Commodity Market Capital Flow and Asset Return
Predictability∗
Harrison Hong†
Motohiro Yogo‡
February 28, 2010
Abstract
We establish several new findings on the relation between capital flow in commodity markets and asset returns. Capital flowing into commodity markets, as measured
by high open-interest growth, predicts high commodity returns and low bond returns.
Open-interest growth is a more powerful and robust predictor of commodity returns
than other known predictors such as the short rate, the yield spread, the basis, and
hedging pressure. It is positively correlated with commodity returns but has information for future returns beyond that contained in past commodity prices. Open-interest
growth also predicts changes in inflation and inflation expectations. These findings
suggest that open-interest growth contains information about future inflation that gets
priced into commodity and bond markets with delay. Our findings are consistent with
recent theories of gradual information diffusion and have implications for macroeconomic forecasting models.
∗
This paper subsumes our earlier work titled “Digging into Commodities”. For comments and discussions,
we thank Erkko Etula, Hong Liu, David Robinson, Nikolai Roussanov, Allan Timmermann, and seminar
participants at Boston College, Centre de Recherche en Economie et Statistique, Dartmouth College, Fordham University, PanAgora Asset Management, Stockholm School of Economics, University of California San
Diego, University of Pennsylvania, University of Southern California, Washington University in St. Louis,
the 2008 Economic Research Initiatives at Duke Conference on Identification Issues in Economics, and the
2010 Annual Meeting of the American Finance Association. We thank Jennifer Kwok, Hui Fang, Yupeng
Liu, James Luo, Thien Nguyen, and Elizabeth So for research assistance. Hong acknowledges a grant from
the National Science Foundation. Yogo acknowledges a grant from the Rodney L. White Center for Financial
Research at the University of Pennsylvania.
†
Princeton University and NBER (e-mail: hhong@princeton.edu)
‡
University of Pennsylvania and NBER (e-mail: yogo@wharton.upenn.edu)
1.
Introduction
We analyze how capital flow in commodity markets is related to commodity and bond returns. Our analysis is motivated by the recent volatility in commodity prices and the renewed
interest in the behavior of these markets, which have not been seen since the energy crisis of
the 1970s. Once largely ignored by the investment community, commodities have emerged as
an important asset class. By some estimates, index investment in this asset class increased
from $13 billion at the end of 2003 to $317 billion in July 2008, just prior to the financial
crisis (Masters and White, 2008). During the same period, the influx of new investors led to
elevated levels of capital flow as measured by open interest in commodity futures, which grew
from $103 billion to $509 billion. This capital flow has led to inquiries about how trading
affects asset price formation in these markets, underscored by the recent Congressional hearings on the impact of excessive speculation. Hence, understanding the link between capital
flow and commodity price fluctuations is important not only for investors but also for public
policy.
Our analysis covers 30 commodities across four sectors (agriculture, energy, livestock,
and metals) over the period of 1965 through 2008. Using hand-collected data on open interest from the Commitments of Traders since 1965, we establish several new findings on the
relation between capital flow and returns in commodity markets. Figure 1 summarizes our
main finding. The first series is the percentage change in open interest over the previous
12 months, averaged across all commodities. The second series is the return on fully collateralized commodity futures over the previous 12 months, averaged across all commodities.
During the recent commodity boom from 2003 to 2008, capital flowed into the sector at a
persistently high rate, more so than in any other period over the previous thirty years. Only
the energy crisis of the 1970s witnessed higher activity. During these two historic periods
and also more generally, open-interest growth and commodity returns are highly correlated.
But the most interesting finding in this plot is that open-interest growth seems to lead commodity returns. In other words, capital flowing into commodity markets appears to predict
2
subsequent appreciation of commodity prices.
To formalize our observation, we regress the monthly excess returns on a portfolio of
commodity futures onto lagged 12-month open-interest growth. We find that a standard
deviation increase in open-interest growth increases expected commodity returns by 0.64%
per month. Similarly, we find that a standard deviation increase in open-interest growth
increases expected spot-price growth by 0.41% per month. Both of these estimates are
economically large and statistically significant. Open-interest growth is a more powerful and
robust predictor than a number of other variables that are known to predict commodity
returns. These include common predictors such as the short rate and the yield spread and
commodity-specific predictors such as aggregate basis (i.e., the ratio of futures to spot price
averaged across commodities) and aggregate hedging pressure (i.e., the net short position
of hedgers averaged across commodities).1 Open-interest growth is a more robust predictor
than these other variables in two important ways. First, aggregate open-interest growth
predicts returns on sector portfolios, in contrast to other variables that predict returns for
only particular sectors. Second, open-interest growth is the only variable that continues
to demonstrate forecasting power in the most recent period since 1987, when there are the
greatest number of commodities in the database.
Open-interest growth is most closely related to 12-month commodity returns. We find
that past aggregate commodity returns forecast the subsequent month’s return. In other
words, there is momentum in the time series of aggregate commodity returns. However, in a
horse race between these variables, open-interest growth entirely drives out the forecasting
power of past commodity returns. This means that open-interest growth contains information about future returns that is not fully captured by past commodity prices. A potential
1
Bessembinder and Chan (1992) are the first to establish that the same variables that predict bond and
stock returns (such as the short rate, the default spread, and the dividend yield) also predict commodity
returns. There is mixed evidence that basis predicts returns on commodity futures. Fama and French (1987)
are the first to establish that basis predicts returns for some commodities. They emphasize that there is
more consistent evidence for the theory of storage. A number of other studies have documented mixed
evidence for the theory of backwardation, controlling for systematic risk and using an empirical proxy for
hedging pressure (Carter, Rausser, and Schmitz, 1983; Chang, 1985; Bessembinder, 1992; de Roon, Nijman,
and Veld, 2000).
3
interpretation of these findings is that capital flows into commodity markets in response to
news, which get impounded into commodity prices with delay. In the 1970s, for example,
there were news about supply shocks to oil. In the most recent period, there were news
about strong demand for commodities from the emerging economies.
To test this hypothesis, we examine whether open-interest growth predicts inflation and
excess bond returns. Consistent with the hypothesis, we find that high open-interest growth
predicts rising inflation and also a rising nominal short rate. In addition, high open-interest
growth predicts low bond returns with a t-statistic over 3. A standard deviation increase in
open-interest growth decreases expected bond returns by 0.32% per month. Open-interest
growth is the only predictor that survives in the most recent period since 1987, when the
short rate and the yield spread fail to predict bond returns. Hence, open-interest growth
not only contains powerful information about future commodity returns, but also important
information about future bond returns.
To summarize, our novel finding is that capital flow into the commodity sector contains
information about inflation news and bond returns that is not fully captured by commodity
prices. As we discuss in the body, our findings are most consistent with recent theories
of gradual information diffusion in asset markets (see Hong and Stein, 2007, for a review).
These theories suggest that when market prices under-react to news, trading activity emerges
as a useful additional predictor of future returns. Moreover, the fact that capital flow can be
useful for predicting economic activity like inflation expectations has important implications
for macroeconomic forecasting models.
Our work is part of a second generation of commodity papers that have recently emerged
in response to renewed interest in commodity markets. In a pioneering study that lays
out the agenda, Gorton and Rouwenhorst (2006) emphasize that commodities have a high
Sharpe ratio and a low correlation with other asset classes. They argue that this evidence
is consistent with the theory of backwardation in particular and market segmentation more
generally. Acharya, Lochstoer, and Ramadorai (2009) find that producers’ hedging demand,
4
as captured by their default risk, predicts commodity returns. Etula (2009) finds that the
supply of speculator capital, as captured by changes in broker-dealer balance sheets, predicts
commodity returns, especially in energy. Relative to these studies, we share the view that
market segmentation is a key driver of predictability in commodity markets. However, our
focus on the implications of gradual information diffusion is quite different from these other
studies that focus on limited risk-bearing capacity in commodity markets. More closely
related is a group of studies that document momentum in the cross section of commodity
returns (Erb and Harvey, 2006; Gorton, Hayashi, and Rouwenhorst, 2007; Miffre and Rallis,
2007; Asness, Moskowitz, and Pedersen, 2009). We find momentum in the time series of
aggregate commodity returns, which interacts with capital flow into commodity markets.
The rest of the paper proceeds as follows. Section 2 describes the commodity market data
and the construction of the key variables used in our empirical analysis. Section 3 reports
summary statistics for commodity returns, spot-price growth, and the predictor variables.
Section 4 presents our main finding that open-interest growth predicts commodity returns.
We also present evidence that open-interest growth predicts inflation news and bond returns
to illuminate the economic mechanism behind our findings. Section 5 concludes.
2.
2.1.
Commodity Market Data and Definitions
Commodity Market Data
Our data on commodity prices are from the Commodity Research Bureau, which has daily
prices for individual futures contracts as well as spot prices for many commodities beginning
in December 1964. Gorton and Rouwenhorst (2006) also use this database, and additional
details can be found in the appendix to their paper. As they point out, the database mostly
contains data for contracts that have survived until the present or that were in existence for
an extended period between 1965 and the present. Many different types of contracts fail to
survive because of lack of interest from market participants, and they are consequently not
5
recorded in the database. Consequently, the computed returns on commodity futures may
be subject to survivorship bias.
Following Gorton and Rouwenhorst (2006), we work with a broad set of commodities
contained in the database. Table 1 is a list all our commodities, together with the date
of the first recorded futures price for each commodity. We categorize commodities into
four broad sectors. Agriculture consists of 15 commodities and tends to contain the oldest
contracts. Energy consists of five commodities. Heating oil is the oldest contract in energy,
which starts in November 1978. Data for crude oil are available only since March 1983.
Livestock consists of five commodities, and metals consists of six commodities. A potential
concern with using a broad set of commodities is that not all contracts are liquid. In results
that are not reported here, we have confirmed our main findings on a subset of 17 relatively
liquid commodities that are in the AIG Commodities Index.
Following Gorton and Rouwenhorst (2006), we exclude futures contracts with one month
or less to maturity. These contracts are typically illiquid because futures traders do not
want to take delivery of the underlying physical commodity. We therefore rule out investment
strategies that require holding futures contracts to maturity. While Gorton and Rouwenhorst
(2006) isolate the contract that is closest to maturity for each commodity, we include all
contracts with more than one month to maturity.
We also use data on open interest (i.e., the number of futures contracts outstanding)
as well as the long and short positions of noncommercial traders (or “hedgers”) for each
commodity. Since January 1986, the data are available electronically from the Commodity Futures Trading Commission. Prior to that date, we hand-collected data from various
volumes of the Commitments of Traders in Commodity Futures. Data for December 1964
through June 1972 are from the Commodity Exchange Authority (1964–1972). Data for
July 1972 through December 1985 are from the Commodity Futures Trading Commission
(1972–1985). There is a 11 month gap from January through November of 1982, during
which the Commodity Futures Trading Commission did not collect data due to budgetary
6
reasons.
Figure 2 shows the share of total dollar open interest that each sector represents. The
figure shows that agriculture dominates the early part of the sample, while energy becomes
the biggest sector later in the sample. The relative size of the four sectors is much more
balanced in the second half of the sample starting in 1987. These stylized facts have two important implications for our empirical analysis. First, we construct the aggregate commodity
portfolio as an equal-weighted portfolio of the four sectors, which ensures that the portfolio
composition is consistent throughout the sample. Second, we examine the predictability of
commodity returns by sector and in subsamples to check the robustness of our main results.
The subsample since 1987 is perhaps more representative of what we can expect from commodity markets going forward because it has a more balanced representation across the four
sectors.
2.2.
Aggregate Commodity Returns
To construct aggregate commodity returns, we first compute the return on a fully collateralized position in commodity futures as follows. Let Rf,t be the monthly gross return on
the 1-month T-bill in month t, which is assumed to be the interest earned on collateral. Let
Fi,t,T be the price of a futures contract on commodity i at the end of month t, which matures
at the end of month T . The monthly gross return on a fully collateralized long position in
commodity i with maturity T − t is
Ri,t,T =
Fi,t,T Rf,t
.
Fi,t−1,T
(1)
We sort the universe of commodity futures into four sectors and two levels of maturity.
We define short maturity contracts as those with more than one but no more than three
months to maturity. Long maturity contracts are those with more than three months to
maturity. We then construct eight equal-weighted portfolios of commodity futures, corre-
7
sponding to two levels of maturity for each of the four sectors. For each portfolio, we compute
its monthly gross return as an equal-weighted average of returns on fully collateralized commodity futures. Finally, we construct an aggregate commodity portfolio as an equal-weighted
portfolio of the these eight portfolios. The aggregate commodity portfolio that results from
this construction is consistently balanced with respect to sector and maturity.
For some of our analysis, it is useful to look at commodity returns separately by sector
and maturity. Using the eight portfolios, we construct four sector portfolios as an equalweighted portfolio of the short-maturity and long-maturity portfolio for each sector. For
example, the agriculture portfolio is an equal-weighted portfolio of the short-maturity and
the long-maturity portfolio for agriculture. Using the eight portfolios, we also construct two
maturity-sorted portfolios as an equal-weighted portfolio of the four sector portfolios for each
level of maturity. For example, the short-maturity portfolio is an equal-weighted portfolio
of the short-maturity portfolio for agriculture, energy, livestock, and metals.
Our construction of the aggregate commodity portfolio differs from that of Gorton and
Rouwenhorst (2006), who construct their portfolio by equal-weighting all commodities that
exist at each point in time. The advantage of our approach is that the sectors are always
equal-weighted, and hence no sector dominates even as the number of commodities within
each sector changes over time. Despite the differences in the construction, the summary
statistics for our aggregate commodity portfolio (reported in Section 3) are very close to
those reported by Gorton and Rouwenhorst (2006).
2.3.
Aggregate Spot-Price Growth
We construct aggregate spot-price growth in analogy to our construction of aggregate commodity returns. Let Si,t be the spot price of commodity i at the end of month t. The monthly
spot-price growth for commodity i is
Gi,t =
Si,t
.
Si,t−1
8
(2)
We compute spot-price growth for each sector as an equal-weighted average of all commodities in that sector. We then compute aggregate spot-price growth as an equal-weighted
average spot-price growth across the four sectors.
The reason we examine spot-price growth separately from futures price movements is
that spot-price growth is economically different from the return on commodity futures for
two important reasons. First, unlike investment in physical commodities, commodity futures
are financial investments like bonds and stocks. Therefore, the results for commodity futures
are more meaningful than those for the spot price from a pure investment or asset allocation
perspective. Second, as we discuss below, different theories have different implications for
mean reversion in futures versus spot prices. Therefore, the way in which a variable predicts
futures versus spot prices can help us discriminate among different theories.
2.4.
Predictor Variables
Our key predictor variable is aggregate open-interest growth. To construct this variable,
we first compute the dollar open interest for each commodity as the spot price times the
number of contracts outstanding. We then aggregate dollar open interest within each sector
and compute its monthly growth rate. Finally, we compute aggregate open-interest growth
as an equal-weighted average of open-interest growth across the four sectors.2 Because
monthly open-interest growth is noisy, we smooth it by taking a 12-month geometric average
in the time series. A variable that is closely related to open-interest growth is the 12month geometric average of aggregate commodity returns. We use this variable to test for
momentum in aggregate commodity returns.
In addition to open-interest growth, we consider a variety of predictor variables that are
known to predict commodity returns, which can grouped into two categories (see de Roon
2
We have tried an alternative construction that uses only the number of contracts outstanding and does
not involve the spot price. We first compute the growth rate of open interest for each commodity. We then
compute the median of open-interest growth across all commodities within each sector. Finally, we compute
aggregate open-interest growth as an equal-weighted average of open-interest growth across the four sectors.
This alternative construction leads to a time series that is very similar to our preferred construction of
open-interest growth.
9
et al., 2009, for a similar list). The first category consists of aggregate market predictors,
which are motivated by theories like the (I)CAPM that view commodity markets as being
fully integrated. According to this view, commodity prices are driven by aggregate market
predictors that influence portfolio allocation decisions across different asset classes. An
implication of this theory is commodities may be useful for hedging time-varying investment
opportunities in other asset classes. Hence, commodity prices may be high, or its expected
returns may be low, when such hedging motives are important. Since an investor can hedge
market fluctuations by either entering futures contracts or by holding physical commodities,
this theory implies that the same aggregate market predictors should predict returns on
commodity futures and spot-price growth with similar sign and magnitude.
We focus on two aggregate market variables that are known to predict the common variation in bond and stock returns: the short rate and the yield spread (Fama and Schwert,
1977; Campbell, 1987; Fama and French, 1989). These variables are also known to predict
commodity returns (Bessembinder and Chan, 1992; Bjornson and Carter, 1997). The short
rate is the monthly average yield on the 1-month T-bill. The yield spread is the difference
between Moody’s Aaa corporate bond yield and the short rate. In analysis that is not reported here, we have also experimented with other predictor variables. In particular, we have
examined the dividend yield for a value-weighted portfolio of NYSE, AMEX, and Nasdaq
stocks. We have also examined the default spread (i.e., the difference between Moody’s Baa
and Aaa corporate bond yields) and measures of aggregate stock market volatility (i.e., both
realized volatility and the VIX). Although these variables predict returns individually, their
incremental contribution is weak once we control for the short rate and the yield spread.
The second category consists of those variables that are motivated by the view that
commodity markets are segmented to some degree. The basis, or the ratio of futures to
spot price, emerges as a particularly important variable in these theories. In the theory
of backwardation, producers take a (long or short) position in commodity futures to hedge
their position in the underlying spot (Keynes, 1923; Hicks, 1939). Risk averse speculators
10
demand a risk premium for providing insurance. The excess demand for insurance, or hedging
pressure, drives a wedge between the futures price and the expected future spot price. In
the theory of storage, a low inventory causes the spot price to be temporarily high, which
subsequently mean reverts as supply-demand imbalances correct (e.g., Deaton and Laroque,
1992). Note that the theory of backwardation and storage have very different implications
for mean reversion in futures versus spot prices. The theory of backwardation implies that a
low basis predicts high returns on commodity futures, but it remains silent about spot-price
movements. In contrast, the theory of storage implies that a low basis predicts low spot-price
growth, but it remains silent about futures-price movements.
We construct aggregate basis in analogy to our construction of aggregate commodity
returns. We first compute the basis for each commodity i with maturity T − t as
Basisi,t,T =
Fi,t,T
Si,t
T 1−t
− 1.
(3)
While the Commodity Research Bureau has a reliable record of spot prices, a spot price
is not always available on the same trading day as a recorded futures price. In instances
where the spot price is missing, we first try to use an expiring futures contract to impute
the spot price. If an expiring futures contract is not available, we then use the last available
spot price within 30 days to compute the basis. For example, if we have a futures price on
December 31, but the last available spot price is from December 30, we compute the basis
as the ratio of the futures price on December 31 to the spot price on December 30.
We then compute the median of basis within each of eight portfolios, corresponding to
four sectors and two levels of maturity. We use the median, instead of the mean, because it
is less sensitive to outliers in the basis for individual futures contracts. Finally, we compute
aggregate basis as an equal-weighted average of the basis across the eight portfolios. Recall
that the basis is the implied net convenience yield derived from the cost-of-carry relation.
The net convenience yield is defined as the riskless interest rate, plus additional storage costs,
11
minus the convenience usage earned from owning the spot. Hence, aggregate basis identifies
the common variation in the net convenience yield across all commodities.
In addition to the basis, the theory of backwardation implies that a direct measure of
hedging pressure should be correlated with the basis and also predict returns on commodity
futures. We construct an aggregate version of hedging pressure as defined by de Roon,
Nijman, and Veld (2000). We first construct hedging pressure for each sector as the ratio
of two objects. The numerator is the dollar value of short minus long positions held by
noncommercial traders in the Commitments of Traders, summed across all commodities
in that sector. The denominator is the dollar value of short plus long positions held by
noncommercial traders, summed across all commodities in that sector. Finally, we compute
aggregate hedging pressure as an equal-weighted average of hedging pressure across the four
sectors.
3.
Summary Statistics of Commodity Markets
3.1.
Commodity Returns
Panel A of Table 2 reports the summary statistics for monthly excess returns over the 1month T-bill rate. The aggregate commodity portfolio has a mean of 0.58% and a standard
deviation of 4.05%. This corresponds to an annualized average excess return of 6.96% and
an annualized standard deviation of 14.03%. During the same period, the 10-year Treasury
bond has an annualized average excess return of 2.04% and an annualized standard deviation
of 8.00%. The CRSP value-weighted market portfolio for NYSE, AMEX, and Nasdaq stocks
has an annualized average excess return of 4.32% and an annualized standard deviation of
15.66%. The Sharpe ratio for the aggregate commodity portfolio was higher than that for
stocks in this sample period, which is emphasized by Gorton and Rouwenhorst (2006).
The table also reports the autocorrelation and the cross correlation of excess returns. The
first-order autocorrelation for aggregate commodity returns is 0.08, which is comparable to
12
that for bond and stock returns. Aggregate commodity returns have a correlation of −0.11
with bond returns and a correlation of 0.07 with stock returns. Because commodities have
a high Sharpe ratio and low correlation with bonds and stocks, it is an attractive asset class
from the perspective of diversifying the investment portfolio.
The table also reports the summary statistics for the four sector portfolios. Both the
mean and the standard deviation of excess returns have the same ordering across the four
sectors. Agriculture has the lowest average excess return at 0.27% per month and the lowest
standard deviation at 4.25% per month. Livestock has an average excess return of 0.47% per
month and a standard deviation at 4.87% per month. Metals have an average excess return
of 0.59% per month and a standard deviation of 7.40% per month. Energy has the highest
average excess return at 0.91% per month and the highest standard deviation at 8.08% per
month.
3.2.
Predictor Variables
Table 3 reports the summary statistics for the predictor variables. Aggregate basis has
a mean of 0.03% and a standard deviation of 0.84%. Its autocorrelation is 0.69, which
is lower than that for the short rate and the yield spread. This suggests that aggregate
basis is a predictor variable that operates at a higher frequency than the aggregate market
predictors. Aggregate basis has a positive correlation of 0.22 with the short rate and a
negative correlation of −0.11 with the yield spread. Recall that the net convenience yield
is defined as the riskless interest rate, plus additional storage costs, minus the convenience
usage earned from owning the spot. Because aggregate basis is the average net convenience
yield across commodities, the positive correlation between aggregate basis and the short rate
is unsurprising (Fama and French, 1988; Bailey and Chan, 1993).
Figure 3 shows aggregate basis together with the spot-price index for an equal-weighted
portfolio of commodities. The spot-price index is the cumulative growth rate of aggregate
spot-price growth, deflated by the consumer price index. Note that movements in the spot13
price index tend to be inversely related to movements in aggregate basis. When the spot-price
index rises, aggregate basis tends to fall. This is due to mean reversion in the spot price.
An increase in the spot price, due to a transitory demand shock for instance, will not lead
to a one-for-one movement in the futures price because the futures market anticipates mean
reversion.
Since 2003, there is a break in this relation between the spot price and the basis, which interestingly coincides with the period of high capital flow into commodity markets. Although
spot prices have appreciated considerably, aggregate basis has risen if anything. This implies
that futures prices have responded at least (if not more than) one-for-one with spot-price
movements. This striking movement in futures prices is unprecedented, even if we consider
the energy crisis of the 1970s. A potential explanation for the recent experience is that
investors believe that there were permanent, or highly persistent, shocks to the demand for
commodities. Another explanation is the conventional wisdom that more capital flowed into
commodity futures than into the spot market in this period, as commodity index investors
chased returns.3 This story provides additional motivation for us to study the relation between capital flow and returns in commodity markets.
Open-interest growth has a mean of 1.47% and a standard deviation of 2.07%. Its autocorrelation is 0.90, which arises from the fact that open-interest growth is smoothed as a
12-month moving average. Open-interest growth is essentially uncorrelated with aggregate
basis, but it has a positive correlation of 0.31 with hedging pressure. Unsurprisingly, 12month open-interest growth has a high contemporaneous correlation of 0.50 with 12-month
commodity returns. This imperfect correlation suggests that capital flow contains information that is not entirely reflected in commodity prices. In fact, Figure 1 shows that capital
flow into commodity markets tends to lead a subsequent rise in commodity prices.
3
Pindyck and Rotemberg (1990) find excess co-movement among commodity prices and conjecture that
this is due to speculators moving in and out of commodities as an asset class. In related work, Tan and Xiong
(2009) find that commodities have co-moved more with stocks in the recent period, which they attribute to
the indexation of commodity markets.
14
4.
What Does Commodity Market Capital Flow Predict?
4.1.
Predictability of Commodity Returns
Table 4 presents our main findings on the predictability of aggregate commodity returns
and spot-price growth. In column (1), we first examine the predictability of commodity
returns by the short rate, the yield spread, and aggregate basis. This specification allows us
to establish a benchmark to see the incremental forecasting power of open-interest growth,
which is our key variable of interest. All coefficients are standardized so that they can be
interpreted as the percent change in monthly expected returns per one standard deviation
change in the predictor variable.
The short rate enters with a coefficient of −0.47 and a t-statistic of −2.40. This means
that a standard deviation increase in the short rate decreases expected returns by 0.47% per
month. Hence, the short rate explains an economically important magnitude of predictability
in commodity returns. The fact that the short rate, or the inflation rate, predicts asset
returns with a negative coefficient is well known (Fama and Schwert, 1977). However, there
is no consensus on its economic interpretation.
In our view, a more interesting and novel finding is the coefficient for the yield spread.
The yield spread predicts commodity returns with a coefficient of −0.45 and a t-statistic of
−2.47. This means that a standard deviation increase in the yield spread decreases expected
returns by 0.45% per month. The fact that the yield spread predicts commodities with a negative coefficient is in sharp contrast to the positive coefficient for bonds and stocks, reported
in previous studies (Campbell, 1987; Fama and French, 1989). The usual interpretation for
bonds and stocks is that when the yield spread is high, which tends to coincide with recessions, the risk premia for all risky assets are high (due to high risk aversion or high quantity
of risk). In contrast, expected commodity returns are low, or alternatively commodity prices
are high, when the yield spread is high. This finding suggests that commodities are a good
15
hedge for time-varying investment opportunities in bond and stock markets.
Aggregate basis predicts commodity returns with a coefficient of −0.52 and a t-statistic of
−2.58. This means that a standard deviation increase in aggregate basis decreases expected
returns by 0.52% per month. The fact that low basis (i.e., a low futures price relative to
the spot price) predicts high returns on being long commodity futures is consistent with the
theory of backwardation. Overall, the R2 of the forecasting regression is 3.29%. While the
specification in column (1) follows earlier work and is not our main focus, we obtain much
stronger results than previously reported. The primary reasons are that we have access
to a longer sample period and that we use of a broader cross section of commodities in
constructing aggregate basis.
In column (2), we introduce open-interest growth to examine its incremental power for
predicting aggregate commodity returns. The coefficients for the other three predictor variables are virtually unchanged from column (1) because open-interest growth is essentially
uncorrelated with these other predictor variables. Open-interest growth enters with a coefficient of 0.64 and a t-statistic of 2.66. This means that a standard deviation increase in
open-interest growth increases expected returns by 0.59% per month. In this sample, openinterest growth explains a larger share of the variation in expected returns than the other
predictor variables. Importantly, the introduction of open-interest growth increases the R2
of the forecasting regression from 3.29% to 5.29%.
In columns (3) and (4) of Table 4, we examine whether the same four variables predict
aggregate spot-price growth. In column (3), we predict spot-price growth using only the
short rate, the yield spread, and aggregate basis. The short rate enters with a coefficient
of −0.68 and a t-statistic of −3.49. The yield spread enters with a coefficient of −0.40 and
a t-statistic of −2.22. These results are similar to those for aggregate commodity returns
in column (1). Aggregate basis enters with a coefficient of 0.37 and a t-statistic of 1.79.
Note that the sign of the coefficient is the opposite of that for aggregate commodity returns
in column (1). The fact that high basis predicts high spot-price growth is consistent with
16
the theory of storage. A standard deviation increase in aggregate basis increases expected
spot-price growth by 0.37% per month. The R2 of this forecasting regression is 2.50%, which
is comparable to that for aggregate commodity returns in column (1).
In column (4), we introduce open-interest growth to examine its incremental power for
predicting aggregate spot-price growth. Open-interest growth enters with a coefficient of
0.41 and a t-statistic of 1.91. Importantly, the inclusion of open-interest growth increases
the R2 of the forecasting regression from 2.50 to 3.16%. This is similar to our findings for
aggregate commodity returns.
In Table 5, we examine additional predictor variables that are potentially related to
open-interest growth. For the purposes of comparison, column (1) repeats our main specification from column (2) of Table 4. In column (2), we predict commodity returns with
past 12-month commodity returns instead of open-interest growth. Past returns enter with
a coefficient of 0.42 and a t-statistic of 1.90. This means that a standard deviation increase
in past returns increases expected returns by 0.42% per month, which is comparable to the
economic magnitude of open-interest growth in column (1). Our finding boils down to a
strong momentum effect in the time series of aggregate commodity returns. Our finding is
different from the well known momentum effect in stock and commodity returns, which is a
cross sectional phenomenon.
In column (3), we run a horse race between open-interest growth and past returns. We
find that open-interest growth crowds out past returns. Open-interest growth enters with
a coefficient of 0.59 and a t-statistic of 1.84, while past returns enter with a statistically
insignificant coefficient of 0.12. On the one hand, this result shows that the forecasting power
of open-interest growth is closely related to the momentum effect in aggregate commodity
returns. On the other hand, capital flow and past returns, while highly correlated, contain
different information about future commodity returns. As revealed by Figure 1, capital flow
tends to lead returns slightly and can therefore be more informative. Our finding is not only
novel for the commodity market literature, but it connects more generally to evidence for
17
slow information diffusion in asset markets as we discuss below.
In column (4), we predict commodity returns with hedging pressure instead of openinterest growth. Hedging pressure enters with a coefficient of 0.28 and a t-statistic of 1.50.
The sign of the coefficient is consistent with the theory of backwardation, which implies
that high hedging pressure should predict high returns. However, hedging pressure is only
marginally significant. In column (5), we run a horse race between open-interest growth
and hedging pressure. We find that open-interest growth entirely eliminates the forecasting
power of hedging pressure. Open-interest growth enters with a coefficient of 0.61 and a
t-statistic of 2.50, while hedging pressure enters with a coefficient of 0.11 and a t-statistic
of 0.56. Recall that the correlation between open-interest growth and hedging pressure is
0.31. Hence, a plausible interpretation of these results is that hedging pressure is just a noisy
proxy for open-interest growth.
We summarize our main findings in Tables 4 and 5 as follows. The fact that the short
rate and the yield spread predict changes in both futures and spot prices is consistent with
the asset allocation view of commodities, that commodity prices are partly driven by aggregate market predictors. The fact that aggregate basis predicts changes in both futures
and spot prices with opposite signs is consistent with the segmented markets view, that
the theory of backwardation and the theory of storage are partly responsible for commodity
price fluctuations. The results for hedging pressure lends further support to the theory of
backwardation.
More importantly, these tables demonstrate that our findings for open-interest growth are
unrelated to these traditional theories. First, the forecasting power of open-interest growth
is unaffected by these other known predictors based on these theories. Second, the fact that
open-interest growth predicts changes in both futures and spot prices with the same sign
suggests that our findings are unrelated to the theory of backwardation or hedging pressure in
futures markets. A more likely explanation for our findings is that capital flow in commodity
markets contains important information about commodity prices in general (e.g., about the
18
future supply or demand for commodities), instead of being a proxy for risk. As we discuss
in more detail below, the fact that open-interest growth is contemporaneously correlated
with changes in commodity prices and also forecasts future returns suggests the following
interpretation: open-interest growth contains information about fundamentals that is not
entirely impounded in commodity prices.
4.2.
Dissecting the Predictability of Commodity Returns
We consider a number of additional analyses to check the robustness of our findings and to
build towards an interpretation. In Table 6, we examine the predictability of commodity
returns by contract maturity and sector. We first examine the predictability of a portfolio
of short-maturity contracts separately from a portfolio of long-maturity contracts. Short
maturity refers to futures contracts with three months or less to maturity, and long maturity
refers to futures contracts with more than three months to maturity. The results for these
two portfolios are very similar to those for the aggregate commodity portfolio in Table 4.
The coefficient for the yield spread is −0.37 for short maturity and −0.45 for long maturity.
The coefficient for aggregate basis is −0.50 for short maturity and −0.47 for long maturity.
Finally, the coefficient for open-interest growth is 0.50 for short maturity and 0.79 for long
maturity, both of which are statistically significant. Overall, these results show that our
main findings are not driven by long-maturity contracts that are potentially less liquid than
short-maturity contracts.
We next examine whether the same aggregate variables that were used in Table 4 predict
returns on the four sector portfolios. For agriculture, the short rate enters with a coefficient
of −0.44 and a t-statistic of −1.75, similar to that obtained for the aggregate commodity
portfolio. The yield spread has a much weaker forecasting power for the agriculture portfolio,
compared to the aggregate commodity portfolio. The yield spread enters with a statistically
insignificant coefficient of −0.25. Aggregate basis has virtually no forecasting power for the
agriculture portfolio. The coefficient is only −0.09 with a t-statistic of −0.43. Relative to
19
these other predictor variables that have weak forecasting power, open-interest growth has
some forecasting power for the agriculture portfolio. Open-interest growth enters with a
coefficient of 0.44 and a t-statistic of 1.44.
We next examine energy, which is a sector of particular interest in light of our introduction. Note that energy contracts are available only since 1978, so the sample is shorter than
the other three sectors. The short rate enters with a statistically insignificant coefficient of
−0.34. However, the yield spread enters with a coefficient of −1.36 and a t-statistic of −2.52.
This coefficient is over three times the magnitude of that for the aggregate commodity portfolio. This implies that energy is responsible for much of the forecasting power of the yield
spread for the aggregate commodity portfolio. We also find that aggregate basis is a strong
predictor of returns on the energy portfolio. A high aggregate basis predicts low returns on
being long energy futures with a coefficient of −1.65 and a t-statistic of −2.06. The magnitude of this coefficient is larger than that for any other sector. Importantly, open-interest
growth is a strong predictor with a coefficient of 1.34 and a t-statistic of 2.50. Again, the
magnitude of this coefficient is larger than that for any other sector.
For livestock, neither the short rate or the yield spread are statistically significant. The
short rate enters with a coefficient of −0.17 and a t-statistic of −0.63. The yield spread
enters with a coefficient of 0.14 and a t-statistic of 0.49. However, aggregate basis is a strong
predictor of returns on the livestock portfolio with a coefficient of −0.88 and a t-statistic of
−3.30. Finally, open-interest growth enters with a coefficient of 0.52 and a t-statistic of 1.93.
The short rate has the highest forecasting power for metals. The short rate enters with
a coefficient of −1.04 and a t-statistic of −2.32. The magnitude of this coefficient is more
than two times that for any other sector. The yield spread also works quite well for metals,
although its forecasting power is less than that for energy. The yield spread enters with a
coefficient of −0.71 and a t-statistic of −1.84. Aggregate basis enters with a statistically
insignificant coefficient of −0.14. However, open-interest growth predicts returns on the
metal portfolio with a coefficient of 0.70 and a t-statistic of 1.55.
20
We now summarize our findings for the sector portfolios. Open-interest growth is the
most robust variable in the sense that it consistently predicts returns across all four sectors.
While open-interest growth is only marginally significant in agriculture and livestock, the
magnitude of the coefficients is economically important across all four sectors. In contrast,
the forecasting power of the other three predictor variables is confined to particular sectors.
The short rate works best for metals and to some extent for agriculture. The yield spread
works best for energy and to a lesser extent for metals. Aggregate basis has the highest
forecasting power for energy and livestock and virtually no power for agriculture and metals.
These patterns for aggregate basis can be interpreted in light of the theory of backwardation.
For example, fluctuations in the convenience yield are apparently more important drivers of
expected returns in energy than they are for metals.
In the first two columns of Table 7, we examine the predictability of aggregate commodity
returns by subsample. We split our sample into two halves, 1965–1986 and 1987–2008. The
short rate, the yield spread, and aggregate basis have virtually no forecasting power for
aggregate commodity returns in the second half of the sample. For example, the short rate
enters with a coefficient of −0.90 and a t-statistic of −2.98 in the first half, while it enters
with a coefficient of 0.07 and a t-statistic of 0.17 in the second half. The yield spread is
not statistically significant in either subsample. Its coefficient of −0.35 in the first half is
larger than its coefficient of −0.20 in the second half. Aggregate basis enters with the same
coefficient of −0.40 in both subsamples. However, it is not statistically significant in either
subsample. Overall, our results for the 1965–1986 sample are consistent with the classic
literature on commodities that did not find a strong role for the yield spread or the basis
(Fama and French, 1987; Bessembinder and Chan, 1992).
In contrast to these other predictor variables, open-interest growth has consistent forecasting power in both subsamples. Open-interest growth enters with a coefficient of 0.69
and a t-statistic of 1.92 for the first half, while it enters with a coefficient of 0.73 and a
t-statistic of 2.60 for the second half. These results show that our findings for open-interest
21
growth are not driven by a particular period in the sample, nor any peculiarity related to
our hand-collected data prior to 1986.
In the last two columns of Table 7, we examine the predictability of aggregate spot-price
growth by subsample. The forecasting power of the short rate and the yield spread again
comes mostly from the first half of the sample. The short rate enters with a coefficient of
−0.93 and a t-statistic of −3.38 in the first half, while the coefficient is nearly zero in the
second half. The yield spread enters with a coefficient of −0.28 in the first half, which is
larger than the coefficient of −0.16 in the second half. Moreover, the yield spread is not
statistically significant in either subsample. Aggregate basis retains its forecasting power in
the second half of the sample, which can be interpreted as evidence for the theory of storage.
Its coefficient is 0.81 with a t-statistic of 1.78. Open-interest growth predicts spot-price
growth somewhat better in the second half of the sample compared to the first half. Its
coefficient is 0.34 with a t-statistic of 1.24 in the first half, while its coefficient is 0.77 with
a t-statistic of 2.06 in the second half.
Having established that open-interest growth is a robust predictor of commodity returns,
we next examine whether it predicts the volatility of commodity prices. The motivation
for this analysis is that open-interest growth may be proxying for an omitted risk factor
that is not captured by the yield spread, aggregate basis, or hedging pressure. In Table 8,
we regress the monthly volatility of spot-price growth onto lagged predictor variables that
include open-interest growth. As shown in column (1), open-interest growth enters with
a statistically insignificant coefficient of −0.10. In column (2), we include past 12-month
returns as an additional predictor variable. Interestingly, high 12-month returns predicts the
volatility of spot-price growth with a coefficient of 0.64 and a t-statistic of 3.93. This means
that a standard deviation increase in 12-month returns increases the standard deviation of
spot-price growth by 0.64% per month. In contrast, open-interest growth enters with a
coefficient of −0.40 and a t-statistic of −2.47. These results show that open-interest growth
does not seem to be related to the volatility of commodity prices in a way that is consistent
22
with a risk story.
4.3.
Predictability of Inflation News and Bond Returns
Having established the robustness of our main results, we now focus on building an economic
interpretation of our findings. A natural interpretation is that capital flow into commodity
markets contain information about fundamentals that is not fully incorporated into commodity prices. This view of delayed reaction to fundamental news has some anecdotal
support. For example, the 1970s experienced supply shocks to oil, which eventually led to
high inflation. The most recent period starting around 2003 experienced strong demand for
commodities from emerging economies like China and India, which again led to worries about
inflationary pressures. This episode ended abruptly in 2008 with the onset of the financial
crisis. As highlighted by Figure 1, each of these periods with inflation worries experienced
high capital flow, followed immediately by a run-up of commodity prices.
In Table 9, we examine whether open-interest growth does indeed contain information
about inflation in several ways. In column (1), we test whether open-interest growth predicts
the monthly change in the annual inflation rate. We regress the change in inflation onto
its own lag as well as 1-month lags of the yield spread and open-interest growth. This
specification assumes that inflation is integrated of order one, so that we do not include
the level of inflation or the short rate in the regression (see Stock and Watson, 1999, for a
similar specification). Open-interest growth enters with a coefficient of 0.05 and a t-statistic
of 2.99, consistent with our intuition that it contains fundamental news about inflation. In
column (2), we add past 12-month commodity returns to see which of the two variables has
more forecasting power for inflation. We find that past 12-month returns is a more powerful
predictor of inflation. It enters with a coefficient of 0.07 and a t-statistic of 3.10. The
coefficient for open-interest growth drops from 0.05 to 0.02, and the t-statistic is now 1.16.
Our findings are consistent with the known result in the macroeconomic forecasting literature
that commodity prices are valuable predictors of inflation in the near term. This is not
23
surprising since commodity prices feed almost mechanically into the consumer price index.
Nevertheless, it is interesting to note that open-interest growth retains some forecasting
power for inflation. The results in columns (1) and (2) strengthen our intuition that both
open-interest growth and past commodity returns contain news about inflation.
In column (3), we examine whether open-interest growth predicts inflation expectations
as opposed to realized inflation. In particular, we examine the predictability of changes in
the short rate, which can be roughly interpreted as changes in expected monthly inflation
by the Fisher hypothesis. The yield spread enters with a coefficient of 0.07 and a t-statistic
of 1.58. Open-interest growth enters with a coefficient of 0.12 and a t-statistic of 3.22. This
means that a standard deviation increase in open-interest growth implies an increase in the
annualized short rate by 0.12%. In column (4), we add 12-month commodity returns to find
that it also predicts changes in the short rate. However, it does not drive out open-interest
growth. Our findings here suggest that open-interest growth has incremental forecasting
power for inflation expectations beyond commodity prices. This finding, as we discuss in the
conclusion, has potentially important implications for the large industry of macroeconomic
forecasting.
In column (5), we predict excess returns on the 10-year Treasury bond over the 1-month
T-bill rate. The idea here is that long-term bond prices are sensitive to news about inflation
worries over the longer term. The short rate enters with a statistically insignificant coefficient
of 0.14. The yield spread enters with a coefficient of 0.38 and a t-statistic of 2.63. Openinterest growth enters with a coefficient of −0.32 and a t-statistic of −3.17. This means that
a standard deviation increase in open-interest growth decreases expected bond returns by
0.32% per month. The forecasting power of open-interest growth rivals that of previously
known predictors such as the short rate and yield spread. In results not reported here, we
have also tested the predictability of bond returns by subsample. We find that in the recent
sample since 1987, only open-interest growth has any forecasting power for bond returns.
Therefore, our findings have potentially important implications for the large literature on
24
the predictability of bond returns. Column (6) shows that past 12-month returns do not
contain any forecasting power beyond open-interest growth. This finding suggests that openinterest growth may be a more powerful predictor of inflation over long horizons than past
commodity prices. Overall, Table 9 unanimously supports our intuition that the forecasting
power of open-interest growth is coming from information about future inflation above and
beyond that contained in commodity prices.
In light of the ability of open-interest growth to forecast inflation news and expectation,
our findings are most consistent with recent theories examining trading activity and price
momentum, identified in the stock market by Jegadeesh and Titman (1993). This literature
has developed very quickly over the last decade, and there are many studies that have
greatly improved out understanding of the relation between trading and momentum (see
Hong and Stein, 2007, for a survey). Hong and Stein (2007) describe two mechanisms in
the literature in which high trading activity and high past returns can predict high future
returns. One mechanism is gradual information diffusion. To the extent information diffuses
only gradually because of segmentation and limits to arbitrage, an increase in open interest
reflects good news about commodities (perhaps due to inflation worries), which only gets
gradually incorporated. There is substantial support for this view from many studies of price
momentum in the stock market. Another mechanism is serial correlation in trading activity,
perhaps because of passive feedback traders. Price increases lead to positive feedback trading,
which lead to even higher prices. Alternatively, there is time variation in the arrival of news.
Periods of higher intensity trigger disagreement and trading, which lead to higher prices in
the presence of short-sales constraints. There is empirical support for both mechanisms in
stock market studies.
In the context of commodity markets, both mechanisms are believable to the extent that
these markets have traditionally been somewhat segmented from other asset markets. The
commodity price run-ups triggered perhaps initially by fundamental demand from the emerging economies could also led to over-trading and excessive valuations. However, the finding
25
that open-interest growth also predicts bond returns suggests that gradual information diffusion is the more likely mechanism. Indeed, there is evidence for gradual information diffusion
in the cross section of stock returns (Menzly and Ozbas, 2006; Hong, Torous, and Valkanov,
2007; Cohen and Frazzini, 2008). These studies find that when a particular firm or industry
has positive stock returns over some period, customers and suppliers of that firm or industry
tend to experience positive stock returns over a subsequent period. Gervais, Kaniel, and
Mingelgrin (2001) identify an intriguing effect in the cross section of stock returns, where
abnormal volume predicts high returns over the subsequent week. No such pattern exists
for the aggregate stock market. They attribute their finding to serial correlation in trading
activity. In comparison to these studies for the stock market, our finding that capital flow
predicts commodity returns is striking because it operates at a much lower frequency.
This related literature establishes that trading activity will serve as an important predictor of returns in addition to past price movements. Our findings are somewhat more stark
in that capital flow drives out past price movements as a predictor of future returns. However, we do not want to over-emphasize this result since it may be an in-sample outcome.
Moreover, at least for forecasting realized monthly inflation, past commodity prices appear
to perform better than capital flow. One can think of reasons unique to commodity markets
why capital flow predicts returns better than past prices. For example, prices may not move
much initially because of excess production capacity, in which case capital flow may be more
informative. More work remains to fully flesh out whether and why capital flow is more
informative than prices in commodity markets.
5.
Conclusion
Using hand-collected data from Commitments of Traders reports since 1965, we establish a
relation between capital flow and returns in commodity markets. Specifically, we find that
high capital flow into commodity markets, as measured by aggregate open interest in futures
26
markets, predicts high commodity returns and low bond returns. As part of our analysis, we
update and extend the literature on the predictability of commodity returns. Our finding
is robust to controlling for aggregate market predictors such as the short rate and the yield
spread. It is also robust to controlling for commodity market predictors such as the basis, past
commodity returns, and hedging pressure. Our findings seem most consistent with theories
of gradual information diffusion, which suggest that when market prices under-react to news,
trading activity emerges as a useful additional predictor of future returns.
A broader implication of our findings is that capital flow in commodity markets may
be useful for predicting macroeconomic quantities, such as inflation and the term structure
of interest rates. The macroeconomic literature on inflation forecasting has already known
for some time that commodity and bond prices are important inputs in forecasting models.
These models generally assume that commodity and bond prices contain timely information
about inflation expectations. However, we find that commodity and bond prices seem to
initially under-react to inflation news and that capital flow into commodity markets contain
additional forecasting power. Our work points to a new direction for forecasting models of
inflation and the term structure, where capital flow may be fruitfully incorporated to improve
forecasting power.
27
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31
Table 1: List of Commodities in the Portfolio
Our portfolio includes 30 commodities for which futures and spot prices are available through
the Commodity Research Bureau. The futures contracts are traded on the Chicago Board
of Trade (CBOT), the Chicago Mercantile Exchange (CME), the Intercontinental Exchange
(ICE), and the New York Mercantile Exchange (NYMEX). The sample period starts in
December 1964, after which prices are available for many commodities.
Sector
Commodity
Exchange
Agriculture
Butter
Cocoa
Coffee
Corn
Cotton
Lumber
Oats
Orange Juice
Rough Rice
Soybean Meal
Soybean Oil
Soybeans
Sugar
Wheat
Crude Oil
Gasoline
Heating Oil
Natural Gas
Propane
Broilers
Feeder Cattle
Lean Hogs
Live Cattle
Pork Bellies
Aluminum
Copper
Gold
Palladium
Platinum
Silver
CME
ICE
ICE
CBOT
ICE
CME
CBOT
ICE
CBOT
CBOT
CBOT
CBOT
ICE
CBOT
NYMEX
NYMEX
NYMEX
NYMEX
NYMEX
CME
CME
CME
CME
CME
NYMEX
NYMEX
NYMEX
NYMEX
NYMEX
NYMEX
Energy
Livestock
Metals
First Observation
Futures Price Commitments of Traders
September 1996
May 1997
December 1964
July 1978
August 1972
July 1978
December 1964
December 1964
December 1964
December 1964
March 1970
July 1978
December 1964
December 1964
May 1967
January 1969
August 1986
October 1986
December 1964
December 1964
December 1964
December 1964
December 1964
December 1964
December 1964
July 1978
December 1964
December 1964
March 1983
April 1983
December 1984
December 1984
November 1978
October 1980
April 1990
April 1990
August 1987
August 1987
February 1991
March 1991
March 1972
December 1975
February 1966
July 1968
December 1964
July 1968
December 1964
July 1968
December 1983
January 1984
December 1964
December 1982
December 1974
December 1982
January 1977
July 1978
March 1968
July 1978
December 1964
December 1982
32
33
Mean
(%)
Standard
AutoCorrelation with
Deviation correlation Commodity Agriculture Energy Livestock Metals 10-Year
(%)
Portfolio
Bond
Panel A: Commodity, Bond, and Stock Returns
Commodity portfolio
0.58
4.05
0.08
Agriculture
0.27
4.25
0.02
0.64
Energy
0.91
8.08
0.14
0.69
0.06
Livestock
0.47
4.87
0.03
0.56
0.35
0.03
Metals
0.59
7.40
0.06
0.75
0.36
0.15
0.17
10-year bond
0.17
2.31
0.08
-0.11
-0.10
-0.04
-0.07
-0.09
Stock portfolio
0.36
4.52
0.09
0.07
0.04
0.01
0.04
0.12
0.18
Panel B: Spot-Price Growth
Commodity portfolio
0.32
4.00
0.06
Agriculture
0.16
4.66
0.00
0.50
Energy
0.72
11.33
0.04
0.76
-0.03
Livestock
0.27
6.84
-0.05
0.58
0.18
0.03
Metals
0.19
5.04
0.09
0.51
0.23
0.14
0.06
Variable
Table 2: Summary Statistics for Commodity Returns and Spot-Price Growth
Panel A reports the mean and the standard deviation of monthly excess returns over the 1-month T-bill rate. It also reports the
autocorrelation and the pairwise correlation of excess returns. The aggregate portfolio of fully collateralized commodity futures
is equal-weighted across agriculture, energy, livestock, and metals. The other assets are the 10-year Treasury bond and the
CRSP value-weighted stock portfolio. Panel B reports the same statistics for monthly spot-price growth. The sample period is
1965:1–2008:12 (1978:12–2008:12 for energy only).
34
Mean
(%)
5.49
2.60
0.03
1.47
1.03
17.68
Variable
Short rate
Yield spread
Aggregate basis
Open-interest growth
12-month returns
Hedging pressure
Standard
AutoCorrelation with
Deviation correlation Short
Yield Aggregate Open-Interest 12-Month
(%)
Rate Spread
Basis
Growth
Returns
2.69
0.97
1.61
0.93 -0.52
0.84
0.69
0.22
-0.11
2.07
0.90
0.02
-0.10
-0.06
1.24
0.94
0.07
-0.37
0.06
0.50
14.02
0.90
0.27
-0.29
0.16
0.31
0.44
Table 3: Summary Statistics for the Predictor Variables
The table reports the mean, the standard deviation, the autocorrelation, and the pairwise correlation of the predictor variables.
The short rate is the monthly average yield on the 1-month T-bill. The yield spread is the difference between Moody’s Aaa
corporate bond yield and the short rate. Aggregate basis is equal-weighted across agriculture, energy, livestock, and metals.
The next predictor variables are the 12-month geometric average of open-interest growth and the 12-month geometric average
of commodity returns. Hedging pressure is the ratio of short minus long positions relative to short plus long positions held by
noncommercial traders in the Commitments of Traders. The sample period is 1965:1–2008:12.
Table 4: Predictability of Commodity Returns
We test the predictability of returns on an aggregate portfolio of fully collateralized commodity futures and aggregate spot-price growth. We regress monthly excess returns, over
the 1-month T-bill rate, onto 1-month lags of the short rate, the yield spread, aggregate basis, and 12-month open-interest growth. The table reports point estimates for standardized
regressors with heteroskedasticity-consistent t-statistics in parentheses. The sample period
is 1965:1–2008:12.
Predictor Variable
Short rate
Yield spread
Aggregate basis
Commodity
(1)
-0.47
(-2.40)
-0.45
(-2.47)
-0.52
(-2.58)
Open-interest growth
R2 (%)
3.29
35
Return Spot-Price Growth
(2)
(3)
(4)
-0.48
-0.68
-0.70
(-2.04) (-3.49)
(-3.04)
-0.41
-0.40
-0.38
(-1.95) (-2.22)
(-1.85)
-0.48
0.37
0.37
(-2.17) (1.79)
(1.61)
0.64
0.41
(2.66)
(1.92)
5.29
2.50
3.16
Table 5: Alternative Predictors of Commodity Returns
We test the predictability of returns on an aggregate portfolio of fully collateralized commodity futures. We regress monthly excess returns, over the 1-month T-bill rate, onto 1-month
lags of the short rate, the yield spread, aggregate basis, 12-month open-interest growth,
12-month commodity returns, and hedging pressure. The table reports point estimates for
standardized regressors with heteroskedasticity-consistent t-statistics in parentheses. The
sample period is 1965:1–2008:12.
Predictor Variable
Short rate
(1)
(2)
(3)
(4)
(5)
-0.48
-0.39
-0.46
-0.53
-0.50
(-2.04) (-1.96) (-1.99) (-2.49) (-2.16)
Yield spread
-0.41
-0.26
-0.36
-0.42
-0.39
(-1.95) (-1.45) (-1.89) (-2.13) (-1.85)
Aggregate basis
-0.48
-0.53
-0.49
-0.55
-0.49
(-2.17) (-2.37) (-2.27) (-2.61) (-2.19)
Open-interest growth
0.64
0.59
0.61
(2.66)
(1.84)
(2.50)
12-month returns
0.42
0.12
(1.90) (0.38)
Hedging pressure
0.28
0.11
(1.50) (0.56)
R2 (%)
5.29
3.65
5.34
3.76
5.35
36
Table 6: Predictability of Commodity Returns by Maturity and Sector
We test the predictability of returns on portfolios of fully collateralized commodity futures,
separately by maturity (greater than three months for long maturity) and sector. We regress
monthly excess returns, over the 1-month T-bill rate, onto 1-month lags of the short rate,
the yield spread, aggregate basis, and 12-month open-interest growth. The table reports
point estimates for standardized regressors with heteroskedasticity-consistent t-statistics in
parentheses. The sample period is 1965:1–2008:12 (1978:12–2008:12 for energy only).
Predictor Variable
Short
Long Agriculture Energy Livestock Metals
Maturity Maturity
Short rate
-0.48
-0.48
-0.44
-0.34
-0.17
-1.04
(-2.03)
(-1.88)
(-1.75) (-0.78)
(-0.63) (-2.32)
Yield spread
-0.37
-0.45
-0.25
-1.36
0.14
-0.71
(-1.74)
(-1.96)
(-1.07) (-2.52)
(0.49) (-1.84)
Aggregate basis
-0.50
-0.47
-0.09
-1.65
-0.88
-0.14
(-2.41)
(-1.77)
(-0.43) (-2.06)
(-3.30) (-0.33)
Open-interest growth
0.50
0.79
0.34
1.34
0.52
0.70
(2.34)
(2.49)
(1.44)
(2.50)
(1.93) (1.55)
R2 (%)
4.38
4.99
1.45
5.81
4.96
2.37
37
Table 7: Predictability of Commodity Returns by Subsample
We test the predictability of returns on an aggregate portfolio of fully collateralized commodity futures and aggregate spot-price growth, separately by subsample. We regress monthly
excess returns, over the 1-month T-bill rate, onto 1-month lags of the short rate, the yield
spread, aggregate basis, and 12-month open-interest growth. The table reports point estimates for standardized regressors with heteroskedasticity-consistent t-statistics in parentheses.
Predictor Variable
Commodity Return
Spot-Price Growth
1965–1986 1987–2008 1965–1986 1987–2008
Short rate
-0.90
0.07
-0.93
-0.05
(-2.98)
(0.17)
(-3.38)
(-0.09)
Yield spread
-0.35
-0.20
-0.28
-0.16
(-1.01)
(-0.75)
(-0.95)
(-0.43)
Aggregate basis
-0.40
-0.40
0.34
0.81
(-1.40)
(-1.14)
(1.26)
(1.78)
Open-interest growth
0.69
0.73
0.34
0.77
(1.92)
(2.60)
(1.24)
(2.06)
2
R (%)
6.49
5.72
4.51
3.60
38
Table 8: Predictability of Spot-Price Volatility
We test the predictability of spot-price volatility, computed as the standard deviation of
daily spot-price growth within each month. We regress spot-price volatility onto 1-month
lags of the short rate, the yield spread, aggregate basis, 12-month open-interest growth, and
12-month commodity returns. The table reports point estimates for standardized regressors
with heteroskedasticity-consistent t-statistics in parentheses. The sample period is 1965:1–
2008:12.
Predictor Variable
Short rate
(1)
(2)
-0.48
-0.38
(-3.31) (-2.63)
Yield spread
-0.04
0.22
(-0.26) (1.45)
Aggregate basis
0.33
0.29
(2.64) (2.36)
Open-interest growth
-0.10
-0.40
(-0.76) (-2.47)
12-month returns
0.64
(3.93)
R2 (%)
3.96
8.21
39
Table 9: Predictability of Inflation News and Bond Returns
We test the predictability of changes in inflation, computed as the 12-month growth rate of
the consumer price index, and changes in the short rate. We also test the predictability of
excess returns on the 10-year Treasury bond over the 1-month T-bill rate. We regress the
dependent variable onto 1-month lags of the short rate, the yield spread, 12-month openinterest growth, and 12-month commodity returns. The table reports point estimates for
standardized regressors with heteroskedasticity-consistent t-statistics in parentheses. The
sample period is 1965:1–2008:12.
Predictor Variable
Change in Inflation
(1)
(2)
Change in Short Rate
(3)
(4)
Short rate
Yield spread
Open-interest growth
-0.06
(-2.84)
0.05
(2.99)
12-month returns
Lagged dependent variable
R2 (%)
0.11
(5.06)
16.05
-0.04
0.07
(-1.73) (1.58)
0.02
0.12
(1.16) (3.22)
0.07
(3.10)
0.10
-0.01
(4.19) (-0.06)
18.30
4.04
40
0.09
(2.15)
0.08
(1.72)
0.08
(1.57)
-0.01
(-0.06)
4.96
Bond Return
(5)
(6)
0.14
0.13
(0.79) (0.72)
0.38
0.35
(2.63) (2.34)
-0.32
-0.28
(-3.17) (-2.41)
-0.08
(-0.59)
4.27
4.36
6
8
Return
−6
−2
0
2
4
Return (%, 12−mo. avg.)
Open−interest growth (%, 12−mo. avg.)
−4
−2
0
2
4
6
Open−interest growth
1964
1969
1974
1979
1984 1989
Year
1994
1999
2004
2009
Figure 1: Aggregate Open-Interest Growth and 12-Month Average Returns
The figure shows the 12-month geometric average of aggregate open-interest growth. It also
shows the 12-month geometric average of returns on an aggregate portfolio of commodities, equal-weighted across agriculture, livestock, energy, and metals. The sample period is
1965:1–2008:12.
41
1
0
.2
.4
.6
.8
Agriculture
Energy
Livestock
Metals
1964
1969
1974
1979
1984
1989
Year
1994
1999
2004
2009
Figure 2: Open Interest in Commodity Futures by Sector
The figure shows the share of dollar open interest in commodity futures that each sector
represents. The sample period is 1965:1–2008:12.
42
2.4
8
Spot price
0
−4
.4
−2
0
Basis (%)
2
4
.8
1.2
1.6
2
Spot price (log, CPI deflated)
6
Basis
1964
1969
1974
1979
1984 1989
Year
1994
1999
2004
2009
Figure 3: Aggregate Basis and the Spot-Price Index
The figure shows the basis and the spot-price index for an aggregate portfolio of commodities,
equal-weighted across agriculture, livestock, energy, and metals. We define basis for each
futures contract as (Fi,t,T /Si,t )1/(T −t) − 1. We then compute the median of basis within
each of four sectors and two maturity levels (greater than three months for long maturity).
Aggregate basis is an equal-weighted average of basis across all four sectors and two maturity
levels. The spot-price index is deflated by the consumer price index. The sample period is
1965:1–2008:12.
43