Index Investment and the Financialization of Commodities
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Financial Analysts Journal
·
Volume 68 Number 6
©2012 CFA Institute
Index Investment and the
Financialization of Commodities
Ke Tang and Wei Xiong
The authors found that, concurrent with the rapidly growing index investment in commodity markets since
the early 2000s, prices of non-energy commodity futures in the United States have become increasingly cor-
related with oil prices; this trend has been significantly more pronounced for commodities in two popular
commodity indices. This finding reflects the financialization of the commodity markets and helps explain the
large increase in the price volatility of non-energy commodities around 2008.
S
ince the early 2000s, commodity futures have ity markets were partly segmented from outside
emerged as a popular asset class for many financial markets and from each other. Recognition
financial institutions. According to a staff of the potential diversification benefits of investing
report from the U.S. Commodity Futures Trading in the segmented commodity markets prompted
Commission (CFTC 2008), the total value of various the rapid growth of commodity index investment
commodity index–related instruments purchased after the early 2000s and precipitated a fundamen-
by institutional investors increased from an esti- tal process of financialization among commodity
mated $15 billion in 2003 to at least $200 billion in markets. The focus of our study was to analyze the
mid-2008. Several observers and policymakers (see, consequences of this financialization process.
e.g., Masters 2008; U.S. Senate Permanent Subcom- ■ Discussion of findings. In our analysis, we
mittee on Investigations 2009) have expressed a homed in on a salient empirical pattern of greatly
strong concern that index investment as a form of increased price comovements between various
financial speculation might have caused unwar- commodities after 2004, when significant index
ranted increases in the cost of energy and food and investment started to flow into commodity mar-
induced excessive price volatility. kets. Because index investors typically focus on
What is the economic impact of the rapid strategic portfolio allocation between the commod-
growth of commodity index investment? To ity class and other asset classes, such as stocks and
answer this question, we must first recognize the bonds, they tend to trade in and out of all com-
concurrent development of commodity markets modities in a given index at the same time (see,
precipitated by the rapid growth of commodity e.g., Barberis and Shleifer 2003). As a result, their
index investment. Prior to the early 2000s, despite increasing presence should have a greater impact
the liquid futures contracts traded on many com- on commodities in the two most popular commod-
modities, commodity prices provided a risk pre- ity indices—the S&P GSCI and the Dow Jones-UBS
mium for idiosyncratic commodity price risk (Bes- Commodity Index (DJ-UBSCI)—than on commodi-
sembinder 1992; de Roon, Nijman, and Veld 2000) ties off the indices. Consistent with this hypoth-
and had little comovement with stocks (Gorton and esis, we found that futures prices of non-energy
Rouwenhorst 2006) or each other (Erb and Harvey commodities became increasingly correlated with
2006). These aspects are in sharp contrast to the oil after 2004. In particular, this trend was signifi-
price dynamics of typical financial assets, which cantly more pronounced for indexed commodities
carry a premium for systematic risk only and are than for off-index commodities after controlling for
highly correlated with both market indices and a set of alternative arguments. Although the trend
each other. This contrast indicates that commod- intensified after the recent world financial crisis,
triggered by the bankruptcy of Lehman Brothers
Ke Tang is associate professor of finance at Renmin in September 2008, its presence was already evi-
University of China, Beijing. Wei Xiong is professor dent and significant before the crisis. In addition,
of economics at Princeton University, New Jersey, and the greater increases in the correlations of indexed
research associate at the National Bureau of Economic commodities with oil are not simply due to the illi-
Research, Cambridge, Massachusetts. quidity of off-index commodities.
54 www.cfapubs.org ©2012 CFA InstituteIndex Investment and the Financialization of Commodities
There is also evidence of an increasing return Large Trader Reporting Program. They found that
correlation between commodities and the MSCI after the financial crisis erupted in September 2008,
Emerging Markets Index in recent years. This evi- the financial distress of commodity index traders
dence confirms the increasing importance of com- and hedge funds caused them to demand liquid-
modity demands from rapidly growing emerg- ity from commercial hedgers rather than provide
ing economies in determining commodity prices. liquidity to commercial hedgers.
However, comovements of commodity futures Our emphasis on the price comovements of
prices in China remained stable over 2006–2008, in commodities distinguishes our study from those on
sharp contrast to the large increases in the United the returns and risk premiums of commodities (see,
States. This contrast suggests that the increases in e.g., Fama and French 1987; Bessembinder 1992;
commodity price comovements were not caused Bailey and Chan 1993; de Roon, Nijman, and Veld
solely by changes in the supply of and demand for 2000; Erb and Harvey 2006; Gorton, Hayashi, and
commodities driven by emerging economies. Rouwenhorst 2007; Hong and Yogo 2012; Acha-
Price comovements among various commodi- rya, Lochstoer, and Ramadorai 2009). Those papers
ties were also high in the 1970s and early 1980s. focus on the roles of macroeconomic risk, produc-
When the U.S. economy was hit by persistent oil ers’ hedging incentives, and commodity invento-
supply shocks and stagflation, the double-digit ries in determining the cross-sectional and time-
inflation rate, accompanied by high inflation vola- series properties of commodity risk premiums.
tility, coincided with a period of high return cor- Our analysis corroborates that of Pindyck and
relations among commodities (with an average Rotemberg (1990), who found that common mac-
around 0.3). In contrast, in the last few years of the roshocks cannot fully explain the comovements of
past decade, the increases in commodity return cor- commodity prices between 1960 and 1985. More-
relations were not only larger in magnitude (with over, we focused our analysis on connecting the
an average correlation of more than 0.5) but also large inflow of commodity index investment with
different in nature. They emerged while inflation the large increase in commodity price comove-
and inflation volatility remained subdued through- ments in recent years by examining the difference
out the past decade. in these comovements between indexed and off-
The increased commodity price comovements index commodities. This identification strategy is
reflect the financialization process precipitated built on the finding of Barberis, Shleifer, and Wur-
by the rapid growth of commodity index invest- gler (2005) that after a stock is added to the S&P
ment. This process can have significant economic 500 Index, its price comovement with the index
consequences for commodity markets. On the one increases significantly.
hand, the presence of commodity index investors Several contemporaneous papers (see, e.g.,
can lead to a more efficient sharing of commodity Büyüküahin, Haigh, and Robe 2010; Silvennoinen
price risk; on the other hand, their portfolio rebal- and Thorp 2010) have also found that the return
ancing can spill price volatility from outside mar- correlation between commodities and stocks rose
kets on and across commodity markets (see, e.g., substantially during the recent financial crisis
Kyle and Xiong 2001). Although the post-2004 data but not before. Different from those analyses, our
sample may be too short to give a reliable measure analysis highlights that the increase in the correla-
of changes in commodity risk premiums, it is suf- tions between the returns of various commodity
ficient for uncovering a significant volatility spill- futures started long before the crisis and cannot be
over effect: In 2008, indexed non-energy commodi- attributed solely to the crisis. Instead, it identifies
ties had higher price volatility than their off-index the role of index investors in linking various com-
counterparts, and this difference was partly related modity markets with each other and with outside
to the greater return correlations of indexed com- financial markets. In this regard, our study com-
modities with oil. plements Etula (2009), who showed that the risk-
The changes in commodity price correlation bearing capacity of security brokers and dealers is
and volatility have profound implications for a wide an important determinant of risk premiums and
range of issues, from commodity producers’ hedg- return volatility in commodity markets.
ing strategies and speculators’ investment strategies
to many countries’ energy and food policies. We
Commodities and Increased Price
expect these effects to persist so long as index invest-
ment strategies remain popular among investors. Comovements
Following up on the theme of our study, Cheng, We focused on commodities with active futures
Kirilenko, and Xiong (2012) analyzed the futures contracts traded in the United States. In recent
positions taken by individual traders in the CFTC’s years, 28 such commodities have been available.
November/December 2012 www.cfapubs.org 55Financial Analysts Journal
We obtained daily futures prices and open interests return correlation between soybeans and oil moved
on these commodities from Pinnacle Data Corp.1 around zero inside a narrow range between 0.2
Table 1 lists and classifies these commodities in five and –0.1. Over 2004–2009, the correlation steadily
sectors: energy, grains, softs, livestock, and metals.2 climbed, from 0.1 to near 0.6, and this trend is sig-
The energy sector contains four commodi- nificantly different from zero. Similarly, Panels B,
ties: WTI (West Texas intermediate grade) crude C, and D show that oil had low return correlations
oil, heating oil, gasoline, and natural gas. Crude with cotton, live cattle, and copper before 2004 and
oil is the most important component of this sec- that the correlations gradually rose to 0.5, 0.5, and
tor because heating oil and gasoline are refined oil 0.6, respectively, in 2009.5 Although the correlations
products, whose prices move closely with crude dropped in 2010 and 2011, they remained substan-
oil. The grains sector contains nine commodities: tially higher than they were before 2004. Taken
corn, Chicago wheat, Kansas wheat, Minneapolis together, these graphs show that the return corre-
wheat, soybeans, soybean oil, soybean meal, rough lations of a broad set of non-energy commodities
rice, and oats. These grains are substitutes for each with oil were low before 2004, consistent with the
other as food for humans and animals. The softs finding of Erb and Harvey (2006), but have been
sector is a mix of tropical products that are grown steadily increasing since 2004.
primarily in tropical and subtropical regions: cof-
fee, cotton, sugar, cocoa, lumber, and orange juice. Economic Mechanisms
It is common practice to classify them together in
What has caused the increases in return correla-
one sector, although the links between them are not
tions among seemingly unrelated commodities in
as close as in other sectors. The livestock sector has
recent years? To answer this question, we exam-
four commodities: feeder cattle, lean hogs, live cat-
ined several possible economic mechanisms.
tle, and pork bellies. They are substitutes for each
other and are primarily used for human consump- Financialization of Commodities. Evidence
tion. The metals sector contains five commodities: suggests that before the early 2000s, commodity
gold, silver, copper, platinum, and palladium.3 markets were partly segmented from outside finan-
They are used as both investments and inputs for cial markets and from each other. Erb and Harvey
industrial production. (2006) showed that commodities had only low
Figure 1 depicts prices of oil (energy), soy- positive return correlations with each other. Gorton
beans (grains), cotton (softs), live cattle (livestock), and Rouwenhorst (2006) demonstrated that com-
and copper (metals) since 1991. The figure indicates modity returns had negligible correlations with the
synchronized boom and bust cycles among these S&P 500 return, especially at short horizons, such
seemingly unrelated commodities after 2004. Here, as daily and monthly. Bessembinder (1992) and de
we provide some preliminary analysis of increased Roon, Nijman, and Veld (2000) found that returns
return correlations among these commodities in of commodity futures increased with net short
recent years. positions of commodity hedgers after controlling
For each commodity, we followed Gorton and for systematic risk. These attributes are in sharp
Rouwenhorst (2006) and Erb and Harvey (2006) contrast to those of typical financial assets, such as
in constructing a return index from rolling the stocks, whose prices carry a premium for system-
first-month futures contract. More specifically, we atic risk only and tend to have significant return
constructed a hypothetical investment position in correlations with each other (even if they share
the first-month futures contract of the commodity few fundamentals). Instead, these attributes reflect
on a fully collateralized basis.4 We held the con- inefficient sharing of commodity price risk, which
tract until the seventh calendar day of its maturity underlies the long-standing hedging pressure the-
month before rolling it into the next contract. The ory of commodity prices that dates back to Keynes
excess return of this hypothetical investment repre- (1930), Hicks (1939), and, more recently, Hirshleifer
sents the excess futures return to the initial capital (1988). This influential theory posits that commod-
(because interest still accrues on the capital): ity hedgers need to offer a positive risk premium to
( ) ( )
Ri,t = ln Fi,t ,T − ln Fi ,t −1,T ,
induce speculators to share the idiosyncratic risk of
the long positions.
where Fi,t,T is the price of the futures contract held After the equity market collapsed in 2000,
on date t with maturity date T. many institutions considered commodities a new
Figure 2 depicts the one-year rolling return asset class after the widely publicized discovery of
correlations of oil with soybeans, cotton, live cat- a negative correlation between commodity returns
tle, and copper, together with the 95% confidence and stock returns by Greer (2000), Gorton and Rou-
levels. Panel A shows that over 1986–2004, the wenhorst (2006), and Erb and Harvey (2006). As a
56 www.cfapubs.org ©2012 CFA InstituteIndex Investment and the Financialization of Commodities
Table 1. Commodity Futures Traded in the United States and Weights in the S&P GSCI and DJ-UBSCI
Start of Start of
S&P DJ- Futures in Futures in
Commodity GSCI UBSCI Exchangea Contract U.S. China
Energy
WTI crude oil 40.6% 15.0% NYMEX Every month 30/Mar/83
Heating oil 5.3 4.5 NYMEX Every month 14/Nov/78 25/Aug/04
RBOB gasoline 4.5 4.1 NYMEX Every month 18/Apr/06
Natural gas 7.6 16.0 NYMEX Every month 4/Apr/90
Grains
Corn 3.6% 6.9% CME Group Mar, May, Jul, Sep, Dec 1/Jul/59 22/Sep/04
Soybeans 0.9 7.4 CME Group Jan, Mar, May, Jul, Aug, Sep, Nov 1/Jul/59 4/Jan/99
Chicago wheat 3.0 3.4 CME Group Mar, May, Jul, Sep, Dec 1/Jul/59 4/Jan/99
Kansas wheat 0.7 0 KCBT Mar, May, Jul, Sep, Dec 5/Jan/70
Soybean oil 0 2.9 CME Group Jan, Mar, May, Jul, Aug, Sep, Oct, Dec 1/Jul/59
Minneapolis wheat 0 0 MGE Mar, May, Jul, Sep, Dec 5/Jan/70
Soybean meal 0 0 CME Group Jan, Mar, May, Jul, Aug, Sep, Oct, Dec 1/Jul/59
Rough rice 0 0 CME Group Jan, Mar, May, Jul, Sep, Nov 20/Aug/86
Oats 0 0 CME Group Mar, May, Jul, Sep, Dec 1/Jul/59
Softs
Coffee 0.5% 2.7% ICE Mar, May, Jul, Sep, Dec 16/Aug/72
Cotton 0.7 2.2 ICE Mar, May, Jul, Oct, Dec 1/Jul/59 1/Jun/04
Sugar 2.1 2.8 ICE Mar, May, Jul, Oct 4/Jan/61 6/Jan/06
Cocoa 0.2 0 ICE Mar, May, Jul, Sep, Dec 1/Jul/59
Lumber 0 0 CME Group Jan, Mar, May, Jul, Sep, Nov 1/Oct/69
Orange juice 0 0 ICE Jan, Mar, May, Jul, Sep, Nov 1/Feb/67
Livestock
Feeder cattle 0.3% 0% CME Group Jan, Mar, Apr, May, Aug, Sep, Oct 30/Nov/71
Lean hogsb 0.8 2.5 CME Group Feb, Apr, May, Jul, Aug, Oct, Dec 28/Feb/66
Live cattle 1.6 4.1 CME Group Feb, Apr, Jun, Aug, Oct, Dec 30/Nov/64
Pork bellies 0 0 CME Group Feb, Mar, May, Jul, Aug 18/Sep/61
Metals
Goldc 1.5% 6.1% NYMEX Feb, Apr, Jun, Aug, Oct, Dec 31/Dec/74 1/Jan/08
Silverc 0.2 2.4 NYMEX Jan, Mar, May, Jul, Sep, Dec 12/Jun/63
Copperd 2.6 6.7 NYMEX Mar, May, Jul, Sep, Dec 3/Jan/89 12/May/97
Platinumc 0 0 NYMEX Jan, Apr, Jul, Oct 4/Mar/68
Palladiumc 0 0 NYMEX Mar, Jun, Sep, Dec 3/Jan/77
Notes: This table lists all the commodities with futures contracts traded in the United States. The weights of these commodities
in the S&P GSCI and DJ-UBSCI contracts are taken from 2008. The S&P GSCI and DJ-UBSCI also include commodities traded
in London, which are not included in our analysis.
aNYMEX represents the New York Mercantile Exchange. KCBT represents the Kansas City Board of Trade. MGE represents
the Minneapolis Grain Exchange.
bA June contract has been added to the lean hog futures series since 2002. Because this new contract has a low open interest,
we omitted it from our analysis.
cContracts include the current month and the next two consecutive months plus those contracts listed in the table. However,
because the open interest of these short-maturity contracts (with maturities less than three months) is typically small, we omitted
them from our analysis.
dThe S&P GSCI uses copper contracts traded on the London Metal Exchange, whereas the DJ-UBSCI uses those from the NYMEX.
We followed the convention of the DJ-UBSCI and chose March, May, July, September, and December for copper contracts.
November/December 2012 www.cfapubs.org 57Financial Analysts Journal
Figure 1. Commodity Futures Prices, January 1991–July 2011
Normalized Price (Jan/91 = 100)
500
100
50
92 94 96 98 00 02 04 06 08 10
Oil Soybeans Cotton Live Cattle Copper
Notes: This figure depicts the futures prices of five commodities—oil, soybeans, cotton, live
cattle, and copper. We normalized the price of each commodity in January 1991 to be 100
and used a logarithmic scale.
result, billions of investment dollars flowed into a particular commodity.7 Investors can use three
commodity markets from financial institutions, types of financial instruments to gain exposure to
insurance companies, pension funds, foundations, the return of a commodity index: commodity index
hedge funds, and wealthy individuals. The large swaps, exchange-traded funds, and exchange-
index investment flow precipitated a fundamen- traded notes.8
tal process of financialization among commodity Table 1 provides the weights of the 28 com-
markets—the focus of our analysis. modities in the S&P GSCI and DJ-UBSCI in 2008.
■ Commodity indices. The most popular com- Both indices incorporate a wide range of commod-
modity investment strategy is to invest in a bas- ity futures. Some commodities are in neither index:
ket of commodities in a given commodity index. Minneapolis wheat, soybean meal, rough rice, and
A commodity index functions like an equity index, oats in the grains sector; lumber and orange juice
such as the S&P 500, in that its value is derived from in the softs sector; pork bellies in the livestock sec-
the total value of a specified basket of commodities. tor; and platinum and palladium in the metals sec-
Each commodity in the basket is assigned a partic- tor. The composition of these indices has remained
ular weight. Commodity indices typically build on stable in recent years. Furthermore, the set of the
the values of futures contracts, which are usually S&P GSCI and DJ-UBSCI also covers almost all the
nearby contracts with delivery times longer than a commodities in other, less popular indices.
month,6 to avoid the cost of holding physical com- The energy sector carries a much greater weight
modities. When a first-month contract matures than the other sectors in the S&P GSCI and DJ-
and the second-month contract becomes the first- UBSCI. The four energy commodities listed in Table
month contract, a commodity index specifies the 1 add up to 58% of the S&P GSCI and 39.6% of the
so-called roll (i.e., replacing the current contract in DJ-UBSCI. WTI crude oil alone accounts for 40.6%
the index with a following contract). In this way, of the S&P GSCI. Because the commodities in the
commodity indices provide returns comparable to energy sector move closely with each other, we used
passive long positions in listed commodity futures crude oil as a focal point in our analysis of the price
contracts. By far the largest two indices by market comovements of non-energy commodities and oil.
share are the S&P GSCI and the DJ-UBSCI. These ■ Index investment flow. Each Friday, the
two indices use different selection and weighting CFTC releases a weekly Commitments of Traders
schemes: the S&P GSCI is weighted by each com- report, which includes a Supplemental Commod-
modity’s world production, whereas the DJ-UBSCI ity Index Trader (CIT) report. The CIT report shows
relies on the relative amount of trading activity of the positions of a set of index traders identified
58 www.cfapubs.org ©2012 CFA InstituteIndex Investment and the Financialization of Commodities
Figure 2. Rolling Return Correlation of Oil with Various Non-Energy Commodities
A. Soybean–Oil Pair B. Cotton–Oil Pair
Correlation Correlation
1.0 1.0
0.8 0.8
0.6 0.6
0.4 0.4
0.2 0.2
0 0
–0.2 –0.2
–0.4 –0.4
88 90 92 94 96 98 00 02 04 06 08 10 88 90 92 94 96 98 00 02 04 06 08 10
C. Live Cattle–Oil Pair D. Copper–Oil Pair
Correlation Correlation
1.0 1.0
0.8 0.8
0.6 0.6
0.4 0.4
0.2 0.2
0 0
–0.2 –0.2
–0.4 –0.4
88 90 92 94 96 98 00 02 04 06 08 10 90 92 94 96 98 00 02 04 06 08 10
Coefficient ρ 95% Confidence Level
Notes: This figure depicts the one-year rolling return correlation of oil with soybeans, cotton, live cattle, and
copper, together with the 95% confidence levels, in Panels A, B, C, and D, respectively. Panels A, B, and C
start in 1986 because the trading of oil futures started only in March 1983. We omitted the data for 1983–1984
to avoid potential liquidity problems at the beginning and used returns after 1985 to measure correlations.
With the one-year rolling window, our correlation measures start in 1986. Panel D starts in 1990 because the
trading of copper futures started only in January 1989.
by the CFTC in 12 agricultural commodities since 12
3 January 2006. These commodities include corn, ( )
IFt = ∑ NLi ,t − NLi ,t −1 Pi ,t −1 , (1)
i =1
soybeans, Chicago wheat, Kansas wheat, and soy-
bean oil in the grains sector; coffee, cotton, sugar, where NLi,t represents the net long position of
and cocoa in the softs sector; and feeder cattle, lean index traders in commodity i in week t and Pi,t–1 is
hogs, and live cattle in the livestock sector. This the price of the commodity in week t – 1. In this cal-
list coincides with the set of the S&P GSCI and DJ- culation, we use prices of first-month futures con-
UBSCI in these three sectors. The CIT report covers tracts and assume that all position changes occur
no commodities in the energy and metals sectors. during the previous week. Then, we can add the
We can construct the investment flow from index flow from the first week of 2006 (the begin-
index traders in and out of the 12 commodities ning of the CIT report data) to any week before 29
each week by summing the dollar values of their October 2009 to obtain the cumulated index flow
net position changes in these commodities: up to that week.
November/December 2012 www.cfapubs.org 59Financial Analysts Journal
Figure 3 depicts the cumulated index flow, class can cause commodities in the index to move
together with the S&P GSCI agriculture and live- together (see, e.g., Barberis and Shleifer 2003). In
stock excess return index, over January 2006– other words, we would expect price comovements
October 2009. This index follows the performance of commodities in the S&P GSCI and DJ-UBSCI
of the same three sectors—grains, softs, and to be greater than those of off-index commodi-
livestock—that are covered by the CIT report. Fig- ties. Consistent with this theory, Barberis, Shleifer,
ure 3 shows that since the beginning of 2006, these and Wurgler (2005) found that in stock markets,
three sectors have had a large net inflow, which a stock’s listing on the S&P 500 can significantly
cumulated to nearly $20 billion in early 2008. Then, increase its return correlation with the index. Moti-
an outflow led to a cumulated index flow of –$5 bil- vated by these studies, we exploited the difference
lion by March 2009. The figure also shows that fluc- between the return correlations of indexed and off-
tuations of the S&P GSCI agriculture and livestock index commodities with oil to identify the increas-
return index were in sync with the index flow. Our ing presence of index investors in the commodity
regression of the weekly commodity index return markets. We chose oil as a focal point because of
on the index flow also yielded a positive and signifi- its dominant weight in the two most popular com-
cant coefficient. The significant correlation between modity indices. In particular, we examined the fol-
the commodity return and the index flow does not lowing empirical hypothesis regarding the change
represent causality, which can go from the index in this difference after 2004:
flow to the commodity return or vice versa.9 We Hypothesis I. After 2004, non-energy commodities
exploited the difference between indexed and off- in the S&P GSCI and DJ-UBSCI had greater increases in
index commodities to identify the causality.10 return correlations with oil than did off-index commodities.
■ Identification strategy. Index investors are An implicit assumption in this hypothesis is
not particularly sensitive to prices of individual that other participants in commodity markets, such
commodities because they tend to move in and as traditional speculators and commercial hedgers,
out of all commodities in a given index at the same have a limited capacity to absorb trades by index
time on the basis of the strategic allocation of their investors. As a result, the growing presence of
capital to commodities versus other asset classes, index investors can affect commodity prices. Note
such as stocks and bonds. As a result, any shock also that potential substitutions between closely
to their strategic allocation to the commodity related commodities by consumers and producers
Figure 3. Cumulated Index Flow and S&P GSCI Agriculture and Livestock
Excess Return Index, January 2006–October 2009
S&P GSCI Agriculture and Livestock Excess Return Index Index Flow ($ billions)
200 20
Cumulated Index Flow
150 10
S&P GSCI Agriculture and Livestock
100 Excess Return Index 0
50 –10
06 07 08 09 10
Notes: This figure depicts the cumulated index flow to the 12 agricultural and livestock com-
modities covered by the CFTC’s CIT report, together with the S&P GSCI agriculture and live-
stock excess return index. We computed the weekly flow to each commodity according to Equa-
tion 1 and the cumulated flow to each commodity by adding the weekly flow from the first
week of 2006 to a given week. By summing the cumulated flows to the 12 commodities, we
obtained the cumulated index flow.
60 www.cfapubs.org ©2012 CFA InstituteIndex Investment and the Financialization of Commodities
can partly transmit the price impact of index inves- ulated unprecedented demands for a broad range
tors to off-index commodities.11 For example, if of commodities in various sectors, such as energy
prices of corn rise far above those of soybean meal, and metals, and thus may have led to a joint price
consumers will substitute soybean meal for corn to boom for these commodities.
feed their animals. Similarly, if prices of corn rise The commodity demands from the emerg-
far above those of oats, farmers will allocate more ing economies depend positively on the strength
farmland to corn than to oats. But these substitution of their economic growth and negatively on the
effects are likely to be imperfect and to operate at price of the U.S. dollar, which is widely used to
horizons longer than those of futures trading, such settle commodity transactions. We used the MSCI
as the daily horizon that we focused on in our study. Emerging Markets Index to proxy for the growth
The choice of 2004 as the breakpoint is innocu- of emerging economies. We also used the return
ous because our main results build on trends in of the U.S. Dollar Index futures traded on ICE
return correlations between non-energy commodi- (IntercontinentalExchange) to track price fluctua-
ties and oil. Our use of daily, rather than weekly tions of the U.S. dollar. Underlying this futures
or monthly, data allowed us to reliably measure contract is an index that weights dollar exchange
changes in return volatility and correlation in the rates with six currencies (euro, Japanese yen, Brit-
United States after 2004. ish pound, Canadian dollar, Swedish krona, and
One might argue that trading by index inves- Swiss franc). We obtained data on these two indi-
tors has a greater impact on commodities that carry ces from Bloomberg.
a greater weight in the commodity indices. How- Figure 4 depicts the one-year rolling correla-
ever, because their index weights, by construction, tion between daily returns of the S&P GSCI and the
are matched by their greater world production MSCI Emerging Markets Index. Before 2004, the
and higher trading liquidity in futures markets, correlation fluctuated, mostly around zero, except
we would expect these commodities to be able to that it dropped to –0.4 during the Gulf War in
absorb more capital inflow and outflow. Thus, we 1990–1992. The war caused stock prices to fall and
chose to focus on the difference in return correla- oil prices to soar. Interestingly, after 2004, the cor-
tions between commodities in and off the S&P GSCI relation rose gradually, from around 0 to above 0.5
and DJ-UBSCI, rather than between commodities after 2009. This rising trend is consistent with the
with greater and lesser index weights. increasingly important effects of emerging econo-
Because the S&P GSCI and DJ-UBSCI are con- mies on commodity prices in recent years. Figure 4
structed by rolling front-month futures contracts of also shows a clear decreasing trend in return cor-
individual commodities, we focused most of our relations between the S&P GSCI and the U.S. Dollar
analysis on the returns from rolling these front- Index. Before 2004, this correlation fluctuated inside
month futures contracts. A subtle issue is whether a narrow band between –0.2 and 0.2. After 2004, it
the growth of index investment has affected spot dropped steadily, from around 0 to below –0.4 after
prices in the same way, which depends on the 2009. This trend is consistent with both growing
effectiveness of arbitrageurs in synchronizing spot commodity demands from emerging economies
prices and futures prices. If the price of the front- and increasing commodity index investment from
month futures contract of a commodity becomes too outside the United States. In our regression analysis
expensive relative to its spot price—after adjusting of price comovements of non-energy commodities
for interest cost and storage cost for carrying the and oil, we used the MSCI Emerging Markets Index
commodity from now until the contract’s delivery and the U.S. Dollar Index to control for the effects
date—it becomes profitable for arbitrageurs to short of commodity demands from emerging economies.
the contract and simultaneously carry the commod- Despite the important effects of emerging
ity. Thus, we would expect arbitrageurs to spread economies on commodity prices, it remains unclear
the price impact of index investment from front- whether they have driven the increased price cor-
month futures contracts to spot prices if the interest relations across the broad range of commodities
cost and storage cost incurred in such carry trades since 2004. To address this question, we collected
are independent of growing index investment. We
futures prices of commodities traded in China, the
also examined the correlations of spot returns.
growth engine of emerging economies in the past
Rapid Growth of Emerging Economies. The decade, from Wind (a widely used vendor of finan-
rapid growth of China, India, and other emerging cial data in China). China has been gradually intro-
economies is a popular explanation for the recent ducing futures contracts on a set of commodities
commodity price boom (see, e.g., Krugman 2008; since the late 1990s. Table 1 lists these commodities
Hamilton 2009; Kilian 2009). The development of and the starting dates of futures trading in China.
these emerging economies in the past decade stim- Figure 5 depicts front-month futures prices for six
November/December 2012 www.cfapubs.org 61Financial Analysts Journal
Figure 4. Rolling Return Correlations of the S&P GSCI with the MSCI Emerging
Markets Index (January 1989–July 2011) and the U.S. Dollar Index
(January 1987–July 2011)
A. S&P GSCI and MSCI B. S&P GSCI and U.S. Dollar Index
Emerging Markets Index
Correlation Correlation
0.8 0.6
0.6 0.4
0.4 0.2
0.2 0
0 –0.2
–0.2 –0.4
–0.4 –0.6
–0.6 –0.8
90 92 94 96 98 00 02 04 06 08 10 88 90 92 94 96 98 00 02 04 06 08 10
Coefficient ρ 95% Confidence Level
Note: This figure depicts the one-year rolling return correlations of the S&P GSCI excess return index with
the MSCI Emerging Markets Index in Panel A and with the U.S. Dollar Index in Panel B.
commodities in China and the United States.12 The World Financial Crisis. It is well known
Panels A, B, and C show that futures prices of heat- that prices of financial assets tend to move together
ing oil, copper, and soybeans in China had boom- during financial crises. Could the recent increases
and-bust cycles closely matched by corresponding in commodity return correlations be simple reflec-
cycles in the United States. These closely matched tions of the recent financial crisis? The crisis erupted
price dynamics are consistent with the rapidly in full only after the failure of Lehman Brothers, in
growing imports of these commodities by China in September 2008. The timing of the crisis did not
recent years (Commodity Research Bureau 2009). coincide with the increases in commodity return
More interestingly, Panels D, E, and F show that the correlations, which started in 2004—long before
prices of wheat, corn, and cotton in China did not September 2008. Thus, the financial crisis cannot
display any pronounced cycle around 2008, in sharp fully explain the increases in commodity return
contrast to the boom-and-bust cycles experienced correlations. In our regression analysis (discussed
by the prices of these commodities in the United later in the article), we treated separately the pre-
States. Because China is not a major importer or crisis period before September 2008 to isolate the
exporter of wheat, corn, and cotton (Commodity crisis effect.
Research Bureau 2009), this contrast is perhaps not
so surprising.13 Nevertheless, it raises doubt that Inflation. Inflation is a common factor that
commodity demands from China are the driver of drives prices of various commodities. Could the
all commodity prices in the United States. recent rise in commodity return correlations be
We also compared the average commodity driven by an increasingly important effect of infla-
return correlations in China and the United States tion on commodity prices?
in a sample of eight commodities with futures Figure 6 depicts the annualized monthly core
contracts simultaneously traded in China and the inflation rate of the U.S. Consumer Price Index (the
United States.14 Interestingly, the average com- percentage change of the CPI excluding food and
modity return correlation in China did not experi- energy prices) and the one-year rolling volatility of
ence the same dramatic increase as in the United the monthly CPI core inflation rate. We used the CPI
States after 2004. This contrast again refutes com- core inflation rate to avoid the contamination of the
modity demands from China as the driver of the inflation measure by commodity prices. This infla-
large increases in commodity price comovements tion rate hovered near 10% throughout the 1970s,
in the United States. when the economy was hit by persistent oil supply
62 www.cfapubs.org ©2012 CFA InstituteIndex Investment and the Financialization of Commodities
Figure 5. Normalized Commodity Prices in China and the United States
A. Heating Oil B. Copper
Price Price
300 400
250
300
200
200
150
100
100
50 0
05 06 07 08 09 10 11 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11
C. Soybeans D. Wheat
Price Price
350 500
300 400
250
300
200
200
150
100 100
50 0
99 00 01 02 03 04 05 06 07 08 09 10 11 99 00 01 02 03 04 05 06 07 08 09 10 11
E. Corn F. Cotton
Price Price
400 400
300 300
200 200
100 100
0 0
05 06 07 08 09 10 11 05 06 07 08 09 10 11
U.S. Price Chinese Price
Notes: This figure depicts the front-month futures prices of six commodities—heating oil, copper, soybeans,
wheat, corn, and cotton—in China and the United States. The prices in China are settled in renminbi. Each
price series is normalized to 100 in its own currency at the beginning of its sample period.
shocks and stagflation. The inflation rate remained over the past decade. Interestingly, the commod-
high—around 5%—during the 1980s. Inflation was ity return correlations depicted in Figure 2 show
eventually tamed in the 1990s and remained low, time trends opposite to those of the inflation rate
at 2–3%, throughout the late 1990s and the past and inflation volatility over the past decade. Thus,
decade. The volatility of the inflation rate has a pat- it is unlikely that the recent increases in commodity
tern similar to that of the inflation rate’s monthly return correlations were driven by inflation.
measure. It was often above 5% in the 1970s and
early 1980s and remained above 3% from the early Adoption of Biofuel. Another recent devel-
1980s to the early 1990s. After the mid-1990s, infla- opment in commodity markets is the wide adop-
tion volatility gradually declined, reaching about tion of biofuel. To reduce the reliance on oil as the
1% in the early 2000s, and it remained at that level main source of energy, many countries, including
November/December 2012 www.cfapubs.org 63Financial Analysts Journal
Figure 6. Inflation and Inflation Volatility, January 1970–July 2011
Inflation Inflation Volatility
0.2 0.10
Inflation Volatility
0.1 0.05
Inflation
0 0
70 75 80 85 90 95 00 05 10
Note: This figure depicts the annualized monthly CPI core inflation rate (excluding food and
energy prices) and the one-year rolling volatility of the monthly CPI core inflation rate.
the United States, have adopted new energy Average Return Correlations of Indexed and
policies to promote the use of biofuel. In the Off-Index Commodities. We first plotted the aver-
United States, the Energy Policy Act of 2005 man- age return correlations among indexed and off-
dated that 7.5 billion gallons of ethanol be used index commodities going back to the 1970s. We
by 2012; the Energy Independence and Security constructed an average return correlation for all
Act of 2007 increased the mandate to 36 billion commodities with futures contracts traded at a
by 2022. The combination of ethanol subsidies given time. Because commodities in the same
and high oil prices led to a rapid growth of the sector tend to have greater return correlations
ethanol industry, which now consumes about with each other than with commodities in other
one-third of U.S. corn production. The rise of sectors, we had to avoid the potential bias caused
the ethanol industry might have caused prices of by changes in commodity distribution across sec-
corn and such close substitutes as soybeans and tors. We dealt with this issue by using the follow-
wheat to comove with oil prices. Because corn is ing method: For each sector, we constructed an
a major source of livestock feed, this effect may index that tracks the equal-weighted return of all
also have influenced prices of livestock commodi- available commodities. Then, we computed the
ties. However, the growth of ethanol production return correlations between these indices for all
can explain neither the synchronized price booms sector pairs and took the equal-weighted aver-
of commodities unrelated to food, such as cot- age. To highlight the difference between com-
ton and coffee, nor the greater increase in return modities in and off the two popular commod-
correlations among indexed commodities than ity indices, we constructed two separate return
among off-index commodities.15
indices for indexed and off-index commodities
in each sector and calculated the average correla-
Regression Analysis tions of indexed and off-index commodities. We
We used regression analysis to examine our main called a commodity “indexed” if it was in either
hypothesis—Hypothesis I—by controlling for the the S&P GSCI or the DJ-UBSCI and “off-index”
other effects discussed previously. otherwise.
64 www.cfapubs.org ©2012 CFA InstituteIndex Investment and the Financialization of Commodities
Figure 7 depicts the average one-year rolling cor- and off-index commodities despite the high correla-
relations of indexed and off-index commodities over tion levels in the 1970s. This contrast between the
1973–2011. The figure illustrates several interesting high return correlations of the 1970s and those of
points. The average correlation among indexed com- the past decade indicates that they were driven by
modities stayed at a stable level, below 0.1, through- different mechanisms. We focused our analysis on
out the 1990s and early 2000s and was indistinguish- understanding the latter period.
able from that among off-index commodities. In Price Comovements of Non-Energy Commod-
2009, the mild increase in average correlation among ities with Oil. In formally testing Hypothesis I by
off-index commodities, to 0.2, was in sharp contrast using regression analysis, we pooled daily returns
to that among indexed commodities, which climbed of first-month futures contracts of all non-energy
to an unprecedented level of more than 0.5. This commodities from 2 January 1998 to 15 July 2011.
difference in the increase in correlations between We chose this sample period so that there would be
indexed and off-index commodities is consistent six years before 1 January 2004 and roughly seven
with the effect of index investment discussed previ- years afterward. As we discussed previously, there
ously. Although the correlations among both indexed is not much difference between the return correla-
and off-index commodities have dropped since 2010, tions of indexed and off-index commodities in the
a substantial difference nevertheless exists between earlier period. Extending the sample period further
indexed and off-index commodities. back does not affect our result.
Figure 7 also shows that the average correlations We normalized the daily return of commodity i
of indexed and off-index commodities were as high by its average return and return volatility:
as 0.3 in the 1970s. As we discuss later in the arti-
cle, these high correlations coincided with the wild Ri ,t − mean ( Ri )
inflation and inflation volatility during that period. Rin,t = .
std ( Ri )
The average correlations gradually declined, to
below 0.1 in the late 1980s, as inflation and inflation We specified the following panel regression of
volatility were eventually tamed. Interestingly, there the normalized commodity return ( Rin,t ) on the
n
were no pronounced differences between indexed normalized oil return ( Roil ,t ) and a set of control
Figure 7. Average Correlations of Indexed and Off-Index
Commodities, 1973–2011
Correlation
0.6
0.5
0.4
0.3
0.2
0.1
0
–0.1
75 80 85 90 95 00 05 10
Average Correlation of Indexed Commodities
Average Correlation of Off-Index Commodities
Notes: This figure depicts the average return correlations of commodities in the S&P GSCI
and DJ-UBSCI and commodities off these indices. We separated the samples of indexed
and off-index commodities. In each sample, we constructed an equal-weighted return
index for each commodity sector. A commodity is not included in the index until its aver-
age daily futures trading volume in a given calendar year is larger than $20 million. Then,
for both indexed and off-index commodities, we computed the equal-weighted averages of
the one-year rolling return correlations of all sector pairs.
November/December 2012 www.cfapubs.org 65Financial Analysts Journal
variables comprising the normalized returns of the Motivated by the gradual increase in return cor-
n
MSCI Emerging Markets Index ( REM ,t ), the S&P relations of commodities with each other and with
n
500 ( RSP,t ), the J.P. Morgan U.S. Aggregate Bond other variables that are highlighted in Figures 2, 4,
n n
Index ( RJPM ,t ), and the U.S. Dollar Index ( RUSD ,t ): and 7, we specified a linear trend after 2004 in the
regression coefficient of each independent vari-
⎡β0i + β1 ( t − 2004 ) I t ≥2004 ⎤ n
Rin,t = α + ⎢ ⎥ Roil ,t able. Specifically, we decomposed each regression
⎢⎣ +β2 ( t − 2004 ) I t ≥2004 I index ⎥⎦ coefficient into three components. Figure 8 pro-
⎡ κ 0i + κ1 ( t − 2004 ) I t ≥2004 ⎤ n vides a graphical account of this decomposition.
+⎢ ⎥ REM ,t For example, in the coefficient of oil return, the first
⎢⎣ + κ 2 ( t − 2004 ) I t ≥2004 I index ⎥⎦ component, β0i, measures the baseline coefficient
⎡ γ 0i + γ1 ( t − 2004 ) I t ≥2004 ⎤ n (specific to the individual commodity i) before 2004;
+⎢ ⎥ RSP,t (2) the second component, β1(t – 2004)Itt2004, captures
⎢⎣ + γ 2 ( t − 2004 ) I t ≥2004 I index ⎥⎦ a common trend in the coefficient after 2004, with
⎡ θ0i + θ1 ( t − 2004 ) I t ≥2004 ⎤ n β1 as the slope of the trend; and the third compo-
+⎢ ⎥ RJPM ,t nent, β2(t – 2004)Itt2004Iindex, measures the additional
⎢⎣ + θ2 ( t − 2004 ) I t ≥2004 I index ⎥⎦ trend after 2004, with β2 as the slope of the trend if
⎡ η0i + η1 ( t − 2004 ) I t ≥2004 ⎤ n the commodity is in either the S&P GSCI or the DJ-
+⎢ ⎥ RUSD t + UBSCI. The last component captures the difference
⎢⎣ + η2 ( t − 2004 ) I t ≥2004 I index ⎥⎦ in the post-2004 changes between the return correla-
Iindex is an indicator function with a value of 1 if tions of indexed and off-index commodities with oil.
the commodity is in either the S&P GSCI or the DJ- This specification allowed us to conveniently test the
UBSCI and zero otherwise. We included returns changes in the return correlations of individual com-
of the MSCI Emerging Markets Index and the U.S. modities with the right-hand variables after 2004,
Dollar Index to control for the effect of commodity even though these trends stabilized after 2009.16
demands from emerging economies. Because the Our key hypothesis was that β2 is significantly
dollar return should also pick up the effects of inter- positive, which implies that the increasing presence
national index investors, this control might be exces- of index investors has led to a greater increase in
sive. We also included returns of the S&P 500 and the the return correlations (with oil) of indexed com-
J.P. Morgan U.S. Aggregate Bond Index to control for modities than in the return correlations of off-index
the effects of the macroeconomic fundamentals. commodities. We also decomposed the regression
Figure 8. The Difference-in-Difference Regression
Specification
Coefficient Specification
If Indexed:
Slope = β1 + β2
If Off-Index:
β0i
Slope = β1
2004
Notes: This figure illustrates the difference-in-difference regression
specification for a coefficient of any independent variable in Equa-
tion 2. For example, the coefficient of oil return is
β0i + β1 ( t − 2004 ) It ≥ 2004 + β2 ( t − 2004 ) It ≥ 2004 I index .
66 www.cfapubs.org ©2012 CFA InstituteIndex Investment and the Financialization of Commodities
coefficient of each of the control variables in the Panel A of Table 2 also reveals several inter-
same way to control for possible trends driven by esting observations about the return correlations
other economic mechanisms. of non-energy commodities with the control vari-
We analyzed this regression in the full sample ables. First, there is a significant and positive trend
with all non-energy commodities, as well as in sev- in their return correlations with the MSCI Emerg-
eral subsamples that included the soybean complex ing Markets Index after 2004 in both the pre-crisis
(which contains soybeans, soybean meal, and soy- and the full sample periods, as reflected by the
bean oil), the grains sector, the softs sector, the live- positive and significant estimates of coefficient κ1.
stock sector, and the metals sector. We examined This finding is consistent with the increasing return
separately the pre-crisis period, from 2 January correlation between the S&P GSCI and the MSCI
1998 to 31 August 2008, and the full sample period, Emerging Markets Index after 2004. However, the
from 2 January 1998 to 15 July 2011, in order to iso- estimates of κ2—the difference between indexed
late the crisis effect. For each of the periods, we ana- and off-index commodities in the increase of their
lyzed the regression first with only oil return and return correlations with the MSCI Emerging Mar-
then together with the control variables. Table 2 kets Index—are small and insignificant in the pre-
reports the regression results. crisis period and even negative in the full sample
Panel A reports the results from the full sam- period. This lack of difference is consistent with the
ple, with all non-energy commodities. The esti- fact that the effects of commodity demands from
mates of β1 and β2 in both the pre-crisis and the full emerging economies are independent of the com-
sample periods are positive and significant. These modity indices. It also indirectly confirms the dis-
estimates suggest that after 2004, there was a sig- criminating power of our identification strategy
nificant and increasing trend in return correlations based on the difference-in-difference effect.
of non-energy commodities with oil. More impor- Furthermore, the estimates of coefficient η1
tantly, this increasing trend is significantly stronger are negative, with a significant t-statistic in the full
for indexed commodities than for off-index com- sample period and an insignificant one in the pre-
modities. This pattern is robust to including the crisis period. These estimates suggest a negative
control variables in the regressions and thus sup- trend in the return correlations of non-energy com-
ports Hypothesis I—that is, the increasing presence modities with the U.S. dollar after 2004, which is
of index investors led prices of indexed commodi- consistent with the decreasing trend in the return
ties to comove more with oil. Furthermore, this correlation between the S&P GSCI and the U.S.
effect was present even before the disruptions of Dollar Index. More interestingly, the estimates of
the financial crisis in September 2008. coefficient η2 are also negative, with a significant
In the pre-crisis period with the control vari- t-statistic in the pre-crisis period and an insignifi-
ables, the estimates of β1 and β2 are 0.04 and 0.02, cant one in the full sample period. These estimates
respectively. The former value suggests that the indicate that the decreasing trend is stronger for
return correlation between an off-index non-energy indexed commodities than for off-index commodi-
commodity and oil increased by 0.04 each year. At ties. This difference-in-difference result suggests
that rate, the correlation had a cumulative increase that the decreasing trend in return correlations of
of 0.28 over 2004–2011. The return correlation non-energy commodities with the U.S. dollar was
between an indexed non-energy commodity and not driven entirely by commodity demands from
oil had an extra increase of 0.02 each year. Thus, emerging economies and was at least partly related
its cumulative increase over 2004–2011 was 0.42, to trading by international index investors in com-
which is substantial in economic terms. modity markets.
Panel B of Table 2 also reports the estimates of As mentioned earlier, commodities in the S&P
β1 and β2 in each subsample of non-energy com- GSCI and DJ-UBSCI are selected on the basis of their
modities after including the control variables. The world production and trading liquidity in futures
estimates are consistently positive and significant markets. Hence, the higher liquidity of indexed
across the subsamples except in the livestock sec- commodities works against our hypothesis because
tor, in which the estimate of β1 is zero and the esti- prices of more liquid commodities are less likely to
mate of β2 is positive (but significant only in the full be affected by the trading of index investors. Liquid-
sample period). Taken together, the increased price ity might be a concern for off-index commodities
comovements of indexed non-energy commodities because it can cause price fluctuations of off-index
and oil were not driven by a few commodities con- commodities to lag behind oil. To account for this
centrated in one sector; rather, our result regarding concern, we also introduced two lags of the oil
the increased price comovements is robust across return in the regression analysis. These unreported
various subsamples of commodities.17 results show that the difference-in-difference effect
November/December 2012 www.cfapubs.org 67Financial Analysts Journal
Table 2. Regressions of Daily Futures Returns of Non-Energy Commodities
Pre-Crisis Period Full Sample Period
(2 January 1998–31 August 2008) (2 January 1998–15 July 2011)
Estimate t-Stat. Estimate t-Stat. Estimate t-Stat. Estimate t-Stat.
A. Full sample, with all non-energy commodities
Trend with oil after 2004
β1 0.6 10.48 0.04 8.21 0.05 18.27 0.03 10.04
β2 0.2 3.55 0.02 2.61 0.02 5.26 0.02 4.91
Trend with MSCI Emerging Markets Index after 2004
κ1 0.02 4.41 0.02 6.32
κ2 0.00 –0.39 –0.01 –2.69
Trend with S&P 500 after 2004
γ1 –0.01 –1.09 –0.01 –2.08
γ2 0.00 0.27 0.00 0.80
Trend with J.P. Morgan U.S. Aggregate Bond Index after 2004
θ1 –0.01 –1.48 0.00 –0.93
θ2 –0.01 –0.91 0.00 0.13
Trend with U.S. Dollar Index after 2004
η1 –0.01 –1.10 –0.01 –2.75
η2 –0.02 –2.47 0.00 –0.23
R2 2.13% 4.64% 5.57% 8.98%
B. Estimates of β1 and β2 in various commodity sectors
Soybean complex
β1 0.07 4.29 0.04 4.48
β2 0.06 2.90 0.04 3.57
Grains sector
β1 0.06 6.84 0.04 8.97
β2 0.04 3.21 0.02 4.00
Softs sector
β1 0.02 2.07 0.01 2.42
β2 0.02 1.56 0.02 2.71
Livestock sector
β1 0.00 0.26 0.00 –0.34
β2 0.01 0.35 0.03 3.31
Metals sector
β1 0.05 5.42 0.04 6.15
β2 0.03 2.08 0.02 2.30
Notes: This table reports results from the regression specified in Equation 2. We adjusted the t-statistics for heteroscedasticity
and serial correlation by 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 omitted the estimates for α, β0i, κ0i, γ0i, θ0i, and η0i in Panel A. Panel B
separately reports the estimates of the main variables of interest, β1 and β2, in various subsamples of commodities.
68 www.cfapubs.org ©2012 CFA InstituteIndex Investment and the Financialization of Commodities
between the return correlations of indexed and off- estimated from one-year rolling windows. The S&P
index commodities with oil is robust to the illiquid- GSCI non-energy excess return index tracks price
ity concern.18 fluctuations of S&P GSCI commodities in the four
non-energy sectors. Figure 9 shows that the price
Spot Returns. Thus far, our analysis has
of oil is always volatile. During most of the 1990s
focused on returns of rolling front-month futures
and the past decade, its volatility was at least twice
contracts of various commodities. Does the
as high as the volatility of the S&P 500. In 2008, oil
difference-in-difference effect also hold for spot
return volatility shot up from around 30% to 60%,
returns? We analyzed the spot return correlations
a level that caused great public concern. However,
of non-energy commodities with oil. Owing to
oil return volatility had also reached this level
the lack of centralized spot markets for commodi-
before—in the early 1990s, during the Gulf War.
ties, spot prices are often not readily available. We
More importantly, although the return volatility
acquired spot prices for a set of commodities from
of non-energy commodities was stable—around
Pinnacle Data Corp. The set includes oil and 16
10%—throughout the 1990s and early 2000s, it
non-energy commodities (8 short of the non-energy
started to rise after 2004 and peaked at an unprec-
commodities with futures listed in Table 1). These
edented 27% in 2008, concurrent with the hikes in
non-energy commodities include corn, soybeans,
volatility of oil and the S&P 500.
wheat, Kansas wheat, soybean oil, Minneapolis
Various factors may have contributed to the
wheat, and oats from the grains sector; cotton and
large volatility increase in oil and non-energy
sugar from the softs sector; live cattle and lean hogs
commodities. First, the world economic recession
from the livestock sector; and gold, silver, copper,
that accompanied the recent financial crisis made
platinum, and palladium from the metals sector.
macroeconomic fundamentals more uncertain
We pooled their daily spot returns and
and thus commodity demands and prices more
regressed them on the spot return of oil and the set
volatile. Second, the financial crisis, which initially
of control variables based on the regression speci-
disrupted the markets for mortgage-backed securi-
fied in Equation 2. The estimates of coefficients β1
ties, eroded the balance sheets of many financial
and β2 are reported in Table 3. The estimate of β1
institutions and eventually reduced the risk appe-
is positive and significant in the pre-crisis period
tite of financial investors (including index inves-
but is insignificant in the full sample period. More
tors) for seemingly unrelated assets in their port-
interestingly, the estimate of β2 is positive and sig-
folios, including commodities (see, e.g., Kyle and
nificant in both the pre-crisis period and the full
Xiong 2001).
sample period, confirming the same difference-
Figure 10 depicts the one-year rolling corre-
in-difference effect in spot returns as in returns of
lation between the S&P GSCI and the S&P 500.
rolling front-month futures contracts. This result
It illustrates a widely noted correlation increase:
implies that the price effect generated by the grow-
Although this correlation stayed in a band
ing commodity index investment in recent years is
between –0.2 and 0.1 for several years before 2008,
also present in spot prices of commodities.
it quickly climbed from 0 to around 0.6 during
the crisis and remained high even after the crisis
Volatility Spillover abated, in early 2009. This largely increased cor-
The trading of index investors can act as a chan- relation is consistent with both increased macro-
nel to spill volatility from outside financial markets economic uncertainty and the potential spillover
on and across commodity markets. We examined of index investors.
this spillover effect. Figure 9 depicts the annual- To identify the spillover effect of index inves-
ized daily return volatility of oil, the S&P GSCI tors, we analyzed the difference in return volatil-
non-energy excess return index, and the S&P 500 ity between indexed and off-index non-energy
Table 3. Regression Analysis of Spot Returns
Pre-Crisis Period Full Sample Period
(2 January 1998–31 August 2008) (2 January 1998–15 July 2011)
Estimate t-Stat. Estimate t-Stat.
β1 0.03 2.83 0.00 0.26
β2 0.03 2.96 0.03 4.37
Notes: This table reports the regression results of daily spot returns and only the coefficients related to the
trends with oil after 2004. We adjusted the t-statistics for heteroscedasticity and serial correlation by using
the Newey–West method with five lags.
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