to near 0.6, and this trend is significantly different from zero. Similarly, Panels B, C, and D show 

that oil had small return correlations with cotton, live cattle, and copper before 2004, and that the 

correlations have gradually risen to 0.5, 0.4, and 0.6, respectively in 2009.12 We also plot the 

one-year rolling correlation between daily returns of GSCI energy and non-energy indices in 

Panel E. These indices track returns of GSCI commodities (which are listed in Table 1) in the 

energy and non-energy sectors. Their correlation gradually increased from around 0.1 in 2004 to 

                                                           

10 GSCI index is rolled from the fifth to ninth business day of each maturity month with 20% rolled during each day 

of the five-day roll period. DJ-UBS index works similarly. For simplicity, we uniformly specify one-day roll 

strategy on the 7th calendar of each maturity month for all commodities, including those off-index ones. 

11 Panels B, C, and D start in 1986 because trading of oil futures started only in March 1983. We skip the data in 

1983-1984 to avoid potential liquidity problems at the beginning and use returns after 1985 to measure correlations. 

With the one-year rolling window, our correlation measures start in 1986. Panel D starts in 1990 as trading of copper 

futures started only in January 1989. Panel E starts in 1983 because GSCI energy index is available only after 1982. 

12 Forbes and Rigobon (2002) point out that when volatility increases, return correlation can be a biased measure of 

the economic link between assets. We have also adopted the procedure proposed by them to adjust for such biases. 

The adjustment does not create any significant change to the return correlation plots. More importantly, we will 

directly test for changes in the links between non-energy commodities and oil by using formal regression analysis. 

In computing t-stats for testing the changes, we adjust for heteroskedasticity.
9

over 0.7 in 2009. Taken together, these plots show that return correlations of a broad set of nonenergy commodities with oil were small before 2004, which is consistent with the finding of Erb 

and Harvey (2006), but have been steadily increasing after 2004. 

To have a holistic view of return correlations among non-energy commodities and for the 

period back to 1970s, we construct an average return correlation for all commodities with futures 

contracts traded at a given time. As commodities in the same sector tend to have greater return 

correlations with each other than with commodities in other sectors, we need to avoid the 

potential bias caused by changes of commodity distribution across different sectors. We deal 

with this issue using the following method: For each sector, we construct an index which tracks 

the equal-weighted return of all available commodities. Then we compute the return correlations 

between these indices for all sector pairs, and take the equal-weighted average. To highlight the 

difference between commodities in and off the two popular commodity indices, we construct two 

return indices in each sector and calculate the average correlations separately for indexed and 

off-index commodities. We call a commodity “indexed” if it is in either the GSCI or DJ-UBS 

index, and “off-index” otherwise. 

Figure 3 depicts the average one-year rolling correlations of indexed and off-index 

commodities from 1973 to 2009. The plot illustrates several interesting features. The average 

correlation among indexed commodities stayed at a stable level below 0.1 throughout 1990s and 

early 2000s and was indistinguishable from that among off-index commodities. The mild 

increase in average correlation among off-index commodities to a level of 0.2 in 2009 is in sharp 

contrast to that among indexed commodities, which has climbed up to an unprecedented level of 

0.5. This difference in the increase in correlations between indexed and off-index commodities 

allows us to identify the effects of index investment later in our analysis. 

Figure 3 also shows that the average correlations of indexed and off-index commodities had 

been as high as 0.3 in 1970s. As we will discuss in Section III.D, this coincided with the wild 

inflation and inflation volatility during that period. The average correlations gradually declined 

below 0.1 in late 1980s as inflation and inflation volatility were eventually tamed. Interestingly, 

there were no pronounced differences between indexed and off-index commodities despite the 

high correlation levels in the 1970s. Furthermore, inflation and inflation volatility remained 

subdued even to date. The contrast between the high return correlations in 1970s and 2000s 

indicates that they were driven by different mechanisms. Our analysis focuses on understanding 

the latter period. 

III. Economic Mechanisms 

What caused the increases of return correlations among seemingly unrelated commodities in 

recent years? In this section, we discuss several possible economic mechanisms including 
10

growing commodity demands from emerging economies and the financialization process of 

commodities markets precipitated by the rapid growth of commodity index investment. 

A. Rapid Growth of Emerging Economies 

The rapid growth of China, India, and other emerging economies is a popular explanation for 

the recent commodity price boom (e.g., Krugman (2008), Hamilton (2009), and Kilian (2009)). 

The economic development of these emerging economies in 2000s stimulated unprecedented 

demands for a broad range of commodities in different sectors, such as energy and metals, and 

thus might have led to a joint price boom of these commodities. 

The commodity demands from the emerging economies depend positively on the strength of 

their economic growth and negatively on the price of the US dollar, which is widely used to 

settle commodity transactions. We use the Morgan Stanley emerging market equity index to 

proxy for the economic growth of emerging economies. This index tracks equity market 

performance of the global emerging markets. As of May 2005, this index consists of 26 

emerging economies: Argentina, Brazil, Chile, China, Colombia, Czech Republic, Egypt, 

Hungary, India, Indonesia, Israel, Jordan, Korea, Malaysia, Mexico, Morocco, Pakistan, Peru, 

Philippines, Poland, Russia, South Africa, Taiwan, Thailand, Turkey and Venezuela. This broad 

representation makes this index a good proxy for the economic growth of the global emerging 

economies. We use return of the US dollar index futures traded on ICE to track price fluctuations 

of the US dollar. The underlying of this futures contract is an index that weighs dollar exchange 

rates with six component currencies (euro, Japanese yen, British pound, Canadian dollar, 

Swedish krona and Swiss franc). We obtain data on these two indices from Bloomberg. 

Figure 4 depicts the one-year rolling correlation between daily returns of GSCI index and 

Morgan Stanley emerging market equity index. Before 2004, the correlation fluctuated mostly 

around zero, except that it dropped to a negative level of -0.4 during the Gulf war in 1990-1992. 

The war caused stock prices to fall and oil price to soar. Interestingly, after 2004 the correlation 

rose gradually from around 0 to above 0.5 in 2009. This increasing trend confirms an 

increasingly important effect of emerging economies on commodity prices in recent years. 

Figure 4 also shows a clear decreasing trend in return correlation between the GSCI index 

and US dollar index. Before 2004, this correlation fluctuated inside a narrow band between -0.2 

and 0.2. After 2004, it dropped steadily from around 0 to -0.4 in 2009. This trend is consistent 

with growing commodity demands from emerging economies. As we will discuss later, this trend 

is also consistent with increasing index investment flow into commodities markets from outside 

US. In our regression analysis later, we will formally examine the links of the GSCI index to the 

emerging market index and the US dollar index. We will also use the emerging market index to 
11

control for the effects of commodity demands from emerging economies in our analysis of price 

comovements of non-energy commodities with oil. 

Despite the important effects of emerging economies on commodity prices, it remains 

unclear whether they were the driver of the synchronized price boom and bust across the broad 

range of commodities in 2006-2008. To address this question, we collect futures prices of 

commodities traded in China, the growth engine of emerging economies in the 2000s, from Wind 

(a widely used vendor of financial data in China). China gradually introduced futures contracts 

on a small set of commodities since late 1990s. Table 1 lists these commodities and the starting 

dates of futures trading in China. Figure 5 depicts front-month futures prices for six commodities 

in China and the US.13 Panels A, B and C show that futures prices of heating oil, copper and 

soybeans in China had boom-and-bust cycles closely matched with the corresponding cycles in 

the US. These closely matched price dynamics are consistent with the heavy imports of these 

commodities by China. More interestingly, Panels D, E, and F show that the price dynamics of 

wheat, corn, and cotton in China are very different from those in the US.. In the US, these 

commodities experienced boom-and-bust cycles well synchronized with other commodities with 

peaks in early 2008. In contrast, their prices in China did not display any pronounced cycle. As 

China was not a major importer or exporter of wheat, corn, and cotton, the large (explicit or 

implicit) cost of transporting these commodities across the Pacific prevents effective arbitrage of 

price deviations between China and the US. However, the lack of price cycles for these 

commodities in China indicates that the synchronized price boom and bust in the US were not 

driven by demands from China. 

To compare commodity return correlations in China and the US, we pool together a sample 

of 8 commodities with futures contracts simultaneously traded in China and the US. These 

commodities include heating oil in the energy sector; corn, wheat and soybeans in the grain 

sector; cotton and sugar in the soft sector; and copper and gold in the metal sector. We match the 

front-month futures returns of these commodities with the corresponding ones in the US. We use 

the same procedure we used before to first construct an equal-weighted return index for each 

commodity sector in China and in the US based on all commodities available in China. We then 

compute an equal-weighted average of the one-year rolling correlations for all sector pairs in 

each of the countries. Figure 6 depicts the average commodity return correlations in China and 

the US from 2000 to 2009. These two correlations were roughly at the same levels around 0.1 in 

early 2000s. Interestingly, the average correlation in the US had increased steadily to a level 

above 0.5 in late 2000s, while the average correlation in China did not grow much and stayed 

                                                           

13 Commodity prices in China are settled in Renminbi. We normalize the price of each commodity in both China and 

US to be 100 at the beginning of its sample period. Renminbi had a steady appreciation of about 20% against dollar 

from 2005 to 2009. Adjusting the exchange rate fluctuation does not affect the price boom-and-bust cycles in the 

plots. The exchange rate has no effect on commodity price comovements in China either. 
12

below 0.2 throughout the same period. This contrast again refutes commodity demands from 

China as the driver of the large increase of commodity price comovements in the US. 

B. Financialization of Commodities 

The focus of our analysis is the new development in commodities markets – the large inflow 

of index investment in recent years. When equity market collapsed in 2000, the widely 

publicized discovery of a negative correlation between commodity returns and stock returns by 

Greer (2000), Gorton and Rouwenhorst (2006), and Erb and Harvey (2006) in the investment 

communities allowed Goldman Sachs and other indexers to successfully promote commodity 

futures as a new asset class for institutional investors. As a result, commodities markets attracted 

billions of dollars of investment from financial institutions, insurance companies, pension funds, 

foundations, hedge funds, and wealthy individuals. Figure 7 depicts the rapid growth in the open 

interest (total number of contracts outstanding with maturities less than one year) of various 

commodity futures after 2004. 

B.1. Index Investment Flow 

The Commodity Index Traders (CIT) report, released by the US Commodity Futures Trading 

Commission (CFTC) on each Friday, allows us to measure how much index investment has 

flowed into a set of commodities after 2006. The report shows positions of index traders, which 

include swap dealers, pension funds, and other investment funds that trade commodity indices 

for 12 agricultural commodities since 1/3/2006.14 These include corn, soybeans, Chicago wheat, 

Kansas wheat, and soybean oil from the grain sector; coffee, cotton, sugar, and cocoa from the 

soft sector; and feeder cattle, lean hogs, and live cattle from the livestock sector. This list 

coincides with the joint set of GSCI and DJ-UBS indices in these three sectors. The CIT report 

does not cover any commodities in the energy and metal sectors. 

The CIT report classifies the reportable market participants into three groups: commercial 

traders, index traders, and non-commercial traders. The CFTC identifies an individual reportable 

trader as commercial if the trader uses futures contracts in that particular commodity for hedging. 

The non-commercial traders include all reportable traders who are neither commercial nor index 

traders. The CIT report provides the aggregate long and short positions of each of the three 

groups in a particular commodity.15 

                                                           

14 The CIT report supplements the standard Commitments of Traders (COT) report, which is also released by the 

CFTC on the breakdown of every Tuesday’s positions on all exchange-traded futures and options on US-based 

exchanges. The COT report only classifies reportable traders to two categories, commercial and non-commercial. 

15 The CIT report also presents the non-commercial traders’ aggregate spreading positions, i.e., equal long and short 

futures position on the same commodity but with different maturities. 
13

Table 2 reports the average position size of each group of traders in each of the commodities 

based on the weekly CIT report from 1/3/2006 to 10/29/2009. The table shows that index traders’ 

long positions contribute to a substantial fraction of open interest of each of the commodities: an 

average of 28.4% across all the commodities in the sample, 42.4% of lean hogs and 41.6% of 

Chicago wheat respectively at the high end. Index traders’ short positions are minimal, with an 

average of 1.6% of open interest across commodities. 

We can construct the investment flow by index traders in and out of the 12 commodities in 

each week by summing up the dollar value of index traders’ net position change in each of the 

commodities: 

ܨܫ௧ ൌ ∑ ሺܰܮ௜,௧ െ ܰܮ௜,௧ିଵሻܲ௜,௧ିଵ

ଵଶ

௜ୀଵ (1) 

where ܰܮ௜,௧ represents the net long position of index traders in commodity i in week t and ܲ௜,௧ିଵ

is the price of the commodity in week t-1. In this calculation, we use prices of first-month futures 

contracts, and assume that all position changes occur during the previous week. Then we add up 

the index flow from the first week of 2006, the beginning of the CIT report data, to any week 

before 10/29/2009 to obtain the accumulated index flow to that week. 

Figure 8 depicts the accumulated index flow together with the GSCI agriculture & livestock 

excess return index. This index follows the performance of the same three sectors – grains, softs, 

and livestocks – as those covered by the CIT report. The figure shows that since the beginning of 

2006, these three sectors had a large net inflow which accumulated to nearly 20 billion dollars in 

early 2008. Then there was a stream of outflow, which led to an accumulated index flow of 

negative 5 billion dollars by March 2009. The figure also shows that fluctuations of the GSCI 

agriculture & livestock excess return index were in striking sync with the index flow. 

B.2. Economic Effects 

There is evidence suggesting that before early 2000s, commodities markets were partially 

segmented from outside financial markets and from each other. Erb and Harvey (2006) show that 

commodities had only small positive return correlations with each other; Gorton and 

Rouwenhorst (2006) show that commodity returns had negligible correlations with the S&P 500 

stock index return, especially at short horizons such as daily and monthly; Bessembinder (1992) 

and de Roon, Nijman and Veld (2000) find that returns of commodity futures increased with net 

short positions of commodity hedgers after controlling for systematic risk. These attributes 

contrast those of typical financial assets such as stocks, where prices carry premium only for 

systematic risk, and tend to have high return correlations with each other (even if they share little 

common fundamentals). 
14

The segmentation of commodities markets implies potentially inefficient sharing of 

commodity price risk, which is also consistent with the longstanding hedging pressure theory of 

commodity prices dating back to Keynes (1930), Hicks (1939), and more recently Hirshleifer 

(1988). This influential theory posits that commodity hedgers need to offer positive risk premium 

to induce speculators to share the idiosyncratic risk of the long positions they are endowed with. 

Since index investors tend to hold large diversified portfolios across different asset classes, their 

increasing presence is likely to improve the sharing of commodity price risk. However, as is well 

known, measuring risk premium requires a long sample period. The 5-year period currently 

available since 2004(roughly when significant index investment started to flow into commodities) 

is perhaps too short to identify the resulting change in commodity risk premium, which we will 

leave for future research. 

Trading of diversified index investors can act as a channel to correlate commodity prices 

with prices of other assets in their portfolios (e.g., Kyle and Xiong (2001)). The exact nature of 

such spillover effects depends on the index investors’ portfolio composition and rebalancing 

strategies. Since commodity index investors usually invest a large fraction of their portfolios in 

stocks, commodity prices are exposed to shocks to stocks. When a positive shock increases the 

weight of stocks in the investors’ portfolios, diversification incentives motivate them to move 

some money into commodities, and thus causing commodity prices to comove positively with 

stock prices. On the other hand, index investors’ strategic asset allocation from stocks to 

commodities or vice versa can also cause commodity prices to comove negatively with stock 

prices. Furthermore, the rapid growth of commodity index investment is a global phenomenon 

and a significant fraction of the investment flow comes from international investors who are 

exposed to shocks to the US dollar exchange rate. When the US dollar appreciates, the same 

commodity with prices in dollars becomes more expensive to international investors. As a result, 

their demands decrease and cause commodity prices to comove negatively with the US dollar 

exchange rate. We will further discuss these spillover effects in Section V. 

Our main identification strategy of the increasing presence of commodity index investors 

builds on the return correlations among different commodities. Index investors are not 

particularly sensitive to prices of individual commodities since they tend to move in and out of 

all commodities in their index at the same time based on the strategic allocation of their capital to 

commodities versus other asset classes such as stocks and bonds. As a result, any shocks to their 

strategic allocation to the commodity class can cause commodities in the index to move together 

(e.g., Barberis and Shleifer (2003)). In other words, we expect price comovements of 

commodities in the GSCI and DJ-UBS indices to be greater than those off the indices. Consistent 

with this theory, Barberis, Shleifer, and Wurgler (2005) find that in stock markets, addition to the 

S&P 500 index can significantly increase a stock’s return correlation with the index. Motivated 

by these studies, we focus on the difference between return correlations of indexed and off-index 
15

commodities with oil. We choose oil as a focal point because of its dominant weight in the two 

popular aforementioned commodity indices. In particular, we examine the following empirical 

hypothesis on the change in this difference after 2004: 

 After 2004, non-energy commodities in the GSCI and DJ-UBS indices had greater 

increases of return correlations with oil than those off the indices. 

An implicit assumption in this hypothesis is that other participants of commodities markets, 

such as traditional speculators, commodity producers, and commercial users only have a limited 

capacity to absorb trades of index investors. As a result, the increasing presence of index 

investors can affect commodity prices. It is also worth mention that potential substitutions 

between closely related commodities by consumers and producers can partially transmit the price 

impact of index investors to off-index commodities.16 For example, if prices of corn rise far 

above those of soybean meal, consumers will substitute soybean meal for corn to feed their 

animals, or vice versa. Similarly, if prices of corn rise far above those of oats, farmers will 

allocate more farmland to plant corn instead of oats. But these substitution effects are likely to be 

imperfect and operate at horizons longer than those of futures trading such as the daily horizon 

we focus on in this paper. 

The choice of the year 2004 as the break point is not important because our main results 

build on trends in return correlations between non-energy commodities and oil. While the data 

sample after 2004 may be too short for identifying changes in risk premium, the use of daily data 

allows us to reliably measure changes in return volatility and correlation. 

As mentioned before, commodities in the GSCI and DJ-UBS indices are selected based on 

their world production and trading liquidity in futures markets. Hence, the higher liquidity of 

indexed commodities works against this hypothesis because it is less likely for prices of more 

liquid commodities to be affected by trading of index investors. Liquidity might be a concern for 

off-index commodities because it can cause price fluctuations of off-index commodities to lag 

behind oil. We will account for this effect by introducing lags in our regression analysis later. 

One might argue that trading by index investors has a greater impact on commodities that 

carry a greater weight in the commodity indices. However, as their index weights, are matched 

by their greater world production and higher trading liquidity in futures markets by construction, 

we expect these commodities to be able to absorb more capital inflow and outflow. For this 

reason, we choose to focus on the difference in return correlations between commodities in and 

                                                           

16 See Casassus, Liu and Tang (2009) for a study of multi-commodity systems with production, substitution and 

complementary relationships. 
16

off the GSCI and DJ-UBS indices, rather than between commodities with greater and smaller 

weights in the indices. 

As the GSCI and DJ-UBS indices are built on rolling front-month futures contracts of 

individual commodities, most of our analysis focuses on returns from rolling these front-month 

futures contracts. A subtle issue is whether the growth of index investment has affected spot 

prices and futures prices of other maturities in the same way. This depends on the effectiveness 

of arbitrageurs in synchronizing spot prices and futures prices with different maturities. The 

standard textbook example on commodity carry trades works as follows: If the price of the frontmonth futures contract of a commodity becomes too expensive relative to its spot price after 

adjusting for interest cost and storage cost for carrying the commodity from now to the delivery 

date of the contract, an arbitrage opportunity emerges and the arbitrageur can short the contract 

while simultaneously carrying the commodity. Mismatches in the relative prices of futures 

contracts with different maturities can also lead to similar arbitrage opportunities. Thus, we 

expect arbitrageurs to spread the price impact of index investment from front-month futures 

contracts to spot prices and futures prices of other maturities if the interest cost and storage cost 

incurred in such carry trades are independent of growing index investment. In Section IV.D, we 

will separately examine the correlations of spot returns and slope changes of futures price curves. 

C. The World Financial Crisis 

It is well known that prices of financial assets tend to move together during financial crises. 

Could the recent increase of commodity return correlations be a simple reflection of the recent 

financial crisis? 

Figure 9 depicts the VIX index (i.e., Chicago Board Options Exchange Volatility Index), a 

widely used measure of equity market volatility derived from the implied volatility of S&P 500 

index options. The VIX index mostly stayed near its lowest level around 10% from 2004 to 2007. 

It gradually climbed up but nevertheless remained below 30% (a normal level from its past) in 

2007 and the first half of 2008. Only in September 2008, after the failure of Lehman Brothers, 

the VIX index suddenly shot up from 20% to near 70%. The dramatic rise of the VIX index is 

widely regarded as the indicator for the disruption of a full-scale crisis in the financial markets 

across the world. The VIX index declined below 30% in May 2009 as the crisis abated. 

The timing of the financial crisis did not coincide with the increase of commodity return 

correlations, which has already started in 2004 – long before the dramatic jump-up of the VIX 

index in September 2008. As a result, the financial crisis cannot fully explain the increase of 

commodity return correlations. In our regression analysis later, we will separately treat the precrisis period before September 2008 to isolate the effect of the crisis. 
17

On the other hand, the crisis also provides an extreme episode for us to examine the effects of 

financialization on commodities markets. If commodities markets were segmented from outside 

financial markets, we would not expect a crisis outside to have any significant effect on 

commodities markets. Figure 10 depicts the one-year rolling correlation between the GSCI and 

S&P 500 stock index. This figure illustrates a widely noted correlation increase: While this 

correlation stayed in a band between -0.2 and 0.1 for several years before 2008, it quickly 

climbed up from 0 to over 0.5 during the crisis and remained high even after the crisis abated in 

early 2009. 17 This largely increased correlation not only shows that commodities markets 

became more integrated with outside financial markets, but also suggests potential volatility 

spillover from outside to commodities markets through trading of index investors, which we will 

examine in Section V. 

D. Inflation 

Inflation is a common factor that drives prices of different commodities. Could the recent 

increase in commodity return correlations be driven by the increasingly important effects of 

inflation on commodity prices? 

Figure 11 depicts the annualized monthly CPI core inflation rate (the percentage change of 

Consumer Price Index excluding food and energy prices) and the one-year rolling volatility of 

the monthly CPI core inflation rate. We use the CPI core inflation rate to avoid the 

contamination of inflation measure by commodity prices. This inflation rate hovered near 10% 

throughout 1970s when the economy was hit by persistent oil supply shocks and stagflation. The 

inflation rate remained high around 5% during the 1980s. It was eventually tamed in 1990s and 

remained low at 2 to 3% levels throughout late 1990s and 2000s. The volatility of the inflation 

rate has a similar pattern as the inflation rate. It was often above 5% in 1970s and early 1980s, 

and remained above 3% from early 1980s to early 1990s. After mid 1990s, the inflation volatility 

gradually declined to a level around 1% in early 2000s and remained at this level during 2000s. 

Interestingly, in 2000s the commodity return correlations depicted in Figures 2 and 3 show time 

trends opposite to those of the inflation rate and inflation volatility. Thus, it is unlikely that the 

recent increase in commodity return correlations were driven by inflation. 

E. Adoption of Biofuel 

Another recent development in commodities markets is the wide adoption of biofuel. To 

reduce the reliance on oil as the main source of energy, many countries including the US have 

adopted new energy policies to promote the use of biofuel. The 2005 US energy bill mandated 

                                                           

17 See also a recent article in the Wall Street Journal (August 16, 2010) based on price fluctuations of oil and S&P 

500 stock index in 2010: “Oil gets a new dance partner: stocks” by Carolyn Cui. 
18

that 7.5 billion gallons of ethanol be used by 2012. The 2007 energy bill further increased the 

mandate to 36 billion by 2022. The combination of ethanol subsidies and high oil prices led to a 

rapid growth of the ethanol industry, which now consumes about one third of the US corn 

production. The rise of the ethanol industry might have caused prices of corn and other close 

substitutes such as soybeans and wheat to comove with oil prices. As corn is also a major source 

of livestock feed, this effect may have also affected prices of livestock commodities. 

A recent study by Roberts and Schlenker (2010) provides a quantitative estimate of the 

impact of the US ethanol mandate on food prices. By directly estimating demand and supply 

elasticities of agricultural commodities based on crop-yield fluctuations resulted from random 

weather shocks, this study shows that the growth of ethanol production can cause food prices to 

increase by 20-30 percent. While this estimate is significant, it is still too small to explain the 

near quadruple of corn price from about $2.00 per bushel in 2006 to almost $8.00 per bushel in 

2008. More importantly, the growth of ethanol production can explain neither the synchronized 

price booms of commodities unrelated to food such as cotton and coffee, nor the greater increase 

in return correlations among indexed commodities than among off-index commodities. 

IV. Regression Analysis 

We now use regression analysis to examine the effects of the aforementioned economic 

mechanisms on commodity prices in recent years. We first analyze the GSCI index return,18 and 

then analyze price comovements of non-energy commodities with oil. 

A. GSCI Index Return 

We first examine links of the GSCI index return with a set of economic variables, which we 

choose to capture the economic mechanisms discussed in the previous section. We include return 

of Morgan Stanley emerging market equity index ሺܴாெ,௧) and the global shipping index (ܴ஺஼்,௧), 

which was constructed by Kilian (2009) based on an average of dry cargo single voyage freight 

rates, to represent effects caused by the rapid growth of emerging economies. We also include 

returns of the S&P 500 US equity index ሺܴௌ௉,௧), JP Morgan Treasury bond index ሺܴ௃௉ெ,௧), and the 

US dollar index (ܴ௎ௌ஽,௧ሻ which capture the key links of commodity prices with equity market, 

interest rate and dollar exchange rate. As we discussed before, these links are subject to different 

forces at work. For example, the link with the dollar exchange rate is affected by both demands 

for physical commodities from emerging economies and demands for index investment from 

international investors; the links with equity market and interest rate may reflect effects of 

economic fundamentals, as well as portfolio rebalancing of index investors. Finally, we also 

                                                           

18 The correlation between returns of GSCI and DJ-UBS indices is over 0.9. As a result, analyzing DJ-UBS return 

provides very similar results to those from analyzing GSCI return. Thus, we only report results on GSCI return. 
19

examine the link of GSCI return with CPI inflation rate ሺܴ஼௉ூ,௧ሻ. We will separately treat the link 

with CPI inflation rate and CPI core inflation rate (which excludes food and energy prices). 

Morgan Stanley emerging market equity index, S&P 500 US equity index, JP Morgan 

Treasury bond index, and the US dollar index are available at daily frequencies, and are obtained 

from Bloomberg and Datastream. CPI inflation rate and the global shipping index are only 

available at monthly frequencies and are obtained from the websites of the Bureau of Labor 

Statistics and Lutz Kilian. Our sample goes from 1/4/1988 to 10/29/2009, the longest period 

during which all of these variables are available. This sample is sufficient for our focus on 

analyzing changes in the links of GSCI return with these variables after 2004.19 

We use the following regression specification: 

ܴீௌ஼ூ,௧

௡ ൌ ܽ଴ ൅ ܽଵܫ௧ஹ଴ସ ൅ ሾܾ଴ ൅ ܾଵܫ௧ஹ଴ସሺݐ െ 2004ሻሿܴாெ,௧

௡ ൅ ሾܿ଴ ൅ ܿଵܫ௧ஹ଴ସሺݐ െ 2004ሻሿܴௌ௉,௧

௡ (2) 

                        ൅ሾ݀଴ ൅ ݀ଵܫ௧ஹ଴ସሺݐ െ 2004ሻሿܴ௃௉ெ,௧

௡ ൅ ሾ݁଴ ൅ ݁ଵܫ௧ஹ଴ସሺݐ െ 2004ሻሿܴ௎ௌ஽,௧

௡

                               ൅ሾ݂଴ ൅ ݂ଵܫ௧ஹ଴ସሺݐ െ 2004ሻሿܴ஼௉ூ,௧

௡ ൅ ሾ݃଴ ൅ ݃ଵܫ௧ஹ଴ସሺݐ െ 2004ሻሿܴ஺஼்,௧௡ ൅ ߳௧

where ܫ௧ஹ଴ସ is an indicator function which takes a value of 1 if time t is later than 2004 and 0 

otherwise. We normalize every variable by its sample mean and standard deviation (as marked 

by the superscript ݊ on each variable) so that the regression coefficients in a univariate regression 

can be interpreted as the correlation between the right-hand-side and left-hand-side variables. To 

highlight the potential changes in the links of GSCI return with the right-hand-side variables 

after 2004, we impose a linear trend after 2004 in each of the regression coefficients. For 

example, the coefficient of  ܴாெ,௧ consists of a pre-2004 level ܾ଴ and a linear trend ܾଵܫ௧ஹ଴ସሺݐ െ

2004ሻ with ܾଵ as the slope of the trend. This linear trend specification is consistent with the 

gradual increased return correlations of commodities with each other and with other variables 

that are highlighted in Figures 2, 3, and 4. This specification allows us to conveniently test the 

changes in the return correlations of GSCI return with the right-hand-side variables after 2004, 

even though we expect these trends to eventually stabilize.20 

We analyze this regression in both daily and monthly frequencies. We first examine the precrisis period before September 2008 to isolate potential effects of the recent financial crisis, and 

then we examine the full sample period which extends to the end of October 2009. Table 3 

reports the regression results for using the right-hand-side variables individually and jointly, and 

                                                           

19 We are not particularly interested in an elaborate analysis of these links further back to the past. Instead, we refer 

readers to other studies, such as Erb and Harvey (2006) and Gorton and Rouwenhorst (2006), for links of 

commodity prices with broader sets of economic variables over longer sample periods. 

20 In a previous version of this paper, we have also used specifications that use dummies for individual years after 

2004. These specifications give similar results as the linear trend specification, although more cumbersome. For 

brevity of the presentation, we do not present the results based on year-dummies here. 
20

for the pre-crisis period and the full sample period. Panel A covers regressions of the daily data, 

while Panel B covers the monthly data. 

While there was a negligible link between returns of the GSCI index and the emerging 

market index before 2004 (i.e., the estimates of ܾ଴ in different regressions are all insignificant), a 

positive trend appeared after 2004. This trend (i.e., the ܾଵ coefficient) is highly significant in the 

daily regressions in both of the pre-crisis period and full sample period. Although the t-stats 

become insignificant possibly due to the smaller sample size, the magnitudes of the ܾଵ estimates 

in the monthly regressions remain similar to those in the daily regressions. This positive trend 

confirms Figure 4 regarding the increasingly important link between commodity prices and 

emerging economies in recent years. The estimates of ݃଴ are positive and significant, indicating 

that commodity prices are positively correlated with the cost of transporting goods across the 

world. This is consistent with the finding of Kilian (2009) that global economic activity has an 

important effect on oil prices. However the estimates of ݃ଵ are insignificant, indicating little 

changes in this relationship after 2004. Overall, these results confirm the important effects of 

commodity demands from emerging markets on commodity prices. 

Table 3 also shows small but significant negative return correlations of the GSCI index with 

S&P 500 equity index and JP Morgan Treasury bond index before 2004 as reflected by estimates 

of ܿ଴ and ݀଴ in the daily and monthly regressions. These negative correlations are consistent with 

the findings of Greer (2000), Gorton and Rouwenhorst (2006) and Erb and Harvey (2006). There 

were negligible changes in these return correlations between 2004 and September 2008 because 

the estimates of ܿଵ and ݀ଵ are all insignificant in the pre-crisis period. These estimates become 

highly significant in the full sample period, suggesting significant changes after the financial 

crisis in September 2008. In particular, return correlation of GSCI with S&P equity index has 

increased, while with JP Morgan bond index has decreased. These changes are consistent with 

index investors flying away from risky stocks and commodities and invest in riskless Treasury 

bonds during the crisis (e.g., Kyle and Xiong (2001)). These results also confirm the findings of 

Buyuksahin, Haigh and Robe (2009) and Silvennoinen and Thorp (2010) that return correlation 

between GSCI and S&P indices went up during the crisis, but not before the crisis. 

While there was an insignificant link between the GSCI return and the US dollar return 

before 2004 (as reflected by the insignificant estimates of ݁଴), a negative trend appeared after 

2004 (as reflected by the estimates of ݁ଵ). This trend is negative and significant in the daily and 

monthly regressions and in both of the pre-crisis period and full sample period. This trend, as we 

discussed before, is consistent with the hypotheses based on the rapid growth of emerging 

economies and the increase in commodity index investment, and confirms the illustration in 

Figure 4. 
21

It is well known that commodity prices comove positively with inflation rate, albeit 

pronounced only at long horizons such as 1-year and 5-year horizons, e.g., Greer (2000), Gorton 

and Rouwenhorst (2006) and Erb and Harvey (2006). In Panel B of Table 3, the estimates of ݂଴

and ݂ଵ for the monthly CPI inflation rate and CPI core inflation rate are all insignificant, 

indicating insignificant correlations between GSCI return and inflation rate in 1990s and 2000s. 

This is consistent with our earlier discussion that inflation does not appear to have an important 

effect on commodity prices during this period, especially on commodity price fluctuations at 

daily and monthly horizons. 

B. Price Comovements of Non-energy Commodities with Oil 

We now examine whether the increasing presence of index investors contributed to the 

increase in return correlations of non-energy commodities with oil. To identify this effect, we 

focus on the difference between the increased correlations of indexed and off-index commodities 

after 2004. We pool together daily returns of first-month futures contracts of all non-energy 

commodities from 1/2/1998 to 10/29/2009. We choose this sample period so that there are six 

years before 1/1/2004 and roughly six years afterwards. As we discussed before, there is not 

much difference between the return correlations of indexed and off-index commodities in the 

earlier period. Extending the sample period further back does not affect our result. 

We specify the following panel regression of the normalized commodity returns ܴ௜,௧

௡ on the 

normalized return of oil ܴ௢௜௟,௧

௡ , and a set of control variables including the normalized returns of 

the Morgan Stanley emerging market equity index ܴாெ,௧

௡ , S&P 500 US equity index ܴௌ௉,௧

௡ JP 

Morgan Treasury bond index ܴ௃ெ௉,௧

௡ , and US dollar index ܴ௎ௌ஽,௧

௡ : 

ܴ௜,௧

௡ ൌߙ ൅ሾߚ ൅ ௜଴ߚଵሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସ ൅ ߚଶሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସܫ௡௜ௗ௘௫ሿܴ௢௜௟,௧௡ (3) 

 + ሾκ଴௜ ൅ κଵሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସ ൅ κଶሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସܫ௡௜ௗ௘௫ሿܴாெ,௧

௡

 + ሾߛ ൅ ௜଴ߛଵሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସ ൅ ߛଶሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସܫ௡௜ௗ௘௫ሿܴௌ௉,௧

௡

 + ሾߠ ൅ ௜଴ߠଵሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସ ൅ ߠଶሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସܫ௡௜ௗ௘௫ሿܴ௃ெ௉,௧

௡

 +ሾߟ ൅ ௜଴ߟଵሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସ ൅ ߟଶሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସܫ௡௜ௗ௘௫ሿܴ௎ௌ஽,௧

 ௧,௜ߝ ൅ ௡

ܫ௡௜ௗ௘௫ is an indicator function with a value of 1 if the commodity is in either the GSCI or DJ-UBS 

index, and 0 otherwise. We include returns of the Morgan Stanley emerging market equity index 

and the US dollar index to control for the effect of commodity demands from emerging 

economies. As we discussed before, the dollar return might also pick up effects by international 

index investors. Thus, this control might be excessive. We also include returns of the S&P stock 

index and the JP Morgan Treasury bond index to control for the effects of the recent financial 
22

crisis. Again, these controls might be excessive because the spillover of the financial crisis to 

commodities markets may be caused by trading of index investors. 

Motivated by our earlier analysis, we specify a linear trend after 2004 in the regression 

coefficient of each independent variable. Specifically, we decompose each regression coefficient 

into three components. Figure 12 provides a graphical account of this decomposition. For 

example, in the coefficient of oil return, the first component ߚ ௜଴measures the baseline coefficient 

(specific to the individual commodity i) before 2004; the second component ߚଵሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସ

captures a common trend in the coefficient after 2004 with ߚଵ as the slope of the trend; and the 

third component ߚଶሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସܫ௡௜ௗ௘௫ measures the additional trend after 2004 with ߚଶ as the 

slope of the trend if the commodity is in at least one of the GSCI and DJ-UBS indices. The last 

component captures the difference in the changes after 2004 between the return correlations of 

indexed and off-index commodities with oil. Our key hypothesis is that ߚଶ is significantly 

positive, which implies that the increasing presence of index investors has led to a greater 

increase in return correlations of indexed commodities with oil than that of off-index 

commodities. We also decompose the regression coefficient on each of the control variables in 

the same way to control for possible trends driven by other economic mechanisms. 

We analyze this regression in the full sample with all non-energy commodities, as well as in 

several sub-samples including the soybean complex (which includes soybeans, soybean meal, 

and soybean oil), the grain sector, the soft sector, the livestock sector, and the metal sector. We 

separately examine the pre-crisis period from 1/2/1998 to 8/31/2008 and the full sample period 

from 1/2/1998 to 10/29/2009 in order to isolate the crisis effect. For each of the periods, we first 

analyze the regression with only oil return and then together with the control variables. Table 4 

reports the regression results. 

Panel A reports the results from the full sample with all non-energy commodities. The 

estimates of coefficients ߚ ௜଴show that most of the non-energy commodities had a small and 

positive return correlation with oil before 2004, with gold having the highest estimate of 0.15. 

Several commodities from the soft and livestock sectors had a small negative return correlation 

with oil; these commodities include live cattle, feeder cattle, coffee, cocoa, lumber, orange juice, 

and pork bellies. These small return correlations are consistent with the finding of Erb and 

Harvey (2006). 

The estimates of ߚଵ and ߚଶ in both of the pre-crisis and full sample periods are positive and 

significant. These estimates suggest that there was a significant and increasing trend in return 

correlations of non-energy commodities with oil after 2004. More importantly, this increasing 

trend is significantly stronger for indexed commodities than for off-index commodities. This 

pattern is robust to including the control variables in the regressions and thus supports the 

hypothesis that the increasing presence of index investors led prices of indexed commodities to 
23

comove more with oil. Furthermore, this effect was present before the disruption of the financial 

crisis in September 2008. 

In the pre-crisis period with the control variables, the estimates of ߚଵ and ߚଶ are 0.04 and 

0.02 respectively. These values imply that the return correlation between an off-index nonenergy commodity and oil increased by 0.04 each year. At this rate, the correlation had an 

accumulative increase of 0.2 between 2004 and 2009. The return correlation between an indexed 

non-energy commodity and oil had an extra increase of 0.02 each year. Thus its accumulative 

increase between 2004 and 2009 was 0.3, which is substantial in economic terms. 

Panel B of Table 4 also reports the estimates of ߚଵ and ߚଶ in each commodity sub-sample in 

both of the pre-crisis and full sample periods after including the control variables in the 

regressions. The estimates are consistently positive and significant across the sub-samples except 

in the livestock sector, in which the estimate of ߚଵ is zero and the estimate of ߚଶ is positive but 

significant only for the full sample period. Taken together, the increased price comovements 

between indexed non-energy commodities and oil were not driven by a few commodities 

concentrated in one sector; instead, our result about the increased price comovements is robust 

across different sub-samples of commodities. 

We have also examined the regression in (3) based on weekly commodity returns. The 

estimates of ߚଵ and ߚଶ are positive with similar magnitudes as those reported in Table 4. 

However, their t-stats are less significant. This is consistent with our earlier discussion that we 

need to use daily data to measure return correlation in order to compensate for the relatively 

short sample period after 2004. 

Panel A of Table 4 reveals several interesting observations about the return correlations of 

non-energy commodities with the control variables. First, there is a significant and positive trend 

in their return correlations with the emerging market index after 2004 in both of the pre-crisis 

and full sample periods, as reflected by the positive and significant estimates of coefficient κଵ. 

This is consistent with the increasing return correlation between the GSCI index and the 

emerging market index after 2004. However, there is a negligible difference between indexed 

and off-index commodities in the increase of their return correlations with the emerging market 

index, as reflected by the insignificant estimates of κଶ. This lack of difference is consistent with 

the fact that the effects of commodity demands from emerging economies are independent of the 

commodity indices. It also indirectly confirms the discriminating power of our identification 

strategy based on the difference-in-difference effect. 

Furthermore, the estimates of coefficient ߟଵ are negative, with a significant t-stat in the full 

sample period although an insignificant one in the pre-crisis period. These estimates suggest a 

negative trend in the return correlations of non-energy commodities with the US dollar after 

2004, which is consistent with the decreasing trend in the return correlation between the GSCI 
24

index and the US dollar index. More interestingly, the estimates of coefficient ߟଶ are also 

negative, with a significant t-stat in the pre-crisis period although an insignificant one in the full 

sample period. These estimates indicate that the decreasing trend is stronger for indexed 

commodities than for off-index commodities. This difference-in-difference result suggests that 

the decreasing trend in return correlations of non-energy commodities with the US dollar was 

not all driven by commodity demands from emerging economies and was at least partially 

related to trading by international index investors in commodities markets. 

C. Controlling for Illiquidity 

Because off-index commodities tend to be less liquid, there is a potential concern that their 

price fluctuations might lag behind that of oil and thus have smaller contemporaneous return 

correlations with oil than indexed commodities. To ensure that illiquidity is not the reason for the 

less pronounced trends in return correlations between off-index commodities and oil, we add two 

lags of oil return in the regression to control for illiquidity: 

                     ܴ௜,௧

௡ ൌߙ ൅ሾߚ ൅ ௜଴ߚଵሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସ ൅ ߚଶሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସܫ௡௜ௗ௘௫ሿܴ௢௜௟,௧௡ (4) 

 + ሾߚଷ௜ ൅ ߚସሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସ ൅ ߚହሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସܫ௡௜ௗ௘௫ሿܴ௢௜௟,௧ିଵ

௡

 ൅ሾߚ ൅ ௜଺ߚ଻ሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସ ൅ ߚ଼ሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସܫ௡௜ௗ௘௫ሿܴ௢௜௟,௧ିଶ

௡

 + ሾκ଴௜ ൅ κଵሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସ ൅ κଶሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସܫ௡௜ௗ௘௫ሿܴாெ,௧

௡

 + ሾߛ ൅ ௜଴ߛଵሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସ ൅ ߛଶሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସܫ௡௜ௗ௘௫ሿܴௌ௉,௧

௡

 + ሾߠ ൅ ௜଴ߠଵሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସ ൅ ߠଶሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସܫ௡௜ௗ௘௫ሿܴ௃ெ௉,௧

௡

 +ሾߟ ൅ ௜଴ߟଵሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସ ൅ ߟଶሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସܫ௡௜ௗ௘௫ሿܴ௎ௌ஽,௧

௧,௜ߝ ൅ ௡

For the two lagged oil returns ܴ௢௜௟,௧ିଵ

௡ and ܴ௢௜௟,௧ିଶ௡ , we also use the same trend specification as in 

the coefficient of the contemporaneous oil return. With the lags, the effective return correlation 

between a commodity and oil is determined by the sum of the coefficients of ܴ௢௜௟,௧

௡ , ܴ௢௜௟,௧ିଵ௡ , and 

ܴ௢௜௟,௧ିଶ

௡ . Then the hypothesis that return correlations of indexed commodities with oil have more 

pronounced trends after 2004 than off-index commodities is equivalent to that ߚଶ ൅ ߚହ ൅ ߚ଼ is 

significantly positive. We use the F-statistic to test the null hypothesis that ߚଶ ൅ ߚହ ൅ ߚ଼ ൑ 0. 

 Table 5 reports the regression results after adding the two lags of oil return. The estimates of 

the coefficients related to the second lag are all close to zero, suggesting that two lags are 

sufficient. We have also used more lags and their coefficients are all negligible. Interestingly, the 

estimates of ߚଶ ൅ ߚହ ൅ ߚ଼ are positive in both of the pre-crisis period and full sample period, with 

or without the control variables. In particular, in the pre-crisis sample period the estimates of the 
25

two lagged trends ߚହ and ߚ଼ are both close to zero and the F-statistic rejects the null hypothesis 

that ߚଶ ൅ ߚହ ൅ ߚ଼ ൑ 0 with 95% confidence. In the full sample period, the estimate of ߚହ becomes 

significantly negative, but the estimate of ߚଶ ൅ ߚହ ൅ ߚ଼ remains positive. The F-statistic still 

rejects the null hypothesis with 95% confidence in the absence of the control variables, although 

the F-statistic becomes less significant after the control variables are added.21 Taken together, 

Table 5 demonstrates that the difference-in-difference result between the return correlations of 

indexed and off-index commodities with oil is robust to the illiquidity concern about off-index 

commodities. 

We have also examined correlations between trading volume of different commodities. 

Consistent with the increasing presence of index investors in commodities markets, we find a 

significant increasing trend in trading volume correlations between indexed non-energy 

commodities and oil after 2004 even though the existence of such trend is not clear for off-index 

commodities. To save space, we do not report this set of results in the paper. 

D. Spot and Roll Returns 

So far, our analysis focuses on returns of rolling front-month futures contracts of different 

commodities. As highlighted by Erb and Harvey (2006), these returns have two components: 

spot return (i.e., return from a commodity’s spot price) and the so-called roll return (which 

originates from the commodity’s futures price curve). Suppose that the curve is in backwardation 

(i.e., downward sloping). Then besides the spot return, rolling the front-month futures contract 

also yields a positive roll return from the increase in the contract price as its maturity shortens. 

Conversely, the roll return is negative if the curve is in contango (i.e., upward sloping). It is 

intuitive that the roll return fluctuates with the slope of the futures price curve. Erb and Harvey 

(2006) show that roll returns contribute to a significant fraction of the historically high average 

return to GSCI index because many commodities in the index tend to be, although not always, in 

backwardation. Given the important distinction between these two components, it is interesting 

to separately examine their roles in driving our result on the increasing return correlations 

between non-energy commodities and oil. 

We first analyze the spot-return correlations of non-energy commodities with oil. Due to the 

lack of centralized spot markets for commodities, spot prices are often not readily available. We 

acquired spot prices for a set of commodities from Pinnacle Data Corp., the same data vendor 

that provided us with the futures price data. The set includes oil and 16 non-energy commodities 

(8 short of the non-energy commodities with futures listed in Table 1). These non-energy 

                                                           

21 It is conceivable that during the crisis, market liquidity deteriorated, especially for the off-index commodities. As 

a result, the crisis effect after September 2008, which is captured by the control variables, dominated the effect of 

index investment and made it less significant. 
26

commodities include corn, soybeans, wheat, Kansas wheat, soybean oil, Minnesota wheat, and 

oats from the grain sector; cotton and sugar from the soft sector; live cattle and lean hogs from 

the livestock sector; and gold, silver, copper, platinum, and palladium from the metal sector. We 

pool together their daily spot returns and regress them on spot return of oil and the set of control 

variables based on the regression specified in (3). The estimates of coefficients ߚଵ and ߚଶ are 

reported in Panel A of Table 6. The estimate of ߚଵ is positive and significant in the pre-crisis 

period but becomes insignificant in the full sample period. More interestingly, the estimate of ߚଶ

is positive and significant in both of the pre-crisis period and full sample period, confirming the 

same difference-in-difference result in spot returns as in returns of rolling front-month futures 

contracts. This result implies that the price effect generated by the growing commodity index 

investment in recent years is also present in spot prices of commodities. 

As fluctuations of roll returns are driven by slope changes of futures price curves, we now 

examine the slope-change correlations between non-energy commodities and oil. We define the 

slope of commodity ݅’s futures price curve as the difference between the logarithm of its secondmonth futures price (with maturity ܶଶ) and the logarithm of its front-month futures price (with 

maturity ܶଵ) normalized by the difference in the two maturities (ܶଶ െ ܶଵ):

ܵ௜,௧ ൌ ଵ

୘మି୘భ

lnሺܨ௜,௧,்మ /ܨ௜,௧,்భሻ. 

We compute the slope by the difference between the second and front-month futures prices 

rather than between the front-month futures price and spot price so that we can employ the larger 

sample of non-energy commodities with futures contracts listed in Table 1. We pool together 

daily slope changes of these commodities and regress them on slope change of oil by using the 

following difference-in-difference specification: 

                     ∆ܵ௜,௧ ൌߙ ൅ሾߚ ൅ ௜଴ߚଵሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସ ൅ ߚଶሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସܫ௡௜ௗ௘௫ሿ∆ܵ௢௜௟,௧ ൅ ߝ௜,௧. (5) 

As before, coefficient ߚଵ captures the trend after 2004 in the slope-change correlations of offindex non-energy commodities with oil, and coefficient ߚଶ captures the additional trend for 

indexed commodities. 

Panel B of Table 6 reports the regression results. The estimates of ߚଵ and ߚଶ are small and 

insignificant in both of the pre-crisis period and full sample period, indicating no evidence of 

increased slope-change correlations between non-energy commodities and oil after 2004 and no 

evidence of any difference between indexed and off-index commodities. This result implies that 

the increasing presence of index investors after 2004 did not systematically affect the slopes of 

commodity futures curves; this is probably because arbitrageurs were able to effectively spread 

out the price impact of index investment across the curves. This result also suggests that the 
27

increased return correlations between non-energy commodities and oil after 2004 were mostly 

driven by spot returns rather than roll returns. 

V. Volatility Spillover 

Our earlier analysis confirms that the rapid growth of commodity index investment after 

2004 had a significant impact on commodities markets and caused prices of seemingly unrelated 

commodities to move together. This effect is a reflection of an ongoing financialization process, 

through which the previously (partially) segmented commodities markets became more 

integrated with outside financial markets and with each other. While this process may have led to 

a more efficient sharing of commodity price risk, it can also act as a channel to spill over 

volatility from outside financial markets to commodities markets and across different 

commodities markets.22 We now discuss this spillover effect. 

Figure 13 depicts the annualized daily return volatility of oil, GSCI non-energy excess return 

index, and Morgan Stanley world equity index estimated from one-year rolling windows. The 

GSCI non-energy excess return index tracks price fluctuations of GSCI commodities in the four 

non-energy sectors. Morgan Stanley world equity index tracks the equity market performance of 

both developed and emerging economies. The figure shows that oil price is always volatile. 

During most of 1990s and 2000s, its volatility was at least twice as high as the volatility of the 

world equity index. Oil return volatility shot up from around 30% to near 60% in 2008, a level 

that had caused great public concerns. However, this is not the first time for oil return volatility 

to reach this level – it also happened in early 1990s during the Gulf war. More interestingly, 

while return volatility of non-energy commodities had been very stable at a level around 10% 

throughout 1990s and early 2000s, it started to rise after 2004 and peaked at an unprecedented 

level of 27% in 2008. This is concurrent with the hikes in volatility of oil and the world equity 

index. 

Different factors may have contributed to the large volatility increase in oil and non-energy 

commodities. First, the world economic recession that accompanied the recent financial crisis 

has made commodity demands more uncertain and thus prices more volatile. Second, the 

financial crisis which initially disrupted in the markets for mortgage-backed securities eroded 

balance sheets of many financial institutions and eventually hurt the risk appetite of financial 

investors for many seemingly unrelated assets in their portfolios including commodities (e.g., 

Kyle and Xiong (2001)). To identify the latter spillover effect, we analyze the difference between 

return volatility of indexed and off-index non-energy commodities from 1/2/1998 to 10/29/2009. 

                                                           

22 See Bekaert and Harvey (1997) for an analysis of volatility spillover after financial liberalization of emerging 

equity markets. 
28

Specifically, we first normalize the daily return of each commodity (return of rolling its firstmonth futures contract) by its volatility before 2004 and its whole sample mean. After the 

normalization, the return series of all non-energy commodities have the same volatility before 

2004. We then analyze changes of volatilities after 2004 by regressing the pooled squared 

normalized returns onto a set of year dummies for each year after 2004 and their interaction 

terms with an index dummy for whether a given commodity is in at least one of GSCI and DJUBS indices: 

൫ܴ௜,௧

௡ ൯

ଶ

ൌ ܽ଴௜ ൅ ܾ଴ସܫ௬ୀ଴ସ ൅ ܾ଴ହܫ௬ୀ଴ହ ൅ ܾ଴଺ܫ௬ୀ଴଺ ൅ ܾ଴଻ܫ௬ୀ଴଻ ൅ ܾ଴଼ܫ௬ୀ଴଼ (6) 

൅ܾ଴ଽܫ௬ୀ଴ଽ+ܿ଴ସܫ௡௜ௗ௘௫ܫ௬ୀ଴ସ ൅ ܿ଴ହܫ௡௜ௗ௘௫ܫ௬ୀ଴ହ ൅ ܿ଴଺ܫ௡௜ௗ௘௫ܫ௬ୀ଴଺

൅ܿ଴଻ܫ௡௜ௗ௘௫ܫ௬ୀ଴଻ ൅ ܿ଴଼ܫ௡௜ௗ௘௫ܫ௬ୀ଴଼ ൅ ܿ଴ଽܫ௡௜ௗ௘௫ܫ௬ୀ଴ଽ ൅ ߝ௜,௧

The squared return is a widely used proxy for return volatility. The coefficients ܾ଴ସ, ܾ଴ହ, ܾ଴଺, ܾ଴଻, 

ܾ଴଼, and ܾ଴ଽ measure the baseline volatility changes of off-index commodities in each of the 

years after 2004, while the coefficients ܿ଴ସ, ܿ଴ହ, ܿ଴଺, ܿ଴଻, ܿ଴଼, and ܿ଴ଽ measure the additional 

volatility increase of indexed commodities relative to off-index commodities in each of the years. 

Table 7 reports the regression results. It shows that the estimates of coefficients ܾ଴଼ and ܾ଴ଽ are 

positive and significant, indicating significant baseline volatility increase in years 2008 and 2009 

across the commodities. Interestingly, the estimates of coefficients ܿ଴ସ, ܿ଴଺, ܿ଴଻, ܿ଴଼ and ܿ଴ଽ are 

all positive and significant, indicating that indexed commodities exhibited larger volatility 

increases than those off-index commodities in 2004, 2006, 2007, 2008 and 2009. This result is 

consistent with a spillover effect that the presence of index investors has contributed to the large 

increase of commodity price volatility in recent years. 

The volatility spillover could originate from uncertainty about the economy, turmoil in stock 

markets and bond markets, or shocks to oil prices. It is difficult to identify the source because the 

exogenous shocks are unobservable. Following our earlier analysis, we focus on the possible 

spillover of oil price volatility to non-energy commodities through the largely increased return 

correlations between non-energy commodities and oil. From non-energy commodity returns, we 

first filter out the control variables we have used before (i.e., returns of Morgan Stanley 

emerging market index, S&P 500 stock index, JP Morgan Treasury bond index, and US dollar 

index, CPI core inflation rate, and change of the global shipping index) and then oil return by 

using the following regression specification: 

ܴ௜௧ ൌ ܽ଴ ൅ ܽଵܫ௧ஹ଴ସ ൅ ሾܾ଴ ൅ ܾଵܫ௧ஹ଴ସሺݐ െ 2004ሻሿܴாெ,௧ ൅ ሾܿ଴ ൅ ܿଵܫ௧ஹ଴ସሺݐ െ 2004ሻሿܴௌ௉,௧ (7) 

            ൅ሾ݀଴ ൅ ݀ଵܫ௧ஹ଴ସሺݐ െ 2004ሻሿܴ௃௉ெ,௧ ൅ ሾ݁଴ ൅ ݁ଵܫ௧ஹ଴ସሺݐ െ 2004ሻሿܴ௎ௌ஽,௧

       ൅ሾ݂଴ ൅ ݂ଵܫ௧ஹ଴ସሺݐ െ 2004ሻሿܴ஼௉ூ,௧ ൅ ሾ݃଴ ൅ ݃ଵܫ௧ஹ଴ସሺݐ െ 2004ሻሿܴ஺஼்,௧
29

  ൅ሾ݄଴ ൅ ݄ଵܫ௧ஹ଴ସሺݐ െ 2004ሻሿܴ௢௜௟,௧ ൅ ߳௧ 

We have used a similar specification to analyze GSCI index return. Since we only have monthly 

observations on CPI inflation rate and the global shipping index, we treat ܴ஼௉ூ,௧ and ܴ஺஼்,௧ as 

constant during a month. Depending on whether we include oil return in the regression, we 

obtain two sets of residual returns, one after filtering out only the control variables and the other 

after filtering out the control variables and oil return. As we discussed before, the control 

variables serve to filter out the potential effects of economic uncertainty, as well as possible 

spillover of stock market volatility and US dollar volatility to commodities. These controls, 

while potentially excessive, allow us to highlight spillover of oil price volatility to indexed nonenergy commodities. 

We then repeat the difference-in-difference analysis of regression (6) using the two sets of 

residual returns. The results are also reported in Table 7. After filtering out only the control 

variables from non-energy commodity returns, the estimates of coefficients ܿ଴ସ, ܿ଴଺, ܿ଴଻, ܿ଴଼  and 

ܿ଴ଽ are substantially reduced, although ܿ଴ସ , ܿ଴଺ , ܿ଴଻ and ܿ଴଼ are still positive and significant. 

After further filtering out oil return, the estimates of coefficients ܿ଴ସ, ܿ଴଺, ܿ଴଻, ܿ଴଼ and ܿ଴ଽ are 

further reduced, and the estimates of ܿ଴଻ and ܿ଴଼ are now insignificant. These reductions indicate 

that the spillover of oil price volatility through index investment contributed to the greater 

volatility increase of indexed non-energy commodities in 2007 and 2008. 

Overall, we find that non-energy commodities in the GSCI and DJ-UBS indices had 

significantly greater volatility increase than off-index commodities in 2008. In particular, the 

greater volatility increase of indexed commodities was related to their greater return correlations 

with oil. These results suggest that trading of commodity index investors can act as a channel for 

spilling over price volatility to commodities markets. 

VI. Conclusion 

This paper finds that concurrent with the rapid growth of index investment to commodities 

markets, prices of non-energy commodities became increasingly correlated with oil prices and 

this trend is significantly more pronounced for commodities in the two popular GSCI and DJUBS indices. This finding reveals a fundamental process of financialization amongst 

commodities markets, through which commodity prices became more correlated with prices of 

financial assets and with each other. This result also helps explain the synchronized price boom 

and bust of a large set of seemingly unrelated commodities in 2006-2008. 

Our analysis suggests that as a result of the financialization process, the price of an individual 

commodity is no longer simply determined by its supply and demand. Instead, prices are also 

determined by a whole set of financial factors such as the aggregate risk appetite for financial 
30

assets, and investment behavior of diversified commodity index investors. This fundamental 

change is likely to persist as long as commodity index investment remains popular among 

financial investors and has profound implications for a wide range of issues from commodity 

producers’ hedging strategies and speculators’ investment strategies to many countries’ energy 

and food policies. 

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Silvennoinen, Annastiina and Susan Thorp (2010), Financialization, crisis and commodity correlation 

dynamics, Working paper, Queensland University of Technology. 

US Senate Permanent Subcommittee on Investigations (2009), Excessive speculation in the wheat market, 

Committee on Homeland Security and Governmental Affairs, June 24. 

 
32

Figure 1: Commodity Prices 

This figure depicts price appreciations of five commodities, oil, soybeans, cotton, live cattle, and copper, 

since January 1991. We normalize price of each commodity in January 1991 to be 100. 
33

Figure 2: Rolling Return Correlation of Oil with Different Non-energy Commodities 

This figure depicts one-year rolling return correlation of oil with soybean, cotton, live cattle, and copper, 

together with the 95% confidence interval, in Panels A, B, C, and D, respectively. Panel E plots one-year 

rolling return correlation of the GSCI energy and non-energy indices. 

 
34

Figure 3: Average Correlations of Indexed and Off-index Commodities 

This figure depicts average return correlations of commodities in the GSCI and DJ-UBS indices and 

commodities off these indices. We separate the samples of indexed and off-index commodities. In each 

sample, we construct an equal-weighted return index for each commodity sector. A commodity is not 

included into the index until its average daily futures trading volume in a certain calendar year is larger 

than 20 million dollars. Then, for both indexed and off-index commodities, we compute equal-weighted 

average of one-year rolling return correlations of all sector pairs. 

Figure 4: Rolling Return Correlations of GSCI with Emerging Market and US Dollar Indices 

This figure depicts one-year rolling return correlation of the GSCI excess return index with the Morgan 

Stanley emerging market index in Panel A and with the US dollar index in Panel B. 
35

Figure 5: Commodity Prices in China and the US 

This figure depicts front-month futures prices of 6 commodities---heating oil, copper, soybeans, wheat, 

corn, and cotton---in China and the US. 
36

Figure 6: Commodity Return Correlations in China and the US 

This figure depicts average commodity return correlations in China and the US based on a sample of 8 

commodities, which are listed in Table 1 and which have futures contracts simultaneously traded in both 

countries. 

Figure 7: Open Interest of Commodity Futures Contracts

This figure depicts the total open interest of futures contracts with maturities less than one year of five 

commodities, oil, soybeans, cotton, live cattle, and copper, since January 1991. We normalize the open 

interest of each commodity in January 1991 to be 100. 
37

Figure 8: Accumulated Index Flow and GSCI Agriculture & Livestock Excess Return Index 

This figure depicts the accumulated index flow to the 12 agricultural and livestock commodities covered 

by the CIT report of CFTC, together with the GSCI agriculture & livestock excess return index. The 

weekly flow to each of the commodities is computed according to (1), and the accumulated flow to the 

commodity is computed by adding up the weekly flow from the first week of 2006 to a given week. By 

summing up the accumulated flow to the 12 commodities, we obtain the accumulated index flow. 

Figure 9: VIX Index 

This figure depicts the VIX index (the Chicago Board Options Exchange volatility index), a widely used 

equity market volatility measure derived from the implied volatility of S&P 500 index options. 
38

Figure 10: Return Correlation between GSCI and S&P Stock Indices 

This figure depicts one-year rolling correlation of daily returns of the GSCI and S&P 500 stock indices. 

Figure 11: Inflation and Inflation Volatility 

This figure depicts the annualized monthly CPI core inflation rate (excluding food and energy prices) and 

one-year rolling volatility of the monthly CPI core inflation rate. 

 
39

Figure 12: The Difference-in-Difference Specification 

This figure illustrates the difference-in-difference specification for individual commodities’ regression 

coefficient of any independent variable in regression (3). For example, the coefficient of oil return is 

ߚ ൅ ௜଴ߚଵሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସ ൅ ߚଶሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସܫ௡௜ௗ௘௫

  

   

Figure 13: Volatility of Oil, GSCI Non-Energy Index, and Morgan Stanley World Equity Index 

This figure depicts one-year rolling volatility of daily returns of oil, the GSCI non-energy excess return 

index, and the Morgan Stanley world equity index. 

 

ଵߚ slope଴௜ ߚ

slope ߚଵ ൅ ߚଶ

2004

Exp.

Time

If indexed:

If off‐Index:
40

Table 1: Commodity Futures Traded in US and Weights in the GSCI and DJ-UBS Indices 

This table lists all of the commodities with futures contracts traded in the U.S. The weights of these 

commodities in the GSCI and DJ-UBS contracts are taken from 2008.23

Commodities GSCI DJUBS Exchange Contracts Start of futures 

in US 

Start of futures 

in China 

Energy (4 Commodities)

WTI Crude Oil 40.6 15.0% NYMEX Every month 03/30/1983 

Heating Oil 5.3% 4.5% NYMEX Every month 11/14/1978 8/25/2004 

RBOB Gasoline 4.5% 4.1% NYMEX Every month 04/18/2006 

Natural Gas 7.6% 16.0% NYMEX Every month 04/04/1990 

Grains (9 commodities) 

Corn 3.6% 6.9% CME Group Mar, May , Jul, Sep & Dec 07/01/1959 09/22/2004 

Soybeans 0.9% 7.4% CME Group Jan, Mar, May , Jul, Aug, Sep, Nov 07/01/1959 01/04/1999 

Chicago Wheat 3.0% 3.4% CME Group Mar, May , Jul, Sep, Dec 07/01/1959 01/04/1999 

Kansas Wheat 0.7% 0 KCBT24 Mar, May, Jul, Sep, Dec 01/05/1970 

Soybean Oil 0 2.9% CME Group Jan, Mar, May, Jul, Aug, Sep, Oct, Dec 07/01/1959 

Minn. Wheat 0 0 MGE25 Mar, May, Jul, Sep, Dec 01/05/1970 

Soybean Meal 0 0 CME Group Jan, Mar, May, Jul, Aug, Sep, Oct, Dec 07/01/1959 

Rough Rice 0 0 CME Group Jan, Mar, May, Jul, Sep, Nov 08/20/1986 

Oats 0 0 CME Group Mar, May, July, Sep, Dec 07/01/1959 

Softs (6 Commodities) 

Coffee 0.5% 2.7% ICE Mar, May, Jul, Sep, Dec 08/16/1972 

Cotton 0.7% 2.2% ICE Mar, May, Jul, Oct, Dec 07/01/1959 06/01/2004 

Sugar 2.1% 2.8% ICE Mar, May, Jul, Oct 01/04/1961 01/06/2006 

Cocoa 0.2% 0 ICE Mar , May, Jul, Sep, Dec 07/01/1959 

Lumber 0 0 CME Group Jan, Mar, May, Jul, Sep, Nov 10/01/1969 

Orange Juice 0 0 ICE Jan, Mar, May, Jul, Sep, Nov 02/01/1967 

Livestock (4 Commodities) 

Feeder Cattle 0.3% 0.0% CME Group Jan, Mar, Apr, May, Aug, Sep, Oct, 11/30/1971 

Lean Hogs26 0.8% 2.5% CME Group Feb, Apr, May, Jul, Aug, Oct, Dec 02/28/1966 

Live Cattle 1.6% 4.1% CME Group Feb, Apr, Jun, Aug, Oct, Dec 11/30/1964 

Pork Bellies 0 0 CME Group Feb, Mar, May, Jul, Aug 09/18/1961 

Metals (5 Commodities) 

Gold27 1.5% 6.1% NYMEX Feb, Apr, Jun, Aug, Oct, Dec 12/31/1974 01/01/2008 

Silver 0.2% 2.4% NYMEX Jan, Mar, May, Jul, Sep, Dec 06/12/1963 

Copper28 2.6% 6.7% NYMEX Mar, May, Jul, Sep, Dec 01/03/1989 05/12/1997 

Platinum 0 0 NYMEX Jan, Apr, Jul, and Oct 03/04/1968 

Palladium 0 0 NYMEX Mar, Jun, Sep, and Dec 01/03/1977 

                                                           

23 The GSCI and DJ-UBS indices also include commodities traded in London, which are not included in our analysis. 24 Kansas City Board of Trade.

25 Minneapolis Grain Exchange.

26 A June contract has been added to the Lean Hog Futures series since 2002. As this new contract has a low open 

interest, we omit this contract in our analysis. 

27 For gold, silver, platinum, and palladium, contracts include the current month and the next two consecutive 

months, plus those contracts listed in the table. However, because the open interest of those short-maturity contracts 

(with maturities less than 3 months) is typically small, we omit these contracts in our analysis. 

28 The GSCI Index uses the copper contracts traded on LME, while the DJ-UBS Index uses those from NYMEX. 

We follow the convention of the DJ-UBS index and choose Mar, May, Jul, Sep and Dec for copper contracts in our 

analysis.
41

Table 2: The Commodity Index Traders Report 

This table summarizes the Commodity Index Traders (CIT) report released by the CFTC, which is 

reported on each Friday regarding a breakdown of the prior Tuesday’s positions for 12 agricultural 

commodities. The sample period covers from 1/2/2006 to 10/29/2009. We calculate the position of each 

reported category in each commodity relative to the total open interest, and then calculate the mean across 

the whole sample period. 

Index Traders Non-commercial 

Traders 

Commercial 

Traders 

Long Short Long Short Spread Long Short 

Chicago 

Wheat 41.6% 3.6% 10.6% 14.8% 28.2% 12.1% 40.6% 

Kansas 

Wheat 22.7% 0.7% 22.2% 7.8% 13.8% 23.7% 54.5% 

Corn 23.4% 1.7% 12.2% 5.9% 29.7% 23.4% 45.4% 

Soybean 25.6% 1.7% 13.6% 7.6% 29.1% 20.0% 44.3% 

Soybean 

Oil 23.6% 1.2% 14.3% 8.5% 23.0% 29.2% 60.1% 

Cotton 32.2% 1.2% 13.3% 10.8% 27.1% 19.1% 55.8% 

Lean Hogs 42.4% 1.1% 12.0% 15.7% 25.0% 8.8% 40.4% 

Live Cattle 38.7% 0.7% 16.2% 12.6% 23.1% 12.9% 43.4% 

Feeder 

Cattle 25.0% 1.3% 23.0% 16.3% 17.9% 15.5% 20.9% 

Cocoa 12.7% 0.9% 27.7% 13.5% 11.6% 40.5% 69.6% 

Sugar 27.7% 4.8% 11.7% 6.2% 22.5% 28.5% 59.4% 

Coffee 25.0% 0.8% 17.0% 12.2% 28.7% 22.7% 53.7% 

Average 28.4% 1.6% 16.1% 11.0% 23.3% 21.4% 49.0% 

 
42

Table 3: Regressions of GSCI Index Return 

This table reports regressions of return of the GSCI excess return index on returns of the Morgan Stanley 

emerging market equity index (ܴாெ,௧ሻ, the S&P 500 US stock index (ܴௌ௉,௧ሻ, the JP Morgan Treasury 

bond index ሺܴ௃௉ெ,௧), and the US dollar index (ܴ௎ௌ஽,௧ሻ, CPI inflation rate ሺܴ஼௉ூ,௧), and change of the 

global shipping index ሺܴ஺஼்,௧), separately and jointly. We normalize each variable by its sample mean 

and standard deviation. The data sample goes from 1/4/1988 to 10/29/2009. The regression specification 

is given in (2). Panel A reports the daily regression results based on the first four independent variables 

mentioned above, and Panel B reports the monthly results based on all of the six variables. For inflation 

rate, we separately report results based on CPI inflate rate and CPI core inflation rate (which excludes 

food and energy prices). Each panel reports results for the pre-crisis period from 1/4/1988 to 8/31/2008 

and for the full sample period. The t-statistics are adjusted for heteroskedasticity and serial correlation 

using the Newey-West method with five lags. 

Panel A: Regressions of Daily GSCI Return 

Regression (1) Regression (2) Regression (3) Regression (4) Regression (5) 

estimate t-stat estimate t-stat estimate t-stat estimate t-stat estimate t-stat 

The Pre-Crisis Period (1/4/1988 to 8/31/2008) 

ܽ଴ 0.00 -0.34 0.00 -0.31 0.00 -0.29 0.00 -0.33 0.00 -0.26 

ܽଵ 0.02 0.60 0.02 0.54 0.02 0.53 0.01 0.41 0.01 0.24 

Link to 

emerging 

market index

ܾ଴ -0.01 -0.45 0.00 0.14 

ܾଵ 0.07 5.52 0.06 5.00 

Link to S&P 

500 index

ܿ଴ -0.06 -2.65 -0.06 -2.80 

ܿଵ 0.00 0.07 -0.01 -0.89 

Link to bond 

index

݀଴ -0.06 -3.08 -0.06 -3.06 

݀ଵ 0.01 0.76 -0.01 -0.88 

Link to US 

dollar index 

݁଴ -0.02 -0.74 -0.01 -0.60 

݁ଵ -0.14 -6.68 -0.13 -6.43 

ܴଶ 1.20% 0.42% 0.30% 1.93% 3.59% 

The Full Sample Period (1/4/1988 to 10/29/2009) 

ܽ଴ 0.00 0.37 0.01 0.43 0.01 0.41 0.00 0.38 0.01 0.47 

ܽଵ -0.02 -0.50 -0.01 -0.35 -0.02 -0.60 -0.02 -0.75 -0.02 -0.75 

Link to 

emerging 

market index

ܾ଴ -0.02 -0.68 0.01 0.21 

ܾଵ 0.10 8.95 0.06 5.63 

Link to S&P 

500 index

ܿ଴ -0.08 -3.26 -0.07 -3.20 

ܿଵ 0.09 8.29 0.03 3.15 

Link to bond 

index

݀଴ -0.05 -2.61 -0.05 -2.70 

݀ଵ -0.04 -2.47 -0.03 -2.38 

Link to US 

dollar index 

݁଴ -0.02 -0.90 -0.02 -0.91 

݁ଵ -0.11 -6.70 -0.07 -4.91 

ܴଶ 7.47% 4.86% 1.09% 5.10% 10.82% 

 
43

Panel B: Regressions of Monthly GSCI Return 

Reg (1) Reg (2) Reg (3) Reg (4) Reg (5) Reg (6) Reg (7) 

est t-stat est t-stat est t-stat est t-stat est t-stat est t-stat est t-stat est t-stat 

The Pre-Crisis Period (1/4/1988 to 8/31/2008) 

ܽ଴ -0.02 -0.26 -0.01 -0.21 -0.01 -0.13 -0.02 -0.24 -0.02 -0.28 -0.02 -0.22 0.09 1.47 0.12 1.92 

ܽଵ 0.08 0.49 0.06 0.38 0.05 0.31 0.04 0.27 0.08 0.49 0.09 0.56 -0.35 -1.60 -0.52 -2.38 

Link to emerging 

market index

ܾ଴ -0.02 -0.24 0.04 0.57 

ܾଵ 0.07 1.50 0.10 1.37 

Link to S&P 500 

index

ܿ଴ -0.15 -1.53 -0.18 -1.72 

ܿଵ -0.02 -0.36 -0.05 -0.68 

Link to bond 

index

݀଴ -0.18 -3.09 -0.16 -2.54 

݀ଵ 0.03 0.64 -0.11 -2.82 

Link to US dollar 

index 

݁଴ -0.06 -0.68 -0.07 -0.88 

݁ଵ -0.16 -4.80 -0.16 -3.11 

Link to 

inflation rate 

݂଴ -0.07 -1.20 

݂ଵ 0.04 0.97 

Link to core 

inflation rate 

݂଴ -0.04 -0.75 -0.02 -0.45 

݂ଵ 0.07 1.58 0.05 1.14 

Link to global 

econ activity 

݃଴ 0.25 2.28 0.28 2.71 

݃ଵ -0.01 -0.24 -0.02 -0.69 

ܴଶ 0.93% 2.94% 2.88% 7.32% 0.6% 0.49% 2.19% 18.11% 

The Full Sample Period (1/4/1988 to 10/29/2009) 

ܽ଴ 0.03 0.39 0.03 0.52 0.05 0.78 0.04 0.66 0.03 0.40 0.03 0.42 0.15 2.33 0.17 2.94 

ܽଵ -0.09 -0.53 -0.06 -0.33 -0.20 -0.79 -0.15 -0.79 -0.07 -0.38 -0.07 -0.35 -0.66 -2.22 -0.62 -4.13 

Link to emerging 

market index

ܾ଴ -0.03 -0.35 0.04 0.58 

ܾଵ 0.14 3.97 0.05 0.76 

Link to S&P 500 

index

ܿ଴ -0.18 -1.76 -0.19 -1.81 

ܿଵ 0.13 3.59 0.06 1.08 

Link to bond 

index

݀଴ -0.18 -3.16 -0.16 -2.65 

݀ଵ 0.03 0.85 -0.04 -1.46 

Link to US dollar 

index 

݁଴ -0.05 -0.70 -0.07 -0.99 

݁ଵ -0.16 -3.98 -0.12 -2.07 

Link to 

inflation rate 

݂଴ -0.08 -1.36 

݂ଵ 0.10 2.97 

Link to core 

inflation rate 

݂଴ -0.04 -0.74 -0.02 -0.38 

݂ଵ 0.10 1.84 0.03 0.63 

Link to global 

econ activity 

݃଴ 0.28 2.51 0.29 3.27 

݃ଵ 0.04 1.35 0.01 0.30 

ܴଶ 11.24% 7.99% 2.98% 10.99% 4.77% 1.04% 7.07% 25.92% 

 
44

Table 4: Regressions of Daily Futures Returns of Non-energy Commodities 

This table reports regression results of daily returns of front-month futures contracts of non-energy 

commodities on oil return (ܴ௢௜௟,௧ሻ, separately and jointly with a set of control variables, including returns 

of the Morgan Stanley emerging market equity index (ܴாெ,௧ሻ, the S&P equity index (ܴௌ௉,௧ሻ, the JP 

Morgan Treasury bond index ሺܴ௃௉ெ,௧), and the US dollar index (ܴ௎ௌ஽,௧ሻ:

ܴ௜,௧

௡ ൌߙ ൅ሾߚ ൅ ௜଴ߚଵሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସ ൅ ߚଶሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସܫ௡௜ௗ௘௫ሿܴ௢௜௟,௧௡

 + ሾκ଴௜ ൅ κଵሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସ ൅ κଶሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସܫ௡௜ௗ௘௫ሿܴாெ,௧

௡

 + ሾߛ ൅ ௜଴ߛଵሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସ ൅ ߛଶሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସܫ௡௜ௗ௘௫ሿܴௌ௉,௧

௡

 +ሾߠ ൅ ௜଴ߠଵሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସ ൅ ߠଶሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସܫ௡௜ௗ௘௫ሿܴ௃ெ௉,௧

௡

 +ሾߟ ൅ ௜଴ߟଵሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସ ൅ ߟଶሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସܫ௡௜ௗ௘௫ሿܴ௎ௌ஽,௧

௧,௜ߝ ൅ ௡

We normalize each variable by its sample mean and standard deviation. The data sample goes from 

1/2/1998 to 10/29/2009. We report the results of each regression for the pre-crisis period from 1/4/1988 to 

8/31/2008 and for the whole sample period. The t-statistics are adjusted for heteroskedasticity and serial 

correlation using the Newey-West method with five lags. Panel A reports regression results for the full 

sample with all non-energy commodities. To save space, we skip the estimates for κ଴௜’s, ߛ‘௜଴s, ߠ‘௜଴s, and 

ߟ‘௜଴s in panel A. Panel B separately reports the estimates of the main variables of interests ߚଵ and ߚଶ in 

different subsamples of commodities, including the soybean complex (which includes soybeans, soybean 

meal and soybean oil), the grain sector, the soft sector, the livestock sector, and the metal sector, with the 

control variables. 

 
45

Panel A: Full Sample with All Non-Energy Commodities 

   

The Pre-Crisis Period 

(1/2/1998 to 8/31/2008) 

The Full Sample Period 

(1/2/1998 to 10/29/2009) 

est t-stat est t-stat est t-stat est t-stat 

α    0.00 0.03 0.00 -0.08 0.00 0.75 0.00 0.42 

Link of individual 

commodity with oil before 

2004 

 Corn 0.07 4.17 0.07 4.39 0.09 4.69 0.09 5.07 

 Wheat 0.04 2.36 0.05 2.62 0.06 2.98 0.07 3.48 

 Kansas Wheat 0.04 2.43 0.05 2.76 0.06 3.03 0.07 3.70 

 Soybean 0.06 3.34 0.06 3.25 0.09 4.55 0.08 4.50 

 Soybean Oil 0.05 2.69 0.05 2.54 0.11 5.01 0.09 4.94 

 Live Cattle -0.02 -0.88 -0.01 -0.39 -0.01 -0.54 0.00 0.23 

 Lean Hogs 0.00 -0.07 0.01 0.28 -0.05 -2.26 -0.02 -0.88 

 Feed Cattle -0.07 -3.78 -0.06 -3.15 -0.06 -2.87 -0.04 -1.95 

 Gold 0.15 7.09 0.12 6.57 0.08 3.34 0.07 3.45 

 Silver 0.12 5.93 0.09 5.10 0.10 4.00 0.06 2.96 

 Copper 0.10 5.02 0.09 4.78 0.16 6.87 0.12 5.75 

 Coffee -0.03 -1.06 -0.02 -1.02 -0.04 -1.84 -0.04 -1.58 

β0i Cocoa -0.02 -0.81 -0.02 -0.83 -0.03 -1.17 -0.02 -1.19 

 Cotton 0.04 1.90 0.04 2.13 0.04 2.12 0.05 2.61 

 Sugar 0.02 1.06 0.03 1.23 0.02 0.90 0.03 1.58 

 Rough Rice 0.02 0.81 0.02 0.99 0.02 0.81 0.03 1.38 

 Soybean Meal 0.05 2.72 0.05 2.57 0.07 3.35 0.07 3.35 

 Oat 0.02 0.76 0.02 0.73 0.04 2.02 0.04 2.00 

 Minn Wheat 0.06 3.39 0.06 3.56 0.09 4.46 0.09 4.91 

 Orange Juice -0.02 -1.07 -0.01 -0.53 -0.03 -1.32 0.00 -0.21 

 Lumber -0.02 -1.07 -0.02 -0.89 -0.03 -1.70 -0.03 -1.54 

 Platinum 0.09 4.85 0.08 4.16 0.11 4.81 0.08 3.89 

 Palladium 0.03 1.60 0.02 1.03 0.04 2.02 0.02 1.09 

 Pork Belly -0.02 -0.90 -0.01 -0.60 -0.06 -2.62 -0.03 -1.38 

Trend with oil after 2004 β1    0.06 10.48 0.04 8.21 0.05 15.14 0.03 8.81 

β2    0.02 3.55 0.02 2.61 0.02 5.01 0.02 4.26 

Trend with emerging 

market index after 2004 

κଵ 0.02 4.41 0.02 5.12 

κଶ 0.00 -0.39 -0.01 -1.88 

Trend with S&P 500 index 

after 2004 

 -3.03 -0.01 -1.09 -0.01 ଵߛ

 0.99 0.00 0.27 0.00 ଶߛ

Trend with bond index 

after 2004 

 -1.77 -0.01 -1.48 -0.01 ଵߠ

 -0.10 0.00 -0.91 -0.01 ଶߠ

Trend with US dollar after 

2004 

 -3.67 -0.01 -1.10 -0.01 ଵߟ

 -1.41 -0.01 -2.47 -0.02 ଶߟ

ܴଶ 2.13% 4.64% 5.04% 7.98% 

 
46

Panel B: Estimates of β1 and β2 in Different Commodity Sectors 

The Pre-Crisis Period 

(1/2/1998 to 8/31/2008)

The Full Sample Period 

(1/2/1998 to 10/29/2009)

est t-stat est t-stat 

Soybean Complex 

β1 0.07 4.29 0.05 4.51 

β2 0.06 2.90 0.04 3.08 

Grain Sector 

β1 0.06 6.84 0.05 4.51 

β2 0.04 3.21 0.04 3.08 

Soft Sector 

β1 0.02 2.07 0.02 2.71 

β2 0.02 1.56 0.02 2.11 

Livestock Sector 

β1 0.00 0.26 0.00 -0.26 

β2 0.01 0.35 0.03 2.91 

Metal Sector 

β1 0.05 5.42 0.03 4.20 

β2 0.03 2.08 0.02 1.80 

 
47

Table 5: Regressions of Daily Futures Returns of Non-energy Commodities with Lags 

This table reports regression results of daily returns of first-month futures contracts of non-energy 

commodities on oil return (ܴ௢௜௟,௧ሻ, separately and jointly with a set of control variables, including returns 

of the Morgan Stanley emerging market equity index (ܴாெ,௧ሻ, the S&P equity index (ܴௌ௉,௧ሻ, the JP 

Morgan Treasury bond index ሺܴ௃௉ெ,௧), and the US dollar index (ܴ௎ௌ஽,௧ሻ. We use two lags of oil return in 

the regression specification: 

ܴ௜,௧

௡ ൌߙ ൅ሾߚ ൅ ௜଴ߚଵሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସ ൅ ߚଶሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସܫ௡௜ௗ௘௫ሿܴ௢௜௟,௧௡

 + ሾߚଷ௜ ൅ ߚସሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସ ൅ ߚହሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସܫ௡௜ௗ௘௫ሿܴ௢௜௟,௧ିଵ

௡

 ൅ሾߚ ൅ ௜଺ߚ଻ሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସ ൅ ߚ଼ሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସܫ௡௜ௗ௘௫ሿܴ௢௜௟,௧ିଶ

௡

 + ሾκ଴௜ ൅ κଵሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସ ൅ κଶሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସܫ௡௜ௗ௘௫ሿܴாெ,௧

௡

 + ሾߛ ൅ ௜଴ߛଵሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସ ൅ ߛଶሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସܫ௡௜ௗ௘௫ሿܴௌ௉,௧

௡

 + ሾߠ ൅ ௜଴ߠଵሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସ ൅ ߠଶሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସܫ௡௜ௗ௘௫ሿܴ௃ெ௉,௧

௡

 +ሾߟ ൅ ௜଴ߟଵሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସ ൅ ߟଶሺݐ െ 2004ሻܫ௧ஹଶ଴଴ସܫ௡௜ௗ௘௫ሿܴ௎ௌ஽,௧

௧,௜ߝ ൅ ௡

We normalize each variable by its sample mean and standard deviation. The sample goes from 1/2/1998 

to 10/29/2009. We report the results of each regression for the pre-crisis period from 1/4/1988 to 

8/31/2008 and for the whole sample period. The t-stats are adjusted for heteroskedasticity and serial 

correlation using the Newey-West method with five lags. We only report the coefficients related to the 

trends with oil after 2004. We also report the F-statistic for testing ߚଶ ൅ ߚହ ൅ ߚ଼ ൑ 0 with 95% significance 

level as 2.71. 

 

The Pre-Crisis Period 

(1/2/1998 to 8/31/2008) 

The Full Sample Period 

(1/2/1998 to 10/29/2009) 

No Controls With Controls No Controls With Controls 

est t-stat est t-stat est t-stat est t-stat 

Trend with oil after 2004 

Lag0 β1 0.06 10.35 0.04 7.91 0.05 15.08 0.03 8.80 

β2 0.02 3.56 0.02 2.58 0.02 5.03 0.02 4.22 

Lag1 β4 -0.01 -2.38 -0.01 -2.77 0.00 1.03 0.00 -0.70 

β5 0.00 0.09 0.00 -0.31 -0.01 -2.87 -0.01 -2.99 

Lag2 β7 -0.01 -1.37 0.00 -0.66 0.00 -0.92 0.00 -0.79 

β8 0.00 0.63 0.00 0.60 0.00 -0.05 0.00 -0.11 

F-test for β2+ β5+ β8൑0 8.07 3.43 3.35 1.48 

 
48

Table 6: Regression Analysis of Spot Returns and Slope Changes of Futures Curves 

This table reports regression results of daily spot returns and slope changes of futures curves. Panel A 

pools the daily spot returns of 16 non-energy commodities: corn, soybeans, wheat, Kansas wheat, 

soybean oil, Minnesota wheat, oats, cotton, sugar, live cattle, lean hogs, gold, silver, copper, platinum, 

and palladium, and regresses them on oil spot return and a set of control variables, including returns of the 

Morgan Stanley emerging market equity index, the S&P equity index, the JP Morgan Treasury bond 

index, and the US dollar index based on regression specification (3). Panel B pools together the daily 

slope changes of all non-energy commodities listed in Table 1 and regresses them on slope change of oil 

based on regression specification (5). For both Panels A and B, we report the results of each regression 

for the pre-crisis period from 1/4/1988 to 8/31/2008 and for the whole sample period. We only report the 

coefficients related to the trends with oil after 2004. The t-statistics are adjusted for heteroskedasticity and 

serial correlation using the Newey-West method with five lags. 

Panel A: Spot Returns 

The Pre-Crisis Period 

(1/2/1998 to 8/31/2008)

The Full Sample Period 

(1/2/1998 to 10/29/2009)

est t-stat est t-stat 

β1 0.03 2.83 0.00 0.17 

β2 0.03 2.96 0.03 4.43 

Panel B: Slope Changes 

The Pre-Crisis Period 

(1/2/1998 to 8/31/2008)

The Full Sample Period 

(1/2/1998 to 10/29/2009)

est t-stat est t-stat 

β1 0.01 1.30 0.00 0.08 

β2 0.00 -0.65 -0.01 -0.50 

 
49

Table 7: Regression Analysis of Volatility of Non-energy Commodities 

We analyze volatility of daily returns of all non-energy commodities between 1/2/1998 and 10/29/2009. 

We normalize return of each commodity by its volatility before 2004 and its whole sample mean. We 

filter out from return of each commodity a set of control variables (including returns of the Morgan 

Stanley emerging market index, the S&P 500 index, the JP Morgan Treasury bond index, and the US 

dollar index, the core CPI inflation rate, and change of the global shipping index) and oil return by using 

regression specification (7). Then, we regress the normalized raw returns, the residual returns after 

filtering out the set of control variables, and the residual returns after filtering out the set of control 

variables and oil return onto a set of index and year dummies: 

൫ܴ௜,௧

௡ ൯

ଶ ൌ ܽ଴௜ ൅ ܾ଴ସܫ௬ୀ଴ସ ൅ ܾ଴ହܫ௬ୀ଴ହ ൅ ܾ଴଺ܫ௬ୀ଴଺ ൅ ܾ଴଻ܫ௬ୀ଴଻ ൅ ܾ଴଼ܫ௬ୀ଴଼

                                ൅ܾ଴ଽܫ௬ୀ଴ଽ+ܿ଴ହܫ௡௜ௗ௘௫ܫ௬ୀ଴ସ ൅ ܿ଴ହܫ௡௜ௗ௘௫ܫ௬ୀ଴ହ ൅ ܿ଴଺ܫ௡௜ௗ௘௫ܫ௬ୀ଴଺

     ൅ܿ଴଻ܫ௡௜ௗ௘௫ܫ௬ୀ଴଻ ൅ ܿ଴଼ܫ௡௜ௗ௘௫ܫ௬ୀ଴଼ ൅ ܿ଴ଽܫ௡௜ௗ௘௫ܫ௬ୀ଴ଽ ൅ ߝ௜,௧

To save space, we only report estimates of coefficients related to changes of volatility in years after 2004. 

The t-statistics are adjusted for heteroskedasticity and serial correlation using the Newey-West method 

with five lags. 

 Raw Returns Residual Returns after 

Control Variables 

Residual Returns after 

Control Variables and 

Oil Return 

estimate t-stat estimate t-stat estimate t-stat

Baseline 

Effects 

ܾ଴ସ 0.25 3.52 0.19 2.84 0.19 2.85 

ܾ଴ହ -0.09 -1.62 -0.12 -2.18 -0.13 -2.29 

ܾ଴଺ -0.01 -0.15 -0.08 -1.36 -0.10 -1.58 

ܾ଴଻ -0.07 -1.16 -0.12 -2.09 -0.13 -2.21 

ܾ଴଼ 1.45 10.80 1.10 9.40 0.96 8.70 

ܾ଴ଽ 0.78 8.59 0.55 6.75 0.51 6.54 

Diff-inDiff 

Effects 

ܿ଴ସ 0.34 3.55 0.26 2.84 0.25 2.74 

ܿ଴ହ 0.09 1.26 0.07 1.07 0.06 0.93 

ܿ଴଺ 0.54 4.63 0.45 4.44 0.38 4.06 

ܿ଴଻ 0.25 3.35 0.17 2.35 0.14 1.93 

ܿ଴଼ 0.68 3.42 0.43 2.55 0.24 1.59 

ܿ଴ଽ 0.37 2.88 0.22 1.90 0.14 1.29 

ܴଶ 4.5% 3.2% 2.6%