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Structural Sources of Return and Risk in Commodity Futures Investments
Hilary Till

Principal, Premia Capital Management, LLC

Research Associate with the EDHEC Risk and Asset Management Research Centre

April 2006

Author’s Note: This article originally appeared in the June 2006 edition of Commodities

Now: http://www.commodities-now.com.

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EDHEC pursues an active research policy in the field of finance. Its Risk and Asset Management Research Centre

carries out numerous research programs in the areas of asset allocation and risk management in both the traditional and

alternative investment universes.

Performance and Asset Review

By now it has become well-known that commodities have had superior performance over

the past four and a half years. Figure 1 reviews just how superior that performance has

been: from December 2001 through April 2006, the Goldman Sachs Commodity Index

(GSCI) has returned 23.1% per year while the S&P 500 has returned a more modest 4.9%

per year.

Given these returns, commodity investing has become a sign of sophistication.

Commodities can give “turbo returns if things go wrong for equities and bond markets,”

stated the chief investment officer of a large British pension plan, as quoted in Rees

(2006). The significant growth in commodity index investing is shown in Figure 2.

New commitments from pension funds have fueled some of this growth. “Even normally

cautious British pension funds are following the more adventurous US precedents and are

allocating assets to commodities: the Sainsbury fund has targeted a 5% exposure, the

British Telecom fund just under 3%. ... Global pension funds are estimated by investment

consultants Watson Wyatt to be worth $16 trillion and if they, like the BT fund, were to

allocated 3% to commodities, this would amount to $500bn cascading into what have

been quite narrow markets,” noted a Financial News (2006) story.

Because commodity index investing has grown from an obscure, niche strategy to a more

widely accepted investment, there has been a need to better understand the drivers of

historical commodity returns and risks. An investor would presumably then be in a better

position to make informed judgments on the future prospects of a commodity investment.

This article will provide the busy reader with a summary of the new research on this

topic.

Historical Returns

A recent edition of the prestigious Financial Analysts Journal (FAJ) included two

articles, which explored the historical returns of commodity futures indices. Gorton of

the University of Pennsylvania and Rouwenhorst of Yale University studied the

properties of commodity futures over the period, 1959 though 2004, in the article, “Facts

and Fantasies about Commodity Futures,” in the March/April 2006 FAJ.

Gorton and Rouwenhorst created a monthly time series, starting in 1959, of an equally-

weighted index of commodity futures. Their index was rebalanced monthly. They found

that over the time period of their study that fully collateralized commodity futures

historically had offered the same return and Sharpe ratio as U.S. equities. (The Sharpe

ratio is calculated as an investment’s excess returns over Treasury bills, divided by the

investment’s standard deviation.)

Figure 3 summarizes the historical excess returns of their commodity futures index versus

the returns of stocks and bonds. Stocks are represented by the S&P 500 index, and bonds

are represented by the Ibbotson U.S. corporate bond index. Again, “excess returns”

means that these are the returns over an investment in risk-less Treasury bills.

They also found that commodity futures returns were negatively correlated with equity

and bond returns. This means that over the time period of their study, a commodity

futures investment would have added a new source of returns for an investor, not

accessible through stock and bond investments.

Decomposition of Historical Returns:

Rebalancing, Term-Structure Characteristics, and Paradigm Shifts

The Gorton and Rouwenhorst study is very valuable in providing investors with a

carefully updated examination of historical commodity returns. An additional area of

fertile research has been to understand what the drivers of those historical returns were.

One highly nuanced point about commodity futures investing is that historically their

long-term returns did not rely on broad-based rallies in spot commodity prices. For

example, the second column in Figure 4 shows how the spot prices of a number of

commodities ranged between -2.8% to +3.3% per year from 1983 to 2004. The first

column in Figure 4 shows for each commodity, how much a commodity’s futures returns

can be different from its spot returns. The source of these substantial differences will be

explained below in the “Term-Structure Characteristics” section of this article.

Another highly nuanced point is that when one combines individual commodity contracts

into an index and then actively rebalances their weights periodically, that rebalancing can

also be an additional source of return for a commodity investor. This effect is not

obvious when only examining individual commodity futures contract returns since this is

a portfolio-level effect. This effect, in turn, will be discussed below in the “Rebalancing”

section of this article.

We will also discuss another source of returns, which has scant historical evidence, but

nonetheless may have relevance for commodity investors going forward, and that is a rare

trend shift in spot commodity prices, as occurred during 1970-to-1974. This scenario

will be discussed below in the “Paradigm Shift: The 1970’s Revisited” section of this

article.

Rebalancing

In a second March/April 2006 FAJ article, Erb of Trust Company of the West and Harvey

of Duke University carefully dissect the historical drivers of commodity returns in the

article, “The Strategic and Tactical Value of Commodity Futures.”

They examine the returns of sixteen commodity futures contracts over the period, 1982 to

2004. The average correlation of individual commodities with one another was quite

low: only about 9%. The average standard deviation of the commodities that they studied

was 25%. It turns out that combining lowly correlated, highly volatile instruments can

result in additional index-level returns.

Erb and Harvey show mathematically that “when asset variances are high and

correlations are low,” the diversification return from rebalancing can be high. For

example, “for an equally weighted portfolio of 30 securities with average individual

security standard deviations of 30 percent a year and average security correlations

ranging from 0.0 to 0.3, the diversification return [alone] ranges from 3.05 percent to

4.35 percent.” This return is separate from any returns due to each individual commodity

within the index.

Note that by specifying that the portfolio is equally-weighted, this implicitly means one

will be actively rebalancing the portfolio to maintain its equal weights across instruments.

The returns from rebalancing a commodity portfolio could have been quite meaningful

(historically) because of their constituent’s low mutual correlation and high volatility.

This return-enhancing effect has not been obvious to equity-index investors because of

the typically high mutual correlations amongst equities.

One should also add that a typical investment in a commodity futures portfolio is “fully

collateralized.” A collateralized commodity futures program is unleveraged. That is, for

every desired $1 in commodity futures exposure, an investor must set aside $1 in money-

market funds, making the futures program fully collateralized. When calculating the

returns to a collateralized commodity futures program, one typically includes the

collateral returns as well.

So even if the individual futures contracts in an equally-weighted index have returns that

oscillate around zero, the rebalancing effect plus collateral returns can add up to

meaningful numbers.

The rebalancing effect had also been explained by Greer of PIMCO in a 2000 Journal of

Alternative Investments article. Rather than analyzing an equally-weighted commodity

index, he examined the properties of an index that is value-weighted. Both weighting

schemes are related since both involve rebalancing. Explained Greer, “This value-

weighted construction simply means that each commodity will be given a fixed

percentage of the value of the portfolio. As prices fluctuate, the index reflects the idea of

selling the futures that go up and buying those that go down to maintain this constant

balance. Unless there is an economic reason to expect futures prices to trend indefinitely

up or down, then this construction should provide incremental return to the extent that the

various futures in the index are uncorrelated.” And indeed, from 1970 through 1999, the

value-weighted commodity index considered in Greer’s article had meaningful returns

due to rebalancing alone that ranged from 0.56% to 6.25% per year.

Because of the new academic interest in commodities, one might expect that the

importance of the potential return due to rebalancing will become more widely

appreciated by commodity investors.

Term-Structure Characteristics

Another source of returns in commodity futures investing is due to the arcane concept of

“roll yield,” which we originally explained in Till (2006b).

In the past, even if spot commodity prices declined, there was an additional way that a

commodity investor could have a positive statistical expectation of profit, and that was

through the “roll yield” embedded in certain commodity futures contracts. In case the

reader finds concepts like the “term structure of a commodity futures contract” and “roll

yield” esoteric, these concepts are explained below.

By term structure, we mean one should examine the relative price differences of futures

contracts across delivery months. When a near-month contract is trading at a premium to

more distant contracts, we say that a commodity futures curve is in “backwardation.”

Conversely, when a near-month contract is trading at a discount to more distant contracts,

we say that the curve is in “contango.”

Typically when there are low inventories for a commodity, its commodity futures

contract trades in backwardation: consumers are willing to pay a premium for the

immediately deliverable contract relative to deferred-delivery-month contracts.

When a commodity futures contract is in backwardation, an investor has two potential

sources of returns. Since backwardation typically indicates scarcity, one is on the correct

side of a potential price spike in the commodity by being long at that time.

The other source of return involves a bit more explanation. In a backwardated futures

market, a futures contract converges (or rolls up) to the spot price. This is the “roll yield”

that a futures investor captures. The spot price can stay constant, but an investor will still

earn returns from buying discounted futures contracts, which continuously roll up to the

constant spot price. A bond investor might liken this situation to one of earning “positive

carry.” In a contango market, the reverse occurs: an investor continuously locks in

losses from futures contracts converging to a lower spot price. Correspondingly, a bond

investor might liken this scenario to one of earning “negative carry.”

Over very long timeframes, a number of authors have shown how the term structure of a

commodity futures curve has been the dominant driver of returns in futures investing. In

other words, trends in the spot price of a commodity have generally not been a

meaningful driver of returns over long periods of time.

n particular, Nash and Shrayer of Morgan Stanley (2004) have illustrated how over a

single 21-year timeframe, the returns of a commodity futures contract have been linearly

related to how backwardated the contract has been. This empirical observation is shown

in Figure 5. Over the period, 1983 to 2004, the commodity futures contracts that have

had the highest returns are those in which the front-month contract traded at a premium to

the deferred-delivery contracts; that is, those contracts that had the highest levels of

backwardation had the highest returns. Correspondingly, the contracts that have had the

most negative results are those that typically traded at a discount to the deferred-delivery

contracts; again, those contracts that had the highest levels of contango on average had

the lowest returns.

In more recent research, Feldman and Till (2006) extend the framework originated by

Nash of Morgan Stanley. We find evidence that the power of backwardation to explain

commodity futures returns is indeed valid, but requires the investor to have a long

investment time horizon when relying on this indicator. Specifically, we examine the

soybean, corn, and wheat futures markets over the period, 1950 to 2004. We find that a

contract’s average level of backwardation only explains 24% of the variation in futures

returns over 1-year timeframes and 39% of variation over 2-year timeframes. One must

extend the evaluation period to five years, and then at that time horizon, average levels of

backwardation explain 64% of the variation in futures returns. Figure 6 illustrates the

latter result. Figure 7 provides a related analysis: this graph shows that over five-year

time horizons, the relationship of annualized return to a contract’s average-time-inbackwardation

is again highly linear.

Short-term variability in commodity prices is high, which should make the spot-price

return the dominant factor over shorter horizons. Over longer periods, it appears that spot

commodity prices tend to be mean-reverting. This suggests that the importance of the

spot return should decline as timeframes increase.

The foregoing suggests that there should be a gradual increase in the fraction of futures

return explained by backwardation with increasing time horizon. This relationship is

similar in spirit to the increasing importance of dividend yield as a predictor of equity

return with the lengthening of the time horizon documented by Cochrane (1999). With

one-year horizon the R-squared value of the regression of dividend yield on excess return

is 17%, but at five years the R-squared value becomes 59%. These results are over the

sample period, 1947 to 1996. Cochrane explains this as the result of the cumulative

effects of the slight short-term predictability of a slow-moving variable.

Paradigm Shift: The 1970’s Revisited

As touched upon in Till (2006b), while we found that backwardation has been a driver of

returns over long time horizons for three agricultural futures markets, there is another

noteworthy feature of our historical results. While normally over five-year periods, an

agricultural futures contract’s curve shape has been the driver of returns, there is one

exception, and that is the 1970-to-1974 period. These are the data points in Figure 7 that

do not fit the nearly linear trend-lines of annualized returns as a function of average

backwardation.

What this means for an investor is that there can be an additional fundamental rationale

for a long-term, passive investment in a commodity futures contract besides predicting

structural backwardation for the contract. The second rationale would be to predict that

the factors are in place to repeat the 1970-to-1974 experience. For example, Howell of

Schroders (2005) points out how excessive monetary stimulus had contributed to the high

returns of commodities in the past. Specifically, Howell notes that negative real interest

rates in the 1970’s contributed to a commodity boom at the time. And real short-term

interest rates had become negative in the United States and in China during early 2005.

Further, Roach of Morgan Stanley (2006) discusses the current economic environment as

a “super liquidity cycle,” which is pushing the “Asset Economy to its limit,” of which

one manifestation is the boom in prices of certain commodities.

Now obviously one needs to be very careful about predicting trend shifts in asset prices.

Grantham (2005) notes that his firm has completed research on “30 completed [asset

price] bubbles … all of which came back to the pre-existing trend.” But he states, “Of

these, we now believe 29 were genuine bubbles, and one – oil – was a paradigm shift …”

that occurred in 1973. This is illustrated in Figure 8. Grantham, as a well-known

dedicated “mean-reverter,” who underweighted Japanese equities in the late 1980’s and

later underweighted U.S. technology stocks in the late 1990’s, is pausing in calling for oil

to mean-revert. Even if oil becomes $80 per barrel, “given the unique features of oil, we

cannot be sure it has not ratcheted up again with another trend shift.”

Risk Considerations

Thus far, this article has focused on the drivers of returns for commodity indices and

individual commodity futures contracts. This required a thorough discussion because two

of the sources of return: rebalancing and roll yield are definitely not obvious. We also

noted that rare trend shifts in commodity prices can also be a meaningful source of return.

Another area of interest for investors is obviously the flipside of return: risk.

The following section will draw from Till (2006a) in discussing portfolio-level risk

considerations for an active commodity manager. This discussion will focus on risk

considerations, which like the return-driver discussion, are not obvious to the neophyte in

commodity investing.

Risk management at the portfolio level is fundamentally different from risk management

at the strategy level. At the portfolio level, an investor is concerned with how dynamic

correlations among strategies may affect portfolio-level risk. An investor is further

concerned with how one’s commodity portfolio may perform during financial shocks

since commodity products are frequently expected to be uncorrelated to the dominant

financial asset classes. This section will describe appropriate portfolio-level analyses that

address these concerns.

Diversified Portfolio Goal

Erb and Harvey had discussed how to a large degree, commodity futures are uncorrelated

with one another. A commodity portfolio manager will use this property of commodity

futures contracts to attempt to create a portfolio of diversified commodity strategies with

dampened risk. Commodity hedge fund manager Paul Touradji affirms this view: “One

of the best things about being a commodity manager is the natural internal

diversification.” “While even unrelated equities have a beta to the overall market, many

commodities, such as sugar and aluminum, traditionally have no correlation at all,”

according to Teague (2004) in his interview with the hedge fund manager.

One difficulty with using historical correlations to evaluate portfolio risk is that

correlations amongst commodities vary both seasonally and during eventful periods.

There are times when a common factor can impact seemingly unrelated positions,

causing a seemingly diversified portfolio to have inadvertent concentration risk to the

common factor. Therefore, a commodity investor needs to include scenario analyses,

which show a portfolio’s sensitivity to meaningful events, in his or her risk-measurement

toolkit. Example scenario analyses are provided below.

Extreme Weather Events

Normally, natural gas and corn prices are unrelated. But during the summer, they can be

highly correlated. During a three-week period in July 1999, for example, natural gas and

corn prices were +85% correlated. Both corn and natural gas trades are heavily

dependent on the outcome of weather in the U.S. Midwest. And in July, 1999, the

Midwest had blistering temperatures (which even led to some power outages.) During

that time, both corn and natural gas futures prices responded in nearly identical fashions

to weather forecasts and realizations.

What this means for commodity managers is that they should measure how much

sensitivity their portfolio has to extreme summer weather in the Midwest. The manager

would want to ensure that in the event of a heat-wave in the U.S. Midwest that his or her

portfolio would not perform exceptionally poorly.

Other potentially extreme weather shocks to include in ongoing scenario analyses include

the chance of an end-of-February cold shock on energy positions as well as the possibility

of a damaging hurricane season in the fall.

Sharp Shocks to Business Confidence

Futures products are typically marketed as equity investment diversifiers. Therefore, one

job of risk management is to attempt to ensure that a futures investment will not be too

correlated to the equity market during periods of dramatic equity losses.

Although a commodity futures portfolio may contain no financial futures contracts, the

portfolio can still have systematic risk to the stock market. For example, Bessembinder

(1992) found that live cattle, soybeans, silver and platinum futures contracts had

statistically significant betas to the U.S. stock market using data from January 1967 to

December 1989. (The data for platinum started in January 1968.) More recently, Erb

and Harvey state that “the non-energy sector has a statistically significant, but small

equity risk premia beta” using data from 1982 to 2004.

Given the potential of a commodity portfolio to perform poorly during financial shocks, a

manager should therefore examine what the portfolio’s performance would have been

during the October 1987 stock market crash, the 1990 Gulf War, the Fall 1998 bond

debacle, and during the immediate aftermath of September 11, 2001. If the commodity

portfolio would have done poorly during these events, then the manager may consider

either deleveraging his or her portfolio or buying option protection against one of the

damaging scenarios.

Caveat on Dynamic Correlations: The Relationship Between Commodities and Interest

Rates

Correlations During the Aftermath of the 9/11/01 Attacks

A number of commodity futures strategies have a long commodity bias since they rely on

taking on inventory risk that commercial participants wish to lay off. One consequence is

that these strategies are at risk to sharp shocks to business confidence. And during sharp

shocks to business confidence as occurred in the aftermath of September 11th 2001, not

only did the stock market perform quite poorly, but economically sensitive commodities

also performed poorly.

The Greenspan Federal Reserve Board had responded to financial shocks by cutting

interest rates, which resulted in the stock market stabilizing. As long as this type of

policy continues, one way to hedge a portfolio that has exposure to shocks to business

confidence or shocks to the availability of credit is to include a fixed-income hedge. The

hedge could take the form of either a Eurodollar futures contract overlay or purchases of

out-of-the-money fixed-income calls. The post-9/11/01 experience validated that a long

fixed-income position was an effective hedge for a portfolio that is primarily long

economically sensitive commodities.

Figure 9 reviews the performance of gasoline futures contracts and short-term interest

rate contracts in the aftermath of the 9/11/01 attacks. While gasoline prices plummeted

due to the expectation of an economic slowdown, short-term interest rate contracts rallied

as the Federal Reserve Board (Fed) cut interest rates to calm the financial markets.

Correlations During the Aftermath of Hurricane Katrina

One caveat to this lesson is that the relationship between commodities and interest rates

varies according to the type of meaningful event. For example, during the aftermath of

Hurricane Katrina in late August through the middle of September 2005, gasoline and

short-term interest rates reacted similarly to the prospect of scarce gasoline supplies, as

shown in Figure 10. During the initial explosive rise in gasoline prices, due to the shutdown

of crucial Gulf Coast refineries, interest-rate-market participants concluded that the

Fed would pause in its interest-rate tightening cycle, which then caused deferred month

interest-rate contracts to rally.

According to a Dow Jones Newswire report (2005) of the time, “[Hurricane] Katrina shut

in nearly all of oil and gas production in the Gulf of Mexico … The large scale supply

disruption and fear of an economic shock triggered a massive government response. The

outages prompted the Bush administration to release Strategic Petroleum Reserve oil,

waive air-pollution rules on fuels, and ease restrictions on use of foreign-flagged vessels

to carry fuel in U.S. waters.” Further, “Members of the Organization of Economic

Cooperation and Development agreed … to [release] 2 million barrels a day of crude oil

and petroleum products from their strategic stocks for 30 days.”

This unprecedented government response caused gasoline prices to decline from their

post-Katrina peak, and with that response, fears of an economic slump diminished, which

in turn caused deferred interest-rate contracts to also decline, as the market resumed

pricing in the expectation that the Fed would continue tightening interest rates.

In the scenario just described, changes in daily gasoline prices and short-term interest

rates became +75% correlated during the aftermath of Hurricane Katrina. This is in sharp

contrast to the negative relationship between changes in gasoline prices and short-term

interest rates that occurred in the aftermath of Twin Tower attacks.

In the aftermath of Hurricane Katrina, long positions in interest rates did not serve as an

event hedge for long positions in gasoline; instead these two positions became the same

trade, both on the upside and the downside.

The lesson from this section is that risk management at the portfolio level is a constant

and dynamic process.

Final Note: Prospective Returns

It is obviously useful to have a well-informed view on what the source of commodity

indices’ equity-like returns were. And in addition, if one were considering an actively

managed commodity program instead of an indexed investment, it is a good idea to have

a well-informed view of what the sources of risk in such a program are.

But clearly, what is most of interest to an investor is a prospective view of returns. At

this time, the commodity index with the largest share of investor assets is the Goldman

Sachs Commodity Index (GSCI). As of 11 May 2006, this index was weighted 73.4% in

the energy sector. So it would likely be useful for us to examine the prospects for oil.

One challenging aspect of investing in oil futures at this time is that they appear to have

shifted into “structural contango.” As noted above, this means that an investor will have

to absorb “negative carry” with their oil-based investments. This is analogous to

investing in gold futures contracts where there has been a historical cost in synthetically

paying for the storage costs of this commodity. Historically, the behavior of oil prices

has been one of “structural backwardation,” consistent with the notion of crude oil

inventories generally being scarce.

That crude oil futures have shifted into structural contango seems to contradict the

tightness that is implied by this commodity’s continuous spot price rally. What has

changed?

One theory from a prominent hedge fund is that the true inventories for crude oil should

be represented as above-ground stocks plus excess capacity. Historically, the markets

could tolerate relatively low oil inventories because there was sufficient swing capacity

that could be brought on stream relatively quickly in the case of any supply disruption.

This excess supply cushion has dropped to sufficiently low levels that there have been

two market responses: (1) there have been continuously high spot prices to encourage

either consumer conservation or the development of alternative energy supplies, and (2)

the market has undertaken precautionary stock building, which has led to the steep

contangos that the crude oil market has been experiencing.

Stuart of UBS (2006) has examined the predicted supply and demand growth through

2010, and it appears that on trend, there will be no meaningful increase in oil spare

capacity over the next four years, as shown in Figure 11. In addition the International

Energy Agency (IEA) director stated, “Our impression is that the increased [oil supply]

capacity will just be more or less equal to the increase in demand, [with the result that]

spare capacity will not increase before 2009 or 2010,” as quoted in Meir (2006).

As briefly discussed in Till (2006b), in the absence of oil producers building up a spare

capacity cushion and in the absence of alternative energy sources effectively replacing oil

usage, the only lever to eventually balance supply and demand is demand destruction, as

reasoned by Murti et al. of Goldman Sachs (2005a). The Goldman analysts examine the

experience of the late 1970’s and early 1980’s to see what price spikes are required to

create demand destruction and refer to their predictions as a “super spike” range.

The implication of this structural change in the oil markets is that the returns to energy-

focused commodity investments could become ever more long-option-like. The investor

will pay away option-like premia in the form of the negative carry from the persistent

contango in the oil markets, but will simultaneously be positioned for periodic (and

entirely unpredictable) price spikes until an adequate supply cushion reemerges in the oil

markets.

That said, as Murti et al. (2005b) predict, one would expect that eventually a supply

cushion will reemerge, either through behavioral changes on the part of consumers or

through new infrastructure finally being constructed by producers. These changes may

not occur until the end of the decade, given the very long lead time for large-scale energy

projects. It is at that point one may see oil spot-prices dramatically mean-reverting,

which would confirm that a curve indicator can only be expected to be useful at very long

investment horizons.

Conclusion

This article provides a nuanced view of commodity futures investing. We discussed how

commodity returns had in the past mainly relied on portfolio effects and term-structure

properties of individual commodity futures contracts. But we also noted that rare trend

shifts, as occurred in the early 1970’s, can also be a meaningful source of returns for a

commodity investor. We further discussed some of the dynamic correlation properties of

active commodity investing. These properties are also quite nuanced.

Finally, we examined the prospects of the main constituent of the dominant commodity

index – oil – and provided a framework for understanding what could potentially drive

future returns.

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