commodities as asset class
TRANSCRIPT
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For Institutional Investo
Use Only. Not for Publi
Distribution.
Vanguard research March 201
Investment case
or commodities?
Myths and reality
Author
Geetesh Bhardwaj, Ph
Executive summary. Commodities are one o the least understood
asset classes. Some investors wonder how owning a chunk o steel
or a bushel o corn could provide them with any real return, particularly
in times o deation. Still others contend that the high historical returns
or commodities are primarily the result o two commodity bubbles, and
that, excluding those abnormal periods, commodities have had only poor
results. Then, too, some see commodities as highly volatile and as too
risky or most investors.
This paper describes the undamental properties o commodities to
help institutional investors evaluate the case or investing in them.
One challenge in understanding commodities as an asset class is theabsence o a long data series on the past perormance o commodity
utures.1 To address this problem, Vanguard has recently constructed
an equally weighted, well-diversifed commodities return series, using
1 For example, historical data on the well-diversified DJ (Dow Jones) -UBS Commodity Index (hencefort h, DJ-UBSCI) are available
only since 1991 (also, since the index was launched in 1998, it has a significant backfill).
Connect with Vanguard > www.vanguard.com
> global.vanguard.com (non-U.S. investors)
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2 Our commodities return series represents the broad commodity market; given equal weights, no one single commodity or sector can drive the results.
3 We believe that one needs a long historical data record to understand the fundamental properties of anyasset class.
2
long historical commodity utures data beginning in 1959.2 Our aim here is to
describe the basic properties o commodities, and not to provide an investable
alternative to existing commodity indexes.3 But, as with historical analysis o
any asset class (e.g., stocks, bonds, or short-term debt), it is difcult to know
how the existence o a large and institutionally dominated market would haveaected past returns; or similar reasons, investors need to be mindul that past
returns do not guarantee uture perormance.
Can commodities rightfully be considered an
asset class? Can a commodities investor expect
to earn any real return, or were the two historical
bubbles in commodities their only periods ofoutperformance? Are commodities too volatile
for most investors to touch?
This paper addresses these and other questions
as it seeks to separate myth rom reality in helping
investors evaluate the case or commodity investing.
For instance, as we describe, an investor actually
gains exposure to commodities as an asset class
by investing in commodity futures, and not by
investing in physical commodities. The undamental
economic reason long-only investors in commodity
utures have historically been expected to earn a
risk premium is that long investors have provided
price insurance to producers o commodities, who
hedge price risk by taking the short side o positions
in the utures market. Moreover, commodity utures
have, in act, experienced long periods o signiicant
returns or investors, as well as sequences o booms
and busts similar to equities; there is thus little merit
in the argument that relatively high long-term averagereturns o commodity utures are solely a result o a
ew brie abnormal periods o high returns.
First, we introduce the concept o commodity
utures and explore the theoretical oundations o
why a long-only investor may expect to earn a return
or taking such a position. We then construct a
commodities return series or a long-only investment
in commodities utures, including detailed analysis o
the data. Next, we compare the results o investing
in commodity utures versus physical commodities,
as well as the historical perormance o commodity
uture returns versus equity returns. Finally, we look
at the diversiication beneits o commodity utures
rom a portolio perspective.
Notes on risk: All investments are subject to risk. Investments in bonds are subject to interest rate, credit,
and inflation risk. Foreign investing involves additional risks, including currency fluctuations and political
uncertainty. Diversification does not ensure a profit or protect against a loss in a declining market. Past
performance is no guarantee of future returns. The performance of an index is not an exact representationof any particular investment, as you cannot invest directly in an index.
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Basics of commodity investment
A commodity utures contract is a standardized
agreement to buy (or sell) a prespeciied amount
o a commodity at a uture date, called the maturity
date. The price o the contract (the utures price) is
eectively the price that the seller o the commodity
will receive at the maturity date, and is determined
on a utures exchange by the orces o demand
and supply.4
As stated early in this paper, its important
to appreciate that commodity utures do not
necessarily represent direct exposures to actual
commodities. A long investor who agrees to buy
a physical commodity at a uture date may not
have the commodity actually delivered to him or
her, because the investor has the option to sellthe utures contract beore the actual date o
physical delivery.
Determining the futures price
To understand how commodity utures returns are
derived, one must irst understand how a utures
price is determined. The most important element
making up the utures price is the spot price, that
is, the price that market participants expect to
prevail when a utures contract matures. I market
participants expect the uture spot price to be much
higher than the current spot price (due to utureexpected demand and supply or the commodity),
then the utures price will be higher than the current
spot price; otherwise, the utures price willbe lower
than the current spot price (all else being equal). It
is important to emphasize here that the utures price
is set in relation to the spot price that is expected
to prevail at the time o the maturity o the utures
contract, and not the current spot price.
A spot prices deviation at maturity rom what was
expected when the utures contract was issued is
by deinition subject to risk, and this is the risk that
all utures investors ace. I the realized spot price at
maturity ends up higher than the utures price, then
the long investor will make a proit; otherwise, he or
she aces a loss.
So how can long-only investors consistently earna risk premium in this market? The only way this is
likely to happen is i the utures price, on average,
is set below the expected spot price that obtains
at maturity. We would expect this to occur i there
are sellers o commodity utures in the market that
are willing to systematically accept a lower-than-
expected price or the underlying commodity, in
exchange or the utures buyers assurance o a
certain price at maturity. These sellers are willing
to pay a premium to insure against the price risk.
This premium can be thought o as equaling the
dierence between the utures price at which they
sell in the utures market, and the uture spot price
they would otherwise expect to be paid. Much
academic work has been done on this concept
since the 1930s, when both Keynes (1930) and
Hicks (1939) developed the theory o normal
backwardation, which holds that utures prices
are set below expected uture spot prices.
To understand Keynes and Hickss theory, consider
a producer o corn who wants to insure against the
risk that prices could all at harvest time (such a
participant is called a hedger). The corn producer
can obtain such insurance by selling corn utures
to lock in a predetermined price or his crops.
Participating on the other side o the trade are long
investors (called speculators) who provide the
insurance or the price o corn by buying utures
contracts. These long investors, however, demand
a risk premium or bearing the risk o uture price
luctuations. Thus, the long investors would require
that the utures price be set at least somewhat
below the expected uture spot price.
4 At the end of each trading day, gains and losses during the day are settled by the two parties to the contract via transfers from t heir margin accounts,
through daily mark to market. To understand the ac counting, consider the hypothetical example of a buyer and seller entering into an oil futur es contract.
Suppose the current fut ures price of oil for delivery the subsequent mon th is $70 (U.S. dollars). At the time of deliver y, if the spot price turns out to be
$100 dollars, the seller of the commodity will receive $100 for oil. However the seller, through daily mark to market, would have paid the buyer $ 30, thus
effectively exchanging oil at $70. On the f lip side, if at the time of delivery the spot price is $ 50, the seller of the commodity w ill receive $50 for oil. Further,
the seller, through daily mark to market, would also have received $20 f rom the buyer, again effectively exchanging oil at $70.
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The normal backwardation theory implicitly
assumes that the number o producers requiring
hedging outweighs the number o consumers
requiring similar hedging in the market. For example,
suppose iron ore were produced only by mining
companies and used as an input only by steelmakers.I the hedging demands o both types o irms were
equal, there would be no reason to assume one side
o the utures contracting arrangement (long or short)
would receive a premium or buying or selling an
iron-ore uture. However, i one side o the market
(either the producer or consumer) is more risk-averse
than the other, the more risk-averse side clearly
would be willing to provide an insurance premium
to the other side o the transaction.
Gorton and Rouwenhorst (2006) have also recently
studied this topic, and summarized the undamentals
o commodity utures returns as ollows:
1. The expected return to the long investor in
commodity utures in excess o the risk-ree rate
o return is the risk premium. The return realized
by the long investor is the risk-ree rate, plus the
(insurance) risk premium, plus any unexpected
deviation o the uture spot price rom the
expected uture spot price at the time the long
position was established.
2. A long position in utures is expected to earn a
positive risk premium as long as the utures price
is below the expected spot price at maturity. A
risk premium may exist and be earned regardless
o whether the utures price is higher or lower
than the current spot price or the commodity
in question.
3. Expected trends in spot prices are not a source
o return to an investor in utures, because that
would assume successul market-timing on the
part o the utures investor.
Confusion about terminology.Backwardation and
another term, contango, have oten been used to
characterize the current state o the utures market.
Yet, use o these terms has resulted in some
conusion. Contangocommonly reers to a market
in which utures prices are higherthan the current
spot pricethat is, the term structure o the utures
curve is upward sloping. In backwardation, utures
prices are lowerthan the current spot, and the term
structure o the utures curve is downward sloping.
The deinitions just cited, as well as those used by
the U.S. Commodity Futures Trading Commission,5
reer to the slope o the utures curve at a point in
time, and not to the movement o utures prices over
the lie o a contract. However, as stated earlier, the
theory o normal backwardation contends that
utures prices are set below expected spotprices at
maturity. This theory is about utures prices relative
to expected spot prices. However, the prevailing
terminology describes utures markets by reerring
to utures prices relative to the current spot. The
use o the term backwardation to characterize the
state o the utures market vis--vis the current
spot price has led to a widespread, but alse, belie
that the theory somehow implies that a commodity
utures return premium exists only when markets
are in backwardation. As emphasized earlier in this
discussion, however, this is not the case.
Further, given the preceding deinitions o contango
and backwardation as relative to the current spot
price, the natural state o virtually all (historically,
this has actually occurred close to 70% o the time)
commodity utures markets is reasonably expected
to be in contango. This is implied by the existence o
a cost o carry, according to which those holding
a physical commodity must pay or storage and
other expenses, coupled with a simple arbitrage.
Arbitrage in this case is a inancially equivalent
alternative to buying a commodity uture and selling
it at expiry. In an arbitrage transaction, the investor
buys the underlying physical commodity now in
the spot market, stores it until the utures contract
maturity date, and then sells it in the spot market. Ithe commodity is costly to store (there is a cost o
carry), then investors ability to make this alternative
trade should tend to push utures prices higher than
the current spot price, all else equal, which would
5 The U.S. Commodity Futures Trading Commission (CFTC ) defines these market conditions as: Backwardation: Market situation in which futures prices ar e
progressively lower in the distant delivery mon ths and Contango: Market situation in which prices in succeeding delivery months are pr ogressively higher
than in the nearest delivery month. Source: http://www.cftc.gov/educationcenter/glossary/glossary_b.html.
4
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imply contango. This logic, coupled with a mistaken
belie that commodities-utures risk premiums cannot
exist when markets are in contango, has led some to
conclude that all commodities utures returns come
rom the ew market events when the market is in
backwardation (relative to the current spot price).
This argument ails to recognize that prevailing
terminology compares utures prices with current
spot prices, whereas the theory o normalbackwardation suggests a relationship between the
utures price and the expected spot price at maturity.
To illustrate how a risk premium can potentially be
obtained when the market is in contango, consider
the ollowing hypothetical example (see Figure 1).
Suppose the current spot price is $100 and the
expected spot price is $110. The theory o normal
backwardation suggests that the utures price
should be less than the expected spot price. Thus,
suppose the utures price is $105. This implies that
the expected risk premium is $5 ($110 $105 =$5). Notice, however, that the market is in contango:
that is, the utures price is greater than the current
spot price. This example shows that normal
backwardation can be present while a utures market
is in contango; the long-only investor would expect
to earn a premium o $5, although the realized return
will o course be dierent rom $5 i the spot price at
maturity is dierent rom the expected $110.
Now consider an example in which the utures
markets are in backwardation (see Figure 2).
Suppose the current spot price is $115 and that the
expected spot price is $110. The theory o normal
backwardation suggests that the utures price shouldbe less than the expected spot price. Suppose
the utures price is $105thus, the expected risk
premium would be $5. This example also illustrates
a market condition in which there is delationary
pressure, since the spot price is actually declining
rom $115 to $110; however, the utures investors
are expected to get a positive risk premium as a
result o normal backwardation.
In summary, a long-only utures investor in a
commodity market can expect to earn a positive
risk premium by providing valuable insurance, iproducers or other risk hedgers are willing to pay
or such insurance regardless o whether markets
are in backwardation or contango. The positive risk
premium is the reward or assuming the price risk.
Commodity risk premium in a
contango market
Figure 1.
Note: This hypothetical illustration does not represent the return on
any particular investment.
Source: Vanguard.
Inception (t) Expiration (T)
Expectedspot pricerise, $10
Expected riskpremium, $5
$110: Expected
spot price (ST)
$100: Currentspot price (St)
$105: Futuresprice (Ft)
Commodity risk premium in a
backwardation market
Figure 2.
Note: This hypothetical illustration does not represent the return on
any particular investment.
Source: Vanguard.
Inception (t) Expiration (T)
Expectedspot pricedecline, $5
Expected riskpremium, $5
$110: Expectedspot price (ST)
$115: Currentspot price (St)
$105: Futuresprice (Ft)
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6
Historical data and construction
of commodities return series
Unortunately, a long historical data series on the
perormance o commodity utures as an asset class
is not available. For example, historical data on the
well-diversiied DJ (Dow Jones) -UBS Commodity
Index (henceorth, the DJ-UBSCI) are available only
rom 1991. Yet, we believe that to ully understand
an asset classs undamental properties, longer-term
historical data are necessary. Thereore, to carry
out a robust historical analysis o the behavior o
commodity utures markets, we have constructed a
commodities return series extending back in history
to August 31, 1959.
Figure 3 lists the commodities that make up our
return series and the inception dates o uturescontracts or each. In all, the return series contains
30 commodities, broadly characterized in seven
sectors (energy, precious metals, grains, animal
products, sots, industrial materials, and industrial
metals). Figure 3 also reports the cumulative
annualized excess returns (beyond the 3-month
U.S. Treasury bill return) or each commodity since
the start o its utures contract through April 30,
2009. The last column reports t-statistics denoting
signiicance o the average excess return. Thus, we
tested or the statistical hypothesis that, on average,
the excess returns were positive. We ound thisto be true or only 5 out o the 30 commodities
(at a 95% signiicance levelsee the asterisked
commodities in the igure)a result that supports
the well-documented high volatility o individual
commodity utures returns.
Our commodities return series is an equally
weighted average o these 30 commodities; the
series is well-diversiied and represents the broad
commodity market. The accompanying Appendix
summarizes the steps we used in constructing
the return series and the details o our analysis.Given the return series diversiied nature, no single
commodity or sector can drive the results. To derive
the total returns that would result rom holding a
ully collateralized commodity utures position (we
ruled out use o leveraging), we incorporated the
3-month U.S. Treasury bill return into the price return.
Numerous academic studies have analyzed the
commodity markets using equally weighted returns
o a commodity basket. For example, Bodie and
Rosansky (1980) constructed an equally weightedcommodities return series using quarterly data rom
1950 through 1976. Fama and French (1987) reported
average monthly excess returns or 21 commodities
as well as or an equally weighted portolio. Unless
otherwise noted, rom here on, when reerring to
commodities utures returns, we are reerring to
returns as measured by the perormance o our
newly constructed commodities return series.
Commodity futures versus spot returns:
Case for insurance-risk premium
The previous section included a hypothetical
example o how an investor in commodity utures
could potentially expect to earn a positive return
when commodity prices are alling. To illustrate the
impact o an insurance-risk premium in a delationary
market, consider the crude oil market. Figure 4, on
page 8, compares the cumulative returns o a ully
collateralized investment in crude oil utures with
that o physical crude oil rom April 30, 1983, through
April 30, 2009. Taking a conservative approach, we
ignored the costs o physical storage, insurance,
and shipping, and so on, which would otherwisehave reduced the returns o the physical crude.
Nevertheless, despite a high monthly correlation
between spot and utures returns (0.97), the total
average annual return or a spot investment (2.1%)
was but a raction o the utures average annual
return (11.6%). In act, or a signiicant portion o
this time (April 30, 1983December 31, 1998), spot
oil prices were actually declining. For the ull, nearly
16-year, period, active spot investment produced
an annual total return o 5.7%, while the annual
return o utures investments over the same period
was 7.1%. This example illustrates the case or an
insurance-risk premium or long-only investors in
commodity utures, even during delationary
market conditions.
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Commodity futures and coverage data for our commodities return seriesFigure 3.
Cumulative annualized
Contracts excess returns (inception
Name start date Sector through April 30, 2009) t-Statistic
Aluminum 6/1/1987 Industrial metals 2.7% 0.02
Coal 7/12/2001 Energy 6.1% 0.18
Cocoa 7/1/1959 Sots 0.4% 0.98
Coee 8/16/1972 Sots 0.4% 1.17
Copper 7/1/1959 Industrial metals 7.7% 2.83*
Corn 7/1/1959 Grains 5.4% 0.89
Cotton 7/1/1959 Industrial materials 1.2% 0.47
Crude oil 3/30/1983 Energy 6.4% 1.78
Feeder cattle 11/30/1971 Animal products 1.5% 1.08
Gold 12/31/1974 Precious metals 1.4% 0.15
Heating oil 11/14/1978 Energy 5.5% 1.76
Lean hogs 2/28 /1966 Animal products 2.5% 1.49
Live cattle 11/30/1964 Animal products 4.4% 2.21*
Lumber 10/1/1969 Industrial materials 7.9% 0.81
Natural gas 4/4/1990 Energy 13.5% 0.08
Nickel 4/23/1979 Industrial metals 0.9% 1.04
Oats 7/1/1959 Grains 6.3% 0.58
Orange juice 2/1/1967 Sots 0.4% 0.89
Palladium 1/3/1977 Precious metals 0.3% 1.08
Platinum 3/4/1968 Precious metals 0.5% 1.03
Propane 8/21/1987 Energy 13.5% 2.16*
Rough rice 8/20 /1986 Grains 7.8% 0.89
Silver 6/12/1963 Precious metals 1.7% 0.66
Soybean meal 7/1/1959 Grains 3.8% 1.88
Soybean oil 7/1/1959 Grains 0.9% 1.13
Soybeans 7/1/1959 Grains 4.6% 2.01*
Sugar 1/4/1961 Sots 3.6% 0.83
Unleaded gasoline 12/3/1984 Energy 11.2% 2.33*
Wheat 7/1/1959 Grains 4.4% 0.52
Zinc 1/3/1977 Industrial metals 0.5% 0.54
Notes: The second column provides the date w hen price quotes were first available for various commodities. The fourth column reports cumulative annualized excess
returns (over the 3-month Treasury bill return). T he last column reports t-statistics for testing the statistical significance of the average excess return (the five
commodities found to have significant excess returns are denoted by an asterisk).
Sources: Vanguard calculations, based on Commodity Research Bureau; Datastream, Thomson Reuters.
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8
Cumulative returns of long crude oil futures versus long crude oil (log scale):
April 30, 1983, through April 30, 2009
Figure 4.
Cumulative returns
10
100
1,000
10,000
1983 1985 1987 1989 20091991 1993 1995 1997 1999 2001 2003 2005 2007
Crude oil futures Crude oil spot
Sources: Vanguard calculations, based on Commodity Research Bureau data.
Year
Cumulative real returns of historical commodity futures and equities (inflation-adjusted):
August 31, 1959, through April 30, 2009
Figure 5.
Cumulative real returns
0
500
1,000
1,500
2,000
2,500
3,000
1959 1964 1969 1979 1984 1989 1994 1999 20041974
Year
Commodities Equities
Note: Equity returns for this and subsequent figures in this paper are based on the following equity series: pre-1971: Standard & Poors 500
Index; 1971 through April 22, 2005, Dow Jones Wilshire 5000 Index; April 23, 2005, through April 30, 2009, MSCI US Broad Market Index.
Sources: Vanguard calculations, based on Commodity Research Bureau; Datastream, Thomson Reuters.
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Bursting the commodities bubbles?
As noted early in this paper, some investors hold
that commodities high historical returns can be
attributed primarily to two commodity bubbles,
and that, outside o those periods, returns are
unattractive. This sections discussion reveals that
this argument is alse, based on an analysis o the
historical record.
For the period August 31, 1959, through April 30,
2009, commodity utures (i.e., our commodities
return series) have produced an average annual
return o 9.8%, which is comparable to the 9.0%
average annual return or U.S. equities or the same
period. Figure 5 plots the cumulative real returns
(net o inlation) o commodities and equities or
August 31, 1959, through April 30, 2009.
Identifying historical subperiods
for the commodities return series
To isolate historical episodes or commodity
returns, we divided the 51-year period covered by
our commodities return series into ive subperiods,
to capture the dierent cycles experienced by the
commodity utures markets. Figure 6 plots these
subperiods and the corresponding annual returns
or investors. Over the irst subperiodAugust 31,
1959, through December 31, 1971the average
annual return or commodity utures was 7.7%,
similar to the 8.0% average annual return o U.S.equities or the same period. The next subperiod
January 31, 1972, through December 31, 1973
saw commodities utures return 58.5%. Clearly,
this was a time o abnormally high returns or
commodities, particularly compared with the 2.0%
return o U.S. equities or the same period. Over
the third subperiodJanuary 31, 1974, through
December 31, 2003commodity utures returned
9.1%, a ew points below the corresponding equities
result o 12.3%. For the next subperiodJanuary 31,
2004, through June 30, 2008commodity utures
advanced 19.5%, a high result compared with the
corresponding equities return o 6.0%. Over the inal
subperiod analyzedJuly 31, 2008, through April
30, 2009commodity utures returned 51.5%, a
low result compared with the corresponding equities
return o 35.1%. (Note: All returns in this paragraph
are average annual returns or the periods stated.)
High historical real returns for commodities: A result of two commodity bubbles?
(August 31, 1959, through April 30, 2009)
Figure 6.
Cumulative real returns
0
500
1,000
1,500
2,000
2,500
3,000
1959 1964 1969 1979 1984 1989 1994 1999 20041974
Year
Note: Return figures are average annual nominal returns for the period.
Sources: Vanguard calculations, based on Commodity Research Bureau; Datastream, Thomson Reuters.
August 31, 19591971: 7.7%
January 31, 19721973: 58.5%
January 31, 19742003: 9.1%
January 31, 2004June 30, 2008: 19.5% July 31, 2008April 30, 2009:51.5%
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The bubble of the early 1970s
As stated, it has been argued that two o the
ive subperiods19721973 and 20042008
are commodity bubbles. To analyze this historical
phenomenon urther, we examined these two
periods more closely.
The irst period, January 31, 1972, through
December 31, 1973, can be associated with two
major events in the history o inance. The irst
event was the all o the Bretton Woods monetary
management system. It should be noted, however,
that the high returns o commodities or this period
cannot be attributed directly to luctuations in the
price o gold, as gold utures contracts were not
introduced until 1975 and thus were not part o
the equally weighted commodities return seriesin 19721973.6
The other major historical event o 19721973 was
the irst oil shock: In October 1973, members o the
Organization o Arab Petroleum Exporting Countries
(OAPEC, consisting o the Arab members o OPEC
plus Egypt and Syria) proclaimed an oil embargo.
Again, as in the case o gold, no energy utures were
traded in the 1970s; as indicated in Figure 3, crude
oil contracts were not available until 1983. Thus, to
claim that the high prices o gold and energy were
responsible or commodity utures returns in the
period is incorrect.
To better understand the period, we looked at the
average annual returns o individual commodities or
the two years (see Figure 7). As the igure shows,
no single commodity caused the high returns. Wheat
garnered the best return, but other grains also
experienced unusually high results. Gorton, Hayashi,
and Rouwenhorst (2008) postulated that these
high returns were generated by broad inventory
shortages in a number o commodities, which led
to higher uncertainty in the market, greater risk or
long investors to insure, and temporarily higher risk
premiums or long investors. Thus, according to this
theory, these exceptional returns were the result o
undamental actors, and were not speculative in
nature. Supporting the view that this isolated period
o rocketing returns was not a bubble is that there
was no corresponding correction, no subsequent
crash in returns to support the notion o a bubble
to begin with.
6 Even after adjusting for inflation, the overall return for commodities for the period was 50.1%.
10
Selected individual commodity returns:
19721973
Figure 7.
Average annual returns
(January 31, 1972, through
Commodity December 31, 1973)Silver 52%
Platinum 18%
Live cattle 17%
Lean hogs 46%
Feeder cattle 36%
Corn 51%
Soybeans 70%
Soybean oil 82%
Wheat 113%
Soybean meal 81%
Oats 29%Cocoa 81%
Coee 4%
Sugar 38%
Orange juice 16%
Cotton 99%
Lumber 56%
Copper 59%
Sources: Vanguard calculations, based on Commodity Research Bureau;
Datastream, Thomson Reuters.
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The bubble of the early 2000s
Over the second historical period (January 31,
2004, through June 30, 2008 ), commodities
experienced annual returns o 19.5%. Figure 8
reports selected commodity-level average annual
returns or this period. Clearly, the returns weredominated by the energy sector; however, copper,
oats, soybean oil, silver, and platinum also had
impressive returns. In contrast to the 1970s,
commodity utures returns underwent a dramatic
correction during the period July 2008 through
April 2009, which strongly suggests that there was
a signiicant bubble component to the 20042008
returns. Nevertheless, i we ignore the period o
the irst bubble (January 31, 1972December 31,
1973), commodity utures produced a solid average
annual return o 8.7% or the period August 31,
1959December 31, 2003. I we urther ignore both
bubbles rom the sample (while retaining the
recent 20082009 correction), commodity utures
have produced an average annual return o 7.1% or
the period August 31, 1959, through April 30, 2009
(taking out 19721973 and January 31, 2004, through
June 30, 2008). These data support the view that
high historical returns or commodities cannot just be
attributed primarily to two commodity bubbles, and
that, outside o those periods, long-only investors
still have earned signiicant positive returns.
Commodity futures versus equities:
Comparing returns and volatility
As the preceding analysis suggests, there is little
validity to the claim that a ew historical periods have
determined returns or commodities utures. In act,
investors can point to a long period o substantial
returns rom commodities utures. Clearly, the
early 1970s was a unique time or commodities.
Although we have reuted the notion that 1972 and
1973 represented a bubble, one still has to question
whether that kind o return can happen again.
1
Selected individual commodity returns:
20042008
Figure 8.
Average annual returns
(January 31, 2004, through
Commodity June 30, 2008Crude oil 33%
Heating oil 36%
Natural gas 19%
Gasoline 33%
Coal 26%
Propane 38%
Gold 19%
Silver 26%
Platinum 27%
Palladium 19%
Live cattle 11%Lean hogs 2%
Feeder cattle 15%
Corn 8%
Soybeans 17%
Soybean oil 20%
Wheat 6%
Soybean meal 13%
Oats 23%
Rough rice 12%
Cocoa 18%
Coee 10%Sugar 8%
Orange juice 12%
Cotton 9%
Lumber 14%
Copper 49%
Zinc 16%
Nickel 16%
Aluminum 15%
Sources: Vanguard calculations, based on Commodity Research Bureau;
Datastream, Thomson Reuters.
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I we ignore the high returns o the 1970s, the
period January 31, 1980, through April 30, 2009,
provided average annual commodity returns o
6.2%, as opposed to 10.3% or U.S. equities;
however, during this period, our analysis shows that
commodities had 25% lower volatility than
equities (sources: calculations based on CommodityResearch Bureau; Datastream, Thomson Reuters).
This brings us to a more in-depth look at returns
and volatility or commodity utures versus equities.
As reported in the previous section, average annual
historical returns or commodity utures (9.8%) and
U.S. equities (9.0%) are comparable. To compare the
perormance o the two asset classes more closely,
Figure 9 plots their historical 12-month returns rom
May through April (that is, the irst 12-month period
is deined as beginning on May 31, 1960, and the
inal 12-month period ends on April 30, 2009, just
to include the last data point in our sample, which
is April 2009; the results are similar, however, i we
deine the 12 months as the calendar year). This
graph is also important to address the myth that
commodities have returns only once in 20 years,
and then only poor returns or the next 20 years.
At irst glance, its diicult to tell in Figure 9 which
plotted time series is commodities utures and which
one is equities. The giveaway is the outlier in 1972
and 1973, in which commodities had returns o more
than 50% in one year. The igure reveals that both
commodities and equities have had multiple years
o returns in the 20%40% range. These multiyear
runs contradict the view that one has to endure zero
returns or decades beore experiencing any positive
returns in commodities.
12
Twelve-month returns for commodity futures versus equities: May 31, 1960, through April 30, 2009Figure 9.
Returns
April1961
April1965
April1969
April1973
April1977
April1985
April1989
April1993
April1997
April2005
April2009
April2001
April1981
60
40
20
0
20
40
60%
Commodities Equities
Sources: Vanguard calculations, based on Commodity Research Bureau; Datastream, Thomson Reuters.
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Figure 10 plots a histogram o monthly returns or
commodities and equities, and Figure 11 shows
summary statistics o the monthly data. Several
things stand out: First, equities have a slightly longer
negative tail, and commodities have a slightly longer
positive tail. Second, while equities have returns
that are comparable to those o commodities utures,they have had higher volatility. Third, commodities
utures have positive skewness, while equities have
negative skewness.
Potential diversification benefits
of commodity futures
Advocates o commodity utures have argued that
commodities provide diversiication because o their
low correlation with equities and bonds, while critics
point out that the historical diversiication argument
is no longer valid, since the correlations have
increased signiicantly; urther, they claim there are
no diversiication beneits during deep recessions.
1
Histogram of commodity futures and equity monthly returns: August 31, 1959, through April 30, 2009Figure 10.
Returns
< 20% > 20%20%, 15% 15%, 10% 10%, 5% 5%, 0% 5%, 10% 10%, 15% 15%, 20%0%, 5%
0
20
40
60%
Volatility
Commodities: slightly longer positive tailEquities: slightly longer negative tail
Sources: Vanguard calculations, based on Commodity Research Bureau; Datastream, Thomson Reuters.
Comparing commodity futures and
equity monthly returns: Summary statistics
(August 1959 through April 2009)
Figure 11.
Commodity
utures Equities
Monthly average returns 0.85% 0.82%
Standard deviation 3.70% 4.40%
Skewness 0.26 0.66
Sources: Vanguard calculations, based on Commodity Research Bureau;
Datastream, Thomson Reuters.
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During negative shocks for equities
This section irst addresses the potential
diversiication beneits o commodities during
negative shocks or equities, when the diversiication
beneits really matter. Figure 12 reports the average
monthly returns or domestic equities, commodities,
and international equities or months when these
securities experienced extreme negative and/or
positive shocks. The analysis covers two timeperiods: the ull historical sample rom August 31,
1959, through April 30, 2009 (the igure doesnt
include international equity returns or this period),
and the inal decade starting January 31, 1999.
For the longer historical period, while the average
monthly return or the worst 12 domestic-equity
months was 12.6%, commodities utures declined
at a much smaller average monthly rate o 1.1%.
The relative picture is not as clear over the inal
decade: The worst 12-month average monthly
return or domestic equities was 9.6% (international
equities, 9.7%), while or the same 12 months,
commodities lost an average o 2.7% per month.
Figure 12 indicates that even though commodities
experienced modest declines or the worst months
o domestic equities, commodities still provided
some diversiication beneit, since or the same
months international equities declined 9.7% permonth. O course, commodities have not always
perormed well during equity-market downturns;
thus, or investors concerned about worst-case
outcomes, diversiiers such as cash may be more
eective. However, the signiicantly lower overall
returns or holding cash make the reliable protection
it oers more costly over time.
14
Worst 12 equity months Best 12 equity months
Domestic Commodity International Domestic Commodity International
Period equity returns utures returns equity returns equity returns utures returns equity returns
August 31, 1959April 30, 2009 12.6% 1.1% 11.7% 0.7%
January 31, 1999April 30, 2009 9.6% 2.7% 9.7% 7.5% 1.6% 6.6%
Worst 12 commodity utures months Best 12 commodity utures months
Domestic Commodity International Domestic Commodity International
Period equity returns utures returns equity returns equity returns utures returns equity returns
August 31, 1959April 30, 2009 4.0% 9.8% 1.4% 12.2%
January 31, 1999April 30, 2009 4.9% 7.4% 7.1% 0.1% 6.9% 1.5%
Notes: International equities are represented by the MSCI EAFE + EM Index. As this index does not go back to the 1950s, the international equity returns are reported
only for the more recent sample.
Sources: Vanguard calculations, based on Commodity Research Bureau; Datastream, Thomson Reuters.
Average monthly returns for domestic equities, commodity futures, and international equities
during their 12-worst and 12-best months (selected periods)
Figure 12.
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Correlation of U.S. equities
with commodities versus international stocks
What about the correlation o monthly U.S. equity
returns and commodity utures returnsand,
or urther comparison, o domestic equities and
international equities? The results o our analysisin Figure 13show that the historical correlation
o U.S. equity and commodity utures returns has
been very low; or the period August 31, 1959,
through April 30, 2009, the correlation was only 0.13.
However, the correlation has risen steadily over time.
For January 31, 2001, through April 30, 2009, the
correlation was much higher, at 0.37. Many investors
conidently include exposure to international equities
or diversiication purposes; however, the correlation
o international equity returns with U.S. equities
has also increased over time. From January 31,
2001, through April 30, 2009, the correlation o
international equity returns with U.S. equities was
0.90, ar higher than the 0.37 or commodity utures.
The purpose o this analysis is not to suggest that
investors should abandon international stocks as
potential diversiiers in avor o commodities; ater
all, commodities have been experiencing increasing
correlation with equities over time.
Commodity futures can lessen volatility
of all-equity andstock/bond portfolios
Another way to characterize the diversiicationbeneits o commodity utures is to analyze
the impact o including commodities utures on
the historical volatility o a diversiied portolio.
Figure 14, on page 16, reports the average
annualized change in portolio volatility as commodity
utures are added to the asset mix. We considered
two hypothetical base portolios, one all equities and
the other 60% equities/40% bonds (in the second
example, as we added commodities to the portolio,
we assumed the mix o stocks and bonds in the rest
o the portolio was let constant, at 60%/40%).
Figure 14 is based on data rom January 31, 1974,through April 30, 2009 (however, results are similar
or the ull historical period beginning in 1959). For
this exercise, we excluded the market conditions
o the early 1970s to show that, as with returns,
diversiication beneits are not dependent on a
ew historical periods. In our hypothetical example,
adding commodities to the portolio clearly had the
potential to reduce the portolios volatility. Even or
the 60% stocks/40% bonds portolio, which has
much lower volatility than the all-equity portolio,
1
Comparing correlation of domestic equities
with commodities, international equities
(selected periods)
Figure 13.
Correlation
Correlation of U.S. equities with commodities
Correlation of U.S. equities with international equities
Sources: Vanguard calculations, based on Commodity Research
Bureau; Datastream, Thomson Reuters.
0
20
40
60
80
100%
Aug. 1959April 2009
Jan. 1971April 2009
Jan. 1981April 2009
Jan. 1991April 2009
Jan. 2001April 2009
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16
signiicant diversiication gains could have resulted
rom adding commodities. For example, adding
10%20% commodities would potentially have
reduced volatility in the 60%/40% portolio by about
1 percentage point and almost twice that or the
all-equity portolio (see Figure 14).
Figure 15 reports the hypothetical impact o a 20%
exposure to commodities on portolio returns andvolatility. The irst two columns report total returns,
standard deviations, and Sharpe ratios or the all-
equity and 60%/40% equity/bond portolios. The
next two columns report total returns or these
two portolios ater adding a 20% exposure to
commodities. In this hypothetical example, the
eect on perormance o adding commodities tothe portolio was marginal; average returns improved
by roughly 50 basis points. However, the potential
impact on volatility was highly signiicant. Adding
commodities to the all-equity portolio increased the
Sharpe ratio rom 0.29 to 0.35. Another interesting
comparison is 100% equity exposure versus 80%
exposure to the 60% equity/40% bond portolio and
20% commodity exposure. The returns o the two
portolios are comparable (equity portolio returns
are potentially higher by 11 basis points), while the
diversiied portolio potentially has 41% less volatility.
To urther understand the undamental source o
diversiication beneits o commodity utures, we
compared the equity and commodity utures returns
during dierent stages o the business cycle.
Adding commodity futures can benefit
during different stages of business cycle
It is also instructive to look at the relationship o
commodity utures returns and equities over a
typical business cycle. As identiied by the National
Bureau o Economic Research (NBER), the businesscycle can be divided into our stages: late expansion,
early recession, late recession, and early expansion.
Figure 16 illustrates these patterns using the
historical record.
As shown in Figure 16, during late expansion and
beore the onset o recession, the equity market
has tended to experience low returns, which
continue during the early part o the recession.
Further, because equities are a leading indicator
o the business cycle, equity markets tend to
recover beore a recession is over; also, during
the late-recession period, equities have typically
Average annualized change in portfolio
volatility as a result of adding commodities:
January 31, 1974, through April 30, 2009
Figure 14.
Annualized volatility change relative
to no-commodities portfolio
100% equities 60% equities/40% bonds
Percentage of portfolio in commodities
100%90%0% 10% 20% 30% 40% 50% 60% 70% 80%
6
4
2
0
2
4%
Notes: This hypothetical illustration does not represent the return on
any particular investment.
Corporate bond returns for this and subsequent figures in this paper
are based on the following series: before 1968, Standard & Poors
High Grade Corporate Index; 19691972, Citigroup High Grade Index;
and January 1, 1973, through April 30, 2009, Barclays Capital U.S.
Credit Bond Index.
Sources: Vanguard calculations, based on Commodity Research Bureau;
Datastream, Thomson Reuters.
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7 For details, see Gorton et al. (2008).
experienced higher returns. Commodities utures,
however, have behaved very dierently rom equitiesover the course o the business cycle. Commodity
utures returns are plausibly linked to the state o
inventories in the economy,7 and their returns can
thereore be expected to be a lagging indicator
o recession.
Thus, during the late-expansion period (anticipating a
recession), while equity markets tend to experience
relatively poor returns, low inventory levels would
imply that commodity utures are experiencing
higher-than-normal returns. Further, because o
inertia in inventories, it is not until a recession sets
in that commodities experience low returns. As
stated, coming out o a recession, equities have
tended to revive beore the recession ends, while
commodities utures returns have tended to improve
only ater the early expansion period has begun.
To deine earlyand laterecession, we divided
each o the eight recessions rom 1959 through
2009 into two equal halves. For example, or the
2001 recession that lasted rom April to November,
we deined the period o April 2001 through
July 2001 as early recession, and August 2001
through November 2001 as late recession. For the
current recession, we deined the irst 12 months
(January 2008 through December 2008) as earlyrecession, and January 2009 through April 2009
as late recession. For the expansionary period, the
12 months beore a recession were deined as late
expansion, and the 12 months ater a recession as
early expansion.
1
80% (60% equities/
60% equities/ 80% equities/ 40% bonds)/
100% equities 40% bonds 20% commodities 20% commoditiesTotal return 9.05% 8.45% 9.51% 8.94%
Standard deviation 15.34% 10.46% 12.86% 8.98%
Sharpe ratio 0.29 0.31 0.35 0.40
Note: This hypothetical illus tration does not represent the return on any particular investment. The first t wo columns report total returns, standard deviations,
and Sharpe ratios for the all-equity and 60%/40% equity/bond port folio. The next two columns report the same for these two portfolios after adding a 20% exposure to
commodities.
Sources: Vanguard calculations, based on Commodity Research Bureau; Datastream, Thomson Reuters.
Effects on portfolio returns and Sharpe ratios of adding commodities to portfolio:
August 31, 1959, through April 30, 2009
Figure 15.
Potential diversification benefits of
integrating commodities with equities during
different stages of business cycle
Figure 16.
Note: NBER, National Bureau of Economic Research.
Source: Vanguard.
Late expansion(High commodityreturns, lowequity returns)
Early recession
Earlyexpansion
Late recession(Low commodityreturns, high equityreturns)
NBER Peak
NBER Trough
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Figure 17 reports equity and commodity utures
returns during the dierent stages o the business
cycle: We analyzed the eight recessions since 1959,and we also segregated the last three recessions (all
three occurring ater 1990). During the late-recession
period, the average annual return or equities was
32.39%, while commodities declined 1.14%. During
the late-expansion period, although equities had
started their decline and experienced average annual
returns o 1.72%, commodity utures markets were
actually booming, with average annual returns o
22.67%. These results were robust, and the pattern
persisted or the shorter period o three recessions
since 1990, including the current recession. In the
current recession, equities peaked in October 2007,beore the start o the recession, and commodities
peaked in June 2008, six months ater the economy
was in recession. For the period November 2007 to
June 2008, the average monthlyreturn or equities
was 1.98%, while that or commodities was 3.11%.
The results o this section suggest that
commodities can have diversiication beneits
because commodities behave undamentally
dierently than equities at dierent stages o the
business cycle. Note, however, that this analysis can
only become clear in hindsight. Predicting periods o
outperormance or underperormance or any asset
class can be extremely diicult, i not impossible.
Commodity futures returns and inflation
Inlation is a serious concern or investors who care
about the real purchasing power o their returns.Given the relationship between commodity utures
returns and the stages o the business cycle, it
is instructive to explore the relationship between
commodity returns and inlation. Many traditional
asset classes are a poor hedge against inlation
at least over short- and medium-term horizons.
Figure 18, which reports the correlation o inlation
with commodity utures, equities, and bond returns,
suggests that commodity utures might be a slightly
better inlation hedge than stocks or bonds. We
calculated correlations at monthly as well as rolling
quarterly and annual horizons.
18
Eight recessions since 1959 Last three recessions, since 1990
Equities Bonds Commodities Equities Bonds Commodities
Late expansion 1.72% 1.01% 22.67% 5.40% 7.84% 10.74%
Early recession 25.04% 3.03% 4.48% 27.88% 1.57% 18.05%
Late recession 32.39% 20.30% 1.14% 14.62% 9.41% 14.74%
Early expansion 13.68% 8.64% 5.48% 0.09% 9.37% 7.91%
Note: This table reports equity and commodity futures returns during different stages of the business cycle. To define early and late recession, we divided each of
the historical recessions into two equal halves. The 12-month period before a recession was defined as late expansion, and the 12-month period after a recession
was defined as early expansion. All returns are average annualized returns.
Sources: Vanguard calculations, based on Commodity Research Bureau; Datastream, Thomson Reuters.
Business cycle and diversification benefits of commoditiesFigure 17.
Correlation of equities, bonds, and
commodity return series with inflation:
August 31, 1959, through April 30, 2009
Figure 18.
Equities Bonds Commodities
Monthly requency 0.09 0.10 0.08
Quarterly requency 0.05 0.14 0.25
Annual requency 0.10 0.25 0.34
Sources: Vanguard calculations, based on Commodity Research Bureau;
Datastream, Thomson Reuters.
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Although it appears that commodity utures come
out in ront o equities and bonds as an inlation
hedge, the asset class is not a perect hedge or
inlation. To the extent that commodity utures
represent a bet on commodity prices, they are
directly linked to the undamental components oinlation. However, they do not provide a hedge
against certain other components o inlation, such
as increasing health cost. Further, because utures
prices include inormation about oreseeable trends
in commodity prices, commodity utures returns are
likely to rise and all with unexpected components
o inlation. A detailed analysis o the relationship
o commodity utures returns to inlation is beyond
the scope o this paper, and is a subject o uture
research. I the investors primary objective is
to obtain an inlation hedge, then, clearly, TIPS
(Treasury Inlation Protected Securities) would be
a much better option.
Conclusion
This paper has addressed the attractiveness o a
broadly diversiied portolio o commodity utures
as an asset class. It is critical to understand that
an investment in commodity utures does not
represent direct exposure to physical commodities.
Commodities utures prices are set in relation to
expected spot prices at maturity, and not to current
spot prices. A historical analysis o commoditiesutures return patterns suggests there is little
merit in the argument that relatively high long-term
average commodities utures returns are solely a
result o a ew brie abnormal periods o high
returns. Commodities utures have experienced long
periods o signiicant returns or investors, as well as
sequences o booms and busts similar to equities.
Historically, commodity utures returns and equity
returns have had very low correlation, and although
this correlation has risen over time, recent data
suggest the correlation between equities and
commodities utures is lower than that between
equities and other broadly accepted diversiiers,
such as international equity. In addition, while
equities are leading indicators o the business
cycle, commodity utures have tended to be lagging
indicators. There is no reason to expect that this
relationship to the business cycle will change.
This would suggest that uture corrections between
equities and commodity utures could remain
relatively low, oering potential diversiication
beneits to investors who are willing to accept the
unique risks and opportunities o this asset class.
References
Bodie, Zvi, and Victor Rosansky, 1980. Risk and
Return in Commodity Futures. Financial Analysts
Journal(May/June): 2739.
Davis, Joseph H., and Roger Aliaga-Daz, 2009.
The Global Recession and International Investing.
Valley Forge, Pa.: Vanguard Investment Counseling
& Research, The Vanguard Group.
Fama, Eugene F., and Kenneth R. French, 1987.
Commodity Futures Prices: Some Evidence on
Forecast Power, Premiums, and the Theory o
Storage. Journal of Business60: 5573.
Gorton, Gary, and K. Geert Rouwenhorst, 2006.
Facts and Fantasies about Commodity Futures.
Financial Analysts Journal62(2): 4768.
Gorton, Gary, Fumio Hayashi, and K. Geert
Rouwenhorst, 2008. The Fundamentals of
Commodity Futures Returns. Yale ICF WorkingPaper No. 07-08. New Haven, Conn.:
Yale University.
Hicks, John R., 1939. Value and Capital. Oxord:
Oxord University Press.
Keynes, John M., 1930. A Treatise on Money,
vol. 2. London: Macmillan Publishers.
Santos, Joseph, 2008. A History o Futures Trading
in the United States. In EH.Net Encyclopedia of
Economic and Business History, edited by Robert
Whaples, March 16; available at http://eh.net/
encyclopedia/article/Santos.utures.
Working, Holbrook, 1960. Speculation on
Hedging Markets. Food Research Institute
Studies1: 185220.
1
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Appendix. Construction and methodology
of the commodities return series
We ollowed the methodology o Gorton and
Rouwenhorst (2006) in constructing our equally
weighted commodities return series. The steps in
constructing the return series were:
1. For each month, we constructed price returns on
each commodity uture using the nearest contract
not expiring in that month.
2. For a mechanical trading strategy, on the last
business day o the month beore the expiration
date o a utures contract, we rolled the contract
into the next nearest utures contract.
3. Using monthly returns or each commodity utures
contract, we constructed the return series by
adding the monthly returns together or eachmonth and then dividing them by the number o
commodities in the return series or that month.
Thus, we essentially have an equally weighted
indexing approach with monthly rebalancing.
4. A commodity enters the return series on the
last business day o the month ollowing its
introduction date.
5. Finally, to obtain the total returns o holding a
ully collateralized commodity utures position,
we added to the price returns the 3-month U.S.
Treasury bill return.8
Selection of commodities
The list o 30 commodities that were selected or
this study is based on most o the 23 commodities
eligible for inclusion in the DJ-UBS Commodity
Index (DJ-UBSCI). We augmented this list with nine
more commodities: coal, eeder cattle, lumber, oats,
orange juice, palladium, propane, rough rice, and
soybean meal. Also, because o data limitations, we
did not include tin and lead;9 tin and lead are part o
the eligiblecommodities, but are not in the inal list
o 19 commodities currentlyin the DJ-UBSCI.
The reason we included the additional nine
commodities was to broaden our commodities
universe. Some o the commodities, like oats and
soybean meal, have long trading histories going
back to 1959. These additional nine commodities
represent broad sectors o the commodities
universe: energy (coal and propane), grains (oats,
soybean meal, and rough rice), sots (orange juice),
industrial materials (lumber), animal products (eeder
cattle), and precious metals (palladium).
To test the impact o membership o these
30 commodities in our commodities return series,
we constructed three series o returns, using
the ive steps just described. The irst series is
based on the 19 commodities currently in the
DJ-UBSCI. The second series is based on just the
21 commodities (not including tin and lead) eligible
or inclusion in the DJ-UBSCI. The third series
represents our broad-based commodities return
series, which includes all 30 commodities listed
in the text in Figure 3. Figure A-1 cites the total
8 Source: http: //research.stlouisfed.org/fred2 /.
9 These commodities are traded on the London Metal Exchange, and their data are not covered by the Commodity Research Bureau, our primary data source.
20
Commodity futures returns:
Summary statistics (August 31, 1959,
through April 30, 2009)
Figure A-1.
Commodities universe
All 30DJ-UBSCI: DJ-UBSCI: commodities
19 current 21 eligible in our
commodities commodities* return series
Total returns 10.52% 10.23% 9.85%
Volatility 13.84% 13.58% 12.79%
Sharpe ratio 0.40 0.39 0.38
Correlation
with equities 0.11 0.12 0.13
*Does not include tin and lead (for explanation, see Appendix text).
Sources: Vanguard calculations, based on Commodity Research Bureau;
Datastream, Thomson Reuters.
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returns, volatility, Sharpe ratios, and correlation
with equities or the three commodity return
series rom August 31, 1959, through April 30,
2009. The results were very similar or all three
measures. The volatility o our commodities return
series, however, was the lowest, because o theimpact o greater diversiication. Given this papers
objective to study the behavior o commodities as
a broad asset class, we thus avored the equally
weighted return series based on all 30 commodities.
Why equally weighted returns?
Since our commodities return series is an
equally weighted average o individual commodity
returns, it is well-diversiied and represents the
broad commodities market. Given equal weights,
no one single commodity or sector can drive the
results. However, one can still question whether
the results o our analysis are conditional on
the commodities return series. Recall the two
components o our return series construction:
equal weighting and monthly rebalancing (an
outcome o the equally weighted average). Gorton
and Rouwenhorst (2006) have shown that returns
and volatility properties o commodity utures are
robust to annual rebalancing as well. An alternative
to equal weighting would be a weighting scheme
such as that adopted by the DJ-UBSCI, whose
weights are based on the production volume and/or liquidity o individual commodities. Serious data
restrictions apply, however, in constructing such an
index; urther, such backill could arguably amount to
or lead to a data-mining eort; the agnostic, equally
weighted approach has the virtue o simplicity and
transparency. Later in this Appendix we nonetheless
compare our commodities return series with both
the DJ-UBSCI and the S&P-GSCI over the time
period common to all three.
Investability
Could an investor have actually earned the returns
shown by our return series? Like other indexes, the
equally weighted return series results do not relect
any transaction costs, which would apply in the real
world. However, clearly, commodity utures marketshistorically have had the depth that an investable
strategy requires. As documented by Santos (2008)
and numerous others, the commodities markets in
the United States have a long history, with trading
in agricultural commodities voluminous even in the
19th century. Santos reported that between 1884
and 1888, the volume o grain (wheat, corn, oats,
barley, and rye) utures traded in the U.S. market
was eight times the average annual amount o crops
produced. For cotton, by 1879 utures volume had
outnumbered production by a actor o ive. Working
(1960) estimated that or the period 19541958,
the average dollar value o short hedging contracts
or cotton, wheat, soybean, corn, and soybean oil
was close to $500 million. Certainly a relatively
small investor could have bought the contracts
listed and analyzed in our commodities return series
and endeavored to replicate the equal weighted
methodology. But as with historical analysis o
any asset class (e.g., stocks, bonds, or short-term
debt), it is diicult, i not impossible, to know how
the hypothetical development o a large, liquid, and
institutionally dominated market would have aected
past returns.
Comparing commodities return series
with DJ-UBSCI and S&P-GSCI
DJ-UBSCI data are available rom 1991, and the
index itsel was launched in 1998. S&P-GSCI
data are available rom 1970, and the index was
launched in 1992. This section compares the
perormance o these two indexes with our equally
weighted commodities return series on a number
o parameters over the period common to all three
2
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22
measures: January 31, 1991, through April 30,
2009. For this period the DJ-UBSCI and the
commodities return series had a high correlation
o 0.91 at monthly requency; the correlation
with the S&P-GSCI was much lower, at 0.75 (see
Figure A-2). As the igure shows, the commodities
return series had a marginally higher correlation with
domestic equities than did the DJ-UBSCI. In terms
o inlation and bond returns, the DJ-UBSCI was
correlated almost exactly (inlation) or exactly (bond
returns) with the commodities return series.
The DJ-UBSCI is well recognized as a broad-based
commodities index, while the weighting structure
o the S&P-GSCI makes it primarily an energy index.
As o April 30, 2009, the energy subsector weightingin the S&P-GSCI was more then 70%, whereas the
energy sector weighting in the DJ-UBSCI is capped
at 33%. The DJ-UBSCIs greater diversiication
makes it much less volatile than the S&P-GSCI.
However, as shown in Figure A-3, the commodities
return series is clearly the least volatile o the
three measures; this is to be expected, given the
equally weighted nature o the return series and the
embedded diversiication beneits. Figure A-4 plots
the cumulative returns o the three measures.
Composition of commodities return series
The composition o the commodities return series
as constructed here changed signiicantly rom the
1960s to the 1990s as dierent contracts were
added to the return series. For example, or the irstsubperiod identiied in this paper (19591971), there
were no energy utures contracts. It is possible that
the historical return and diversiication properties
o the return series are distorted owing to its
composition.
To address the issue o composition, we considered
three subperiods (see Figure A-5). During the irst
subperiod (1959 through 1971), commodities in the
return series were cocoa, copper, corn, cotton, oats,
soybean meal, soybean oil, soybeans, wheat, sugar,
silver, live cattle, lean hogs, orange juice, platinum,
and lumber. These commodities represented the
ollowing sectors: sots, grains, industrial metals,
industrial materials, animal products, and precious
Commodities
Domestic equities Inlation Bonds return series
Commodities return series 0.28 0.22 0.16
DJ-UBSCI 0.23 0.23 0.16 0.91
S&P-GSCI 0.17 0.28 0.11 0.75
Sources: Vanguard calculations, based on Commodity Research Bureau; Datastream, Thomson Reuters.
Commodities return series, DJ-UBSCI, and S&P-GSCI: Correlation with domestic equities, inflation, and bonds
(January 31, 1991, through April 30, 2009)
Figure A-2.
Commodities return series, DJ-UBSCI,
and S&P-GSCI: Summary statistics
(January 31, 1991, through April 30, 2009)
Figure A-3.
Commodities
return series DJ-UBSCI S&P-GSCI
Average 0.51% 0.44% 0.36%
Variance 0.11% 0.17% 0.37%
Skewness 0.95 0.66 0.46
Kurtosis 5.65 3.16 1.88
Sources: Vanguard calculations, based on Commodity Research Bureau;
Datastream, Thomson Reuters.
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metals. Notable exceptions were gold and energy
products, both o which were introduced in the
second subperiod, 1975 through April 30, 2009;
natural gas was added in 1990, and coal in 2001. The
last subperiod, 1988 through April 30, 2009, included
almost all o the energy contracts as well.
For the irst subperiod, 1959 through 1971, the
commodities return series had no energy or gold
utures contracts; yet, it produced a return o 7.74%
with a volatility o 8.68%; this was nearly equal to
the 8.02% return o equities, which had a volatility
o 12.92%. The higher volatility o the second and
third subperiods has not just been a characteristic
o commodities, however; all the asset classes have
had higher volatility.
2
Commodities return series, DJ-UBSCI, and S&P-GSCI:
Cumulative returns (January 31, 1991, through April 30, 2009)
Figure A-4.
Cumulative returns
Sources: Vanguard calculations, based on Commodity Research Bureau; Datastream, Thomson Reuters.
S&P-GSCICommodities return series DJ-UBSCI
0
100
200
300
400
500
600
Year20081990 1992 1994 1996 1998 2000 2002 2004 2006
Commodities
Equities (%) Inlation (%) Bonds (%) return series (%)
19591971 8.02 (12.92) 2.81 (0.7) 3.52 (5.38) 7.74 (8.68 )
1975April 30, 2009 11.45 (15.84) 4.19 (1.14) 8.66 (7.3) 8.08 (12.45)
1988April 30, 2009 8.57 (15.03) 2.90 (0.93) 7.35 (5.28) 6.86 (11.53)
Sources: Vanguard calculations, based on Commodity Research Bureau; Datastream, Thomson Reuters.
Total returns (volatility) for equities, inflation, bonds, and commodities return series: Subperiod analysisFigure A-5.
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