The Case for Long-Short Commodity Investing
Joëlle Miffre and Adrian Fernandez-Perez
JAI 2015, 18 (1) 92-104
doi: https://doi.org/10.3905/jai.2015.18.1.092
http://jai.iijournals.com/content/18/1/92
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The Case for Long-Short
Commodity Investing
JOËLLE MIFFRE AND ADRIAN FERNANDEZ-PEREZ
JOËLLE M IFFRE
is a professor of finance
at the EDHEC Business
School in Nice, France.
Joelle.Miffre@edhec.edu
A DRIAN
FERNANDEZ-P EREZ
is a research fellow at
Auckland University of
Technology in Auckland,
New Zealand.
adrian.fernandez@aut.ac.nz
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A
cademic research has made it
clear that commodity strategies
that systematically buy backwardated contracts and sell contangoed contracts outperform long-only
commodity indexes on a risk-adjusted basis.1
Such long-short commodity portfolios use
the slope of the term structure of commodity
futures prices, the positions of hedgers and
speculators, or inventory levels as signals
for asset allocation past performance. The
contribution of this paper is with regard to
the conditional volatility of these long-short
commodity portfolios and their conditional
correlations with traditional assets.
It is well known indeed that the strategic decision to include long-short commodity futures positions in a well-diversified
portfolio of stocks and bonds does not solely
depend on the risk premium. It is also driven
by a desire for risk diversification, and thus
depends on how the long-short commodity
returns correlate with the rest of the investor’s portfolio over time. The purpose of this
paper is precisely to analyze the conditional
volatility of long-short commodity portfolios and the conditional correlations of their
returns with those of traditional assets over
time and in periods of high volatility in traditional asset markets. By so doing, the paper
extends the literature that focused on the
diversification benefits of long-only commodity indexes (Bodie and Rosansky [1980],
Erb and Harvey [2006], Gorton and Rouwenhorst [2006], Büyük şahin et al. [2010],
Daskalaki and Skiadopoulos [2011]).
We present five reasons why traditional
asset managers who contemplate commodity
investments should opt for long-short (as
opposed to long-only) positions in commodity futures markets. First, in line with
previous research, long-short portfolios based
on momentum, term structure, or hedging
pressure (Mom, TS, or HP hereafter) are found
to offer better performance than their longonly counterparts. For example, over the
period 1992–2011, 96.32% of the long-short
commodity portfolios generate Sharpe ratios
that exceed that of the S&P-GSCI. Second,
the conditional volatility of long-short commodity portfolios is found to be less than that
of long-only portfolios. Third, in periods of
turmoil in equity and fixed income markets,
as contagion spreads across markets the volatility of long-short commodity portfolios is
found to rise less than that of long-only commodity portfolios.
Fourth, the conditional correlations of
the S&P 500 Index with long-short commodity portfolios (at −0.01 on average) are
found to be lower than those measured relative to long-only commodity indexes (at
0.16), suggesting that the risk diversification
benefits of commodity futures are stronger
within long-short portfolios. This is particularly true following the debacle of Lehman
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Brothers in September 2008, which led to the spread of
contagion across markets and thus to a rise to 0.44 in
the conditional correlations between long-only commodity portfolios and the S&P 500 Index (Tang and
Xiong [2012], Büyük ahin and Robe [2013]). The conditional correlations between equities and long-short
commodity portfolios, however, have remained low (at
0.05 on average).
Fifth, in periods of high volatility in equity markets, the conditional correlations between the S&P 500
Index and long-short hedging pressure portfolios is
found to decrease. This is good news to equity investors,
as it is precisely when the volatility of equity markets is
high (e.g., following the demise of Lehman Brothers)
that the benefits of diversification are most appreciated.
In contrast, the conditional correlation between longonly commodity indexes and the S&P 500 substantially
rises with the volatility of the S&P 500, suggesting that
the risk reduction that comes from diversification prevails less when needed most, namely, during market
downturn and periods of contagion.
Next we present the dataset, the methodologies
employed to capture the returns and conditional volatility of long-short commodity portfolios, and their
conditional correlations with traditional assets. The
following sections discuss the results and conclude the
paper.
DATA
The dataset includes Friday settlement prices for 27
commodity futures obtained from Datastream. The cross
section is chosen based on the availability of hedgers’ and
speculators’ positions in the Aggregated Commitment
of Traders (COT) report at the Commodity Futures
Trading Commission. It includes 12 agricultural commodities (cocoa, coffee C, corn, cotton n°2, frozen concentrated orange juice, oats, rough rice, soybean meal,
soybean oil, soybeans, sugar n°11, wheat), 5 energy
commodities (blendstock RBOB gasoline, electricity,
heating oil n°2, light sweet crude oil, natural gas), 4
livestock commodities (feeder cattle, frozen pork bellies,
lean hogs, live cattle), 5 metal commodities (copper,
gold, palladium, platinum, silver) and random-length
lumber.
Two starting dates are considered. The first is
January 5, 1979, which marks the first date over which
settlement prices are reported in Datastream International.
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The second is October 2, 1992, which marks the first
weekly reporting of the positions of hedgers and speculators in the COT report. Both samples end March
25, 2011. As proxies for traditional assets, we use the
S&P 500 Composite Index and Barclays Capital U.S.
Aggregate Bond Index.
Futures returns are calculated by assuming that
investors hold the nearest contracts up to one month
before maturity and then roll their positions to the second-nearest contracts. Alternative rolling techniques
could have been considered (e.g., holding distant contracts up to one month before they mature), but we see
three reasons why these approaches are not necessarily
optimal in the present context. First, as the forward
curve is typically steeper at the front end and f lattens
as maturity rises, roll-yields are likely to be more substantial front-end, thereby enhancing the performance
of the strategy considered. Second, distant contracts are
known to be less liquid and thus more expensive to
trade, which might adversely impact net performance.
Third, momentum profits have been shown to be less
significant in both economic and statistical terms when
distant contracts are held (Miffre and Rallis [2007]).
For these reasons, we opt for front or second-nearest
contracts and not for distant contracts.
We also download the long and short positions of
large commercial and non commercial traders from the
COT report and calculate two measures, called hedging
pressure, one for the hedgers and one for the speculators.2 The hedging pressure of, say, speculators is calculated as the number of long positions divided by the total
number of positions taken by non commercial traders.
For example, a hedging pressure of 0.2 for hedgers means
that 20% of hedgers are long and thus 80% are short, a
sign of a backwardated market. The hedging pressure
measures thus defined are used as signals for sorting
commodity futures into portfolios as explained next.
METHODOLOGY
Long-Short Mom, TS, and HP Portfolios
All long-short strategies first and foremost shortlist the 90% most-liquid contracts at the time of portfolio formation, where liquidity is measured as average
dollar open interest over the former R weeks. This is
to ease taking and liquidating positions and thus to
limit transaction costs. The Mom portfolios then consist
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of long (short) positions in the 20% futures with best
(worst) performance over the previous R weeks. The
positions are held over the next H weeks, when a new
Mom portfolio is formed (Erb and Harvey [2006], Miffre
and Rallis [2007]). R and H stand for the ranking and
holding periods of the portfolios expressed in weeks.
The TS portfolios consist of long (short) positions
in the 20% futures with highest (lowest) average rollyields over the previous R weeks, where roll-yields are
measured as the difference in the logs of the prices of
nearest and second-nearest contracts. The positions are
held for H weeks, when a new TS portfolio is formed
(Erb and Harvey [2006], Gorton and Rouwenhorst
[2006]).
The HP portfolio based on the positions of hedgers
consists of long positions in the 20% backwardated
futures for which hedgers are the shortest over the previous R weeks, and short positions in the 20% contangoed futures for which hedgers are the longest over the
previous R weeks. The positions are then held over the
next H weeks, when a new long-short hedging pressure portfolio is formed. The HP portfolio based on
the positions of speculators consists of long positions in
the 20% backwardated futures for which speculators are
the longest, and short positions in the 20% contangoed
futures for which speculators are the shortest. For more
on long-short HP portfolios, please refer to Basu and
Miffre [2013].3
To sum up, the long-short strategies differ with
regard to one characteristic only: the sorting criterion
used for asset allocation. In all other aspects, the strategies follow the same following three principles. First,
the ranking period (R) over which the sorting criterion is averaged out and the holding period (H) over
which the long-short portfolios are held are always set
to either 4, 13, 26, or 52 weeks. So we end up with 16
long-short portfolios for each of the strategies mentioned
above, where these 16 series come from the permutation
of the four ranking and four holding periods. Second,
in line with a long-standing literature (e.g., Bodie and
Rosansky [1980] for long-only portfolios or Miffre and
Rallis [2007] for long-short portfolios), the constituents
of the long and short portfolios are equally weighted.
Third, to facilitate comparison with the fully collateralized S&P-GSCI, the long-short portfolios are also
assumed to be fully collateralized, meaning that their
performance equals half that of the longs minus half
that of the shorts.
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Modeling Conditional Volatility
and Conditional Correlation
We model conditional volatilit y via the
generalized autoregressive conditional heteroskedasticity
GARCH(1,1) model of Bollerslev [1986]. The
GARCH(1,1) variance, ht , is as follows
Rt
μ + εt
ht = γ + αεt2−1 + βht −1
(1)
Rt is the time t return of a given portfolio, εt are residuals
distributed as N(0, ht ); μ is the mean return of Rt ; α, β
and γ are such that γ > 0, α ≥ 0, β ≥ 0, and α + β < 1.
We model the return co movements between
commodities C and traditional assets T via the dynamic
conditional correlation (DCC) model of Engle [2002].4
DCC time-varying correlations are estimated in two
steps. The first step estimates time-varying variances
as GARCH(1,1) processes and the second step models a
time-varying correlation matrix using the standardized
residuals from the first-stage estimation. More specifically, the covariance matrix is expressed as Ht ≡ DtRtDt ,
where Dt = g(√
√ hC , , √ hT ,t ) is a diagonal matrix of uni−
variate GARCH(1,1) volatilities and Rt = Qt*−1
QtQt*−1 is
the time varying correlation matrix, with
−
- Qt = (qC,T,t ) as described by Qt = (1 − a − b)Q +
a(εc,t-1 εT,t-1) + bQ t-1 , where εC ,t = RC ,t hC ,t and
εT ,t = RT ,t hT ,t are standardized residuals modeled from the first stage. Q is the N × N unconditional covariance matrix of standardized residuals,
a and b are non negative coefficients satisfying
a + b < 1,
*
*
- and Qt = qii ,t ) = qii ,t is a diagonal matrix composed of the square root of the ith diagonal elements of Qt , where i stands for C or T.
Rewriting Rt = Qt*−1QtQt*−1 , the time t conditional
return correlation between commodity and traditional
assets can then be expressed as
ρC ,
,t
=
qC ,T ,t
qC , qT ,t
(2)
This framework is useful to study the conditional
volatility of commodity portfolios and their conditional
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correlations with traditional assets. Regressions (3) and
(4) are then estimated
hC , = β0 + β hT ,t + εt
ρC ,
,t
= β0 + β
,t
+ εt
(3)
(4)
which regress the annualized conditional volatility of
long-short and long-only commodity portfolios or their
conditional correlation with traditional assets on the
annualized conditional volatility of traditional indexes
(S&P 500 or Barclays bond index). A positive and significant β coefficient in (3) indicates contagion of risk
across markets or concomitant increases in volatility. This
would be disappointing news to risk-adverse investors, as
the spreading of risk during a crisis would increase the
overall volatility of their combined portfolio. Vice versa,
a negative and significant β coefficient in (3) suggests
that in periods of high volatility in traditional asset markets investors can—other things being equal—decrease
the overall volatility of their portfolio by treating commodities as part of their strategic asset allocation.
A positive and significant β coefficient in (4) indicates that conditional correlations rise with the volatility of traditional assets. If so, the evidence of increased
integration of international stock markets in periods of
high equity volatility (Solnik et al. [1996], Longin and
Solnik [2001]) will be extrapolated to commodity and
traditional asset markets. On the other hand, a negative
and significant β coefficient in (4) indicates increased
segmentation between commodity and traditional asset
markets in periods of high volatility in traditional asset
markets. This result will be treated as welcome news
to investors in traditional assets, as it implies that the
usefulness of commodity futures as a diversification tool
increases in periods of high volatility in traditional asset
markets, namely, when investors need diversification
the most.
EMPIRICAL RESULTS
Performance of Long-Short Commodity
Portfolios
For each signal (Mom, TS, or HP), we obtain
16 strategies that come from the permutation of four
ranking and four holding periods. Instead of discussing
all strategies, the paper focuses for a given signal solely on
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the strategies with the highest and lowest Sharpe ratios
and on an aggregate portfolio that equally weights and
monthly rebalances the 16 strategies based on a given signal.5 Exhibit 1 presents summary statistics for the excess
returns of long-short portfolios based on momentum
(Panel A), term structure (Panel B), the hedging pressure
of hedgers (Panel C) and that of speculators (Panel D).
The samples considered are February 1979–March 2011
in Panels A and B and October 1992–March 2011 in
Panels C and D. For the sake of comparison, Panel E
presents summary statistics for two long-only commodity benchmarks—EW, which is a long-only equally
weighted portfolio of all commodities, and the S&PGSCI.
Altogether, the results of Exhibit 1 highlight the
importance of taking a long-short approach to commodity investing. Over the sample 1979–2011, the
annualized average excess return of a portfolio that
equally weights the 16 Mom (TS) strategies equals
3.45% (4.80%) a year significant at the 1% level; over
the same period, the long-only EW portfolio lost 1.87%.
Likewise over the sample 1992–2011, the annualized
average excess return of a portfolio that equally weights
the 16 hedgers’ (speculators’) HP strategies equals 5.95%
(5.89%) significant at the 1% level; over the same period,
the long-only EW portfolio earned 0.70% and the S&PGSCI earned 4.28%.
As the shorts provide a partial hedge against the
longs, the volatility of the long-short fully collateralized
portfolios (at an average of 10.5%) tends to be less than
that of the long-only commodity benchmarks (12% for
the EW portfolio, 21.78% for the S&P-GSCI in Exhibit
1, Panel E). As a result, the benefits of going long and
short are even stronger on a risk-adjusted basis. Over
the sample 1979–2011, the Sharpe ratios of the equally
weighted 16-Mom and 16-TS portfolios equal 0.45 and
0.61 versus −0.16 for the long-only EW benchmark; over
the period 1992–2011, the Sharpe ratios of the equally
weighted 16 hedgers’ HP and 16 speculators’ HP portfolios equal 0.68 and 0.71 versus 0.06 for the long-only
EW benchmark and 0.20 for the S&P-GSCI. All of the
individual long-short portfolios present Sharpe ratios
that are superior to that of the long-only benchmarks
(EW and S&P-GSCI). These results confirm the importance of taking backwardation and contango into account
when designing dynamic commodity strategies.
Following a long-standing literature (e.g., Bodie
and Rosansky [1980] or Miffre and Rallis [2007]), the
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constituents of the portfolios are equally weighted.
While frequent rebalancing to equal weights has its perks
in terms of potentially generating both a diversification
return (Erb and Harvey [2006]) and an illiquid risk
premium (Pastor and Stambaugh [2003]), that strategy
also exacerbates exposure to expensive illiquid assets,
which could ultimately harm net returns.6 As already
mentioned, the methodology we adopt to account for
potential illiquidity problems consists of systematically
excluding the 10% of the cross section with the lowest
dollar value of open interest averaged over the R weeks
preceding portfolio formation.
To further address concerns regarding transaction
costs, we also study the net performance of the strategies
and implement a break-even analysis. As reported on the
right-hand side of Exhibit 1, the decline in performance
after accounting for plausible and conservative round-
trip transaction costs7 is negligible and does not alter
our conclusion on the superiority of long-short strategies relative to their long-only counterparts. As part of
a break-even analysis, we also calculate the required
level of cost per commodity trade that sets the mean
return of the strategy equal to zero. Across the 16 permutations of ranking and holding periods, break-even
transaction costs equal 0.24% (Mom), 0.51% (TS), 0.67%
(speculators’ HP), and 0.59% (hedgers’ HP); these are
substantially higher than Locke and Venkatesh’s [1997]
estimate at 0.033%. Hence, significant mean returns
remain after considering plausible transaction costs.
Conditional Volatility
Exhibit 2 studies the conditional volatility of longshort Mom, TS, or HP portfolios (Panels A-D) and of
EXHIBIT 1
Performance of Long-Short Commodity Portfolios
Note: The table presents summary statistics for the excess returns of fully collateralized long-short commodity portfolios based on momentum, term structure,
or hedging pressure. Mean is the annualized mean of the portfolio excess returns, SD its annualized standard deviation, SR is the Sharpe ratio of the portfolio or the ratio of Mean to SD. “EW-16 strategies” represents an aggregate portfolio that equally weights the 16 strategies generated from permuting four
ranking (R) and four holding (H) periods. “Best strategy” (“Worst strategy”) is the strategy with the highest (lowest) Sharpe ratio out of the 16 strategies
considered. t-statistics are in parentheses. The last two columns report annualized mean excess returns net of plausible and conservative round-trip transaction costs (T-costs of 0.033% and 0.066%). The samples considered are February 1979–March 2011 (Panels A and B) and October 1992–March 2011
(Panels C and D).
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long-only commodity portfolios (EW and S&P-GSCI,
Panel E). As in Exhibit 1, the samples considered are
1979–2011 in Panels A and B and 1992–2011 in Panels
C and D. Under the headings H 01 and H 02, we report
t-statistics for the hypothesis that the difference in the
conditional volatilities of long-short and long-only commodity portfolios is zero. Note that under H 01 the longonly commodity portfolio used is EW, while under H02 it
is the S&P-GSCI. The t-statistics are very often less than
−1.96, indicating lower conditional volatilities for the
long-short portfolios. This result adds further backing
to the idea that we should take a long-short, as opposed
to a long-only, approach to commodity investing.
Exhibit 2 also studies how the conditional volatility of long-short and long-only commodity portfolios evolves vis-à-vis the volatility of traditional indexes.
Following from (3), we present estimates of the slope
coefficients β1 (β2 ) from regressions of the annualized
conditional volatilities of commodity portfolios on the
annualized conditional volatilities of the S&P 500 Index
(Barclays bond index).
Almost all β1 and β2 coefficients in Exhibit 2 are
positive and significant at the 1% level. Thus a rise in
volatility of traditional assets goes hand-in-hand with a
rise in volatility of both long-only and long-short commodity portfolios. Yet the average β1 and β2 coefficients
stand at merely 0.06 and 0.54 for the long-short commodity portfolios (in Panels A-D), and at 0.43 and 2.45
for the long-only commodity portfolios (in Panels E).
Thus, the volatility of long-short portfolios rises seven
times less than that of long-only commodity portfolios
during equity market turmoil and five times less than
that of long-only commodity portfolios in periods of
high volatility in fixed income markets. Other things
being equal, traditional investors are thus better off
holding long-short commodity positions in periods of
market turmoil. As the shorts perform better than the
longs during a market crash, the overall volatility of the
EXHIBIT 2
Conditional Volatility of Long-Short Commodity Portfolios
Note: The table presents under H01 (H02) t-statistics for the null hypothesis that the difference in the GARCH(1,1) volatility of the fully collateralized
long-short portfolios and that of the equally weighted long-only portfolio of all commodities (S&P-GSCI) equals zero. β1 (β2) is the slope coefficient of a
regression of the conditional volatility of commodity portfolios on the conditional volatility of the S&P 500 Index (Barclays Capital U.S. Aggregate Bond
Index). “EW-16 strategies” represents an aggregate portfolio that equally weights the 16 strategies generated from permuting four ranking (R) and four
holding (H) periods. “Best strategy” (“Worst strategy”) is the strategy with the highest (lowest) Sharpe ratio out of the 16 strategies considered. “EW
long-only” represents an equally weighted, monthly rebalanced portfolio of all commodities. t-statistics in parentheses. The samples considered are February
1979–March 2011 (Panels A and B) and October 1992–March 2011 (Panels C and D).
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EXHIBIT 3
Conditional Correlations between Long-Short (Long-Only)
Commodity Portfolios and S&P 500 Index
combined long-short portfolio rises less during
contagion than the overall volatility of a longonly portfolio.
Conditional Correlations Over Time
Note: The full lines represent the conditional correlations between the total returns of
aggregate long-short portfolios and the S&P 500 Index. The dashed lines represent the
conditional correlations between the total returns of long-only commodity portfolios (longonly equally weighted and S&P-GSCI) and the S&P 500 Index. The sample covers the
period February 1979–March 2011 on the left-hand side and the period October 1992March 2011 on the right-hand side.
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Notwithstanding the importance of conditional volatility, it is important from a risk management perspective to also look at cross-market
linkage and at how conditional correlations evolve
over time. Exhibit 3 plots the conditional returns
correlations between the S&P 500 and various
commodity portfolios. The long-short portfolios
employed on the left-hand side are based on Mom
or TS over the period 1979–2011; those on the
right-hand side center on hedgers’ and speculators’ HP over the period 1992–2011.
As before, instead of plotting each of the 16
long-short Mom, TS, or HP portfolios, Exhibit
3 considers for a given signal an aggregate portfolio that equally weights and monthly rebalances
these 16 permutations of ranking and holding
periods. This is to ease presentation. A casual look
at Exhibit 3 indicates that the conditional correlations between the S&P 500 Index and long-short
commodity portfolios (full lines) are lower than
those with long-only commodity portfolios (dash
lines). While this phenomenon applies across the
whole sample, it seems more pronounced over the
period 2008–2011.
Exhibit 4 presents means for the conditional
correlations as modeled in Equation (2) between
the S&P 500 Index and long-short or long-only
commodity portfolios. As in Exhibit 1, the samples considered are 1979–2011 in Panels A and B,
and 1992–2011 in Panels C and D. Note that the
correlations with the S&P 500 Index are modeled
over the whole sample and over two consecutive
sub samples that end and start with the debacle
of Lehman Brothers dated September 15, 2008.
The right-hand side of Exhibit 4 presents the
average of the conditional correlations between
the Barclays bond index and either one of the
commodity portfolios; because of the availability
of Barclays data, the sample considered then starts
January 1989.
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EXHIBIT 4
Average Conditional Correlations between Traditional Assets and Long-Short Commodity Portfolios
Note: The table presents the average of the DCC(1,1) correlations between the S&P 500 (Barclays) Index and long-short (Panels A to D) and long-only
(Panel E) commodity portfolios. “EW-16 strategies” represents an aggregate portfolio that equally weights the 16 strategies generated from permuting four
ranking (R) and four holding (H) periods. “Best strategy” (“Worst strategy”) is the strategy with the highest (lowest) Sharpe ratio out of the 16 strategies
considered. “EW long-only” represents an equally weighted, monthly rebalanced portfolio of all commodities. t-statistics are in parentheses. The samples
considered are February 1979–March 2011 (Panels A and B) and October 1992–March 2011 (Panels C and D).
Three points are worth noting. First, as in, e.g.,
Bodie and Rosansky [1980], the means of the conditional correlations are low, confirming the strategic role
of commodity portfolios as risk diversifiers. Second,
the average conditional correlation between long-short
commodity portfolios and the S&P 500 Index (−0.01
in Panels A-D) is much lower than that reported for the
long-only indexes (0.16 in Panels E). While the former
are not significantly different from zero, the latter are
positive and significant at the 1% level.
Third, the observed pattern is stronger since September 19, 2008; over the second period the mean
of the conditional correlations between the S&P 500
Index and long-short indexes stands at a mere 0.05 and
is therefore close to its historical average. In sharp contrast, the average correlation between the S&P 500 and
long-only indexes rose sharply to 0.44 after the Lehman
Brothers debacle. This result highlights once more the
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importance of taking both long and short commodity
positions as opposed to being long-only. The benefits
of diversification are then much stronger.8
The right-hand side of Exhibit 4 reports averages of conditional correlations between long-short or
long-only commodity portfolios and the Barclays bond
index. As previously reported (e.g., Bodie and Rosansky
[1980]), the correlations with long-only commodity
indexes in Panels E are low (−0.06 on average) and
negative at the 5% level or better in the case of the
long-only EW portfolio. Albeit insignificant, the correlations between the Barclays bond index and long-short
commodity portfolios are also low in Panels A-D, averaging out 0.02. Other things being equal, from solely
a risk diversification perspective, bond investors shall
be indifferent between the S&P-GSCI and long-short
portfolios and have a slight preference for the long-only
EW portfolio.
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EXHIBIT 5
side presents the same information using a longonly commodity portfolio, the S&P-GSCI, in
The Relationship between Conditional Correlation and
place of the long-short hedger-based portfolio.
Conditional S&P 500 Volatility
The conditional correlations between the longshort commodity index and the S&P 500 Index
in Exhibit 5 tend to fall when the conditional
volatility of the S&P 500 Index rises. In fact, the
correlation between the two series plotted on the
left-hand side is as low as −0.28 and is different
from zero at the 1% level.
On the other hand, the conditional correlations plotted on the right-hand side vis-à-vis
a long-only commodity index tend to rise with
the conditional volatility of the S&P 500 Index
(the correlation between the two series is at 0.24,
significant at the 1% level). This suggests that
investors following a long-short (as opposed to
long-only) approach to commodity investing get
better diversification when it is most needed,
namely, in periods of high volatility in equity
markets. To put this differently, long-short HP
portfolios seem to serve as a partial hedge against
extreme risks in equity markets.
Exhibit 6 reports slope coefficients from
Equation (4), i.e., from regressions of the conditional correlations with the S&P 500 on the conditional S&P 500 volatility. To test the robustness
of the results, we estimate the parameters over
different samples and report them for aggregate
portfolios that equally weight the 16 strategies
generated by a given signal (Panel A), for the best
and the worst of these 16 strategies (Panels B and
C, respectively) and for long-only benchmarks
(Panel D). In line with the preceding discussion, β in Exhibit 6 is negative and almost always
Note: The hedging pressure portfolio considered on the left-hand side is an aggregate
significant for the HP portfolios. In fact, a rise
equally weighted portfolio of 16 long-short hedger-based strategies. The correlation
by
1% in the volatility of the S&P 500 Index
between the two series pertaining to the left-hand side graph (right-hand side graph)
decreases
the conditional correlations between
is −0.28 (0.24), significant at the 1% level.
the S&P 500 Index and the HP portfolios by an
average of 0.34%. It follows that equity investors
Conditional Correlations in Periods of High
worried about diversification in periods of high
Volatility in Traditional Asset Markets
volatility in equity markets should use the positions of
hedgers or speculators over the recent past as long-short
Exhibit 5 plots on the left-hand side the condisignals for asset allocation.
tional return correlations between the aggregate hedgerThese long-short portfolios offer enhanced diversibased portfolio and the S&P 500 Index, and on the
fication benefits to equity investors in periods of severe
right-hand side the annualized conditional volatility of
downturns. The sub sample analysis presented on the
the S&P 500 Index returns. The plot on the right-hand
right-hand side of Exhibit 6 indicates that the conclusion
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EXHIBIT 6
Conditional Correlations between Commodity Portfolios and the S&P 500 Index in Periods of High Volatility in
Equity Markets
Note: The table reports slope coefficients of regressions of the conditional correlation between commodity portfolios and the S&P 500 Index on the conditional volatility of the S&P 500 Index. Aggregate long-short portfolios are portfolios that equally weight the 16 strategies generated from a given signal
(Panel A). “EW long-only” represents an equally weighted, monthly rebalanced portfolio of all commodities. t-statistics are in parentheses.
of decreasing correlation in periods of heightened volatility for the HP portfolios, albeit stronger over the
period 2002–2011, holds in both sub samples.
Conversely and with a few exceptions only, the
long-only benchmarks and long-short TS portfolios
act as worse risk diversifiers in periods of heightened
equity volatility. This is demonstrated in Exhibit 6 by
estimated β coefficients that are positive and often significant for those portfolios, especially over the samples 1980–2011 and 2002–2011. For example, over the
sample 2002–2011, a rise by 1% in the conditional volatility of the S&P 500 Index came hand-in-hand with a
rise by 0.95% in the conditional correlation between the
S&P 500 and long-only benchmarks in Panel D.
When severe volatility strikes in equity markets,
it contaminates long-only commodity markets too,
bringing both asset classes down; subsequently their
conditional return correlations go up. This is unfortunate as it is precisely when contagion spreads from
one market to the next that equity investors need the
benefits of diversification the most. Other things being
equal, it is exactly in periods of heightened volatility
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that long-only commodity investors get the benefits of
diversification the least.
Interestingly, the average β coefficient for longshort TS portfolios in Exhibit 6, Panels A−C stands
at merely 0.20 versus 0.48 for long-only portfolios in
Panel D. Thus, while the correlations with the S&P 500
Index tend to rise in periods of enhanced equity risk for
both types of portfolios, the diversification properties
of commodities are more preserved within the longshort TS portfolios than within the long-only portfolios.
Across Panels A to C, the average of the estimated β
coefficients for the long-short Mom portfolios stands at
−0.03, suggesting little variation in the conditional correlations between Mom and the S&P 500 as the volatility
of the S&P 500 changes.
Exhibit 7 presents the weekly performance of
the S&P 500 Index, of long-only and long-short commodity portfolios in the four weeks that followed
Lehman Brothers’ demise (dated September 15, 2008).
Over that period, the S&P 500 composite index lost an
average of 8.23% a week, the S&P-GSCI lost 6.42% and
the long-only EW portfolio lost 4.71%. Over the same
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EXHIBIT 7
Average Total Returns of Long-Short and Long-Only Portfolios over the Four Weeks Following Lehman
Brothers’ Demise (September 15, 2008)
Note: For a given signal, the long-short portfolios here considered are the aggregate equally weighted portfolios of 16 strategies.
EXHIBIT 8
at 0.98%, the HP portfolios particularly
stand out.
Conditional Correlations between Commodity Portfolios and Barclays
Exhibit 8 considers the same inforBond Index in Periods of High Volatility in Fixed Income Markets
mation as Exhibit 6 but from the perspective of a fixed-income investor. It
reports slope coefficients from regressions of the conditional correlations with
Barclays bond index on the conditional
volatility of Barclays bond index. With
two exceptions only, the reported slope
coefficients are positive and significant
at the 10% level or better. This conclusion holds irrespective of the period
considered (1989–2011 in Panel A;
1993–2011 in Panel B) and of the signal
used to design the long-short strategies.
Note: The table reports slope coefficients of regressions of the conditional correlation between commodity portfolios and Barclays bond index on the conditional volatility of the S&P 500 Index.
The average β coefficients stand at 2.02
“EW-16 strategies” represents an aggregate portfolio that equally weights the 16 strategies generfor the Mom and TS portfolios, 2.21
ated from permuting four ranking (R) and four holding (H) periods. “EW long-only” represents
for the HP portfolios and 2.61 for the
an equally weighted, monthly rebalanced portfolio of all commodities. t-statistics are in parentheses.
long-only portfolios. This uniformly
indicates concomitant rises in f ixed
period, both the long and short commodity portfolios
income
volatility
and conditional correlation. None of
experienced large losses. Consequently, the long-short
the
commodity
portfolios
therefore stand out as particuportfolios performed remarkably well in relative terms,
larly
good
diversifiers
of
interest
rate risk in periods of
suggesting that they act as partial hedges against extreme
heightened volatility in fixed income markets.
risk in equity markets. With an average weekly return
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CONCLUSIONS
This article takes the stand of a traditional investor
who contemplates investing in commodities. Is he/she
better off holding a long-only or a long-short commodity portfolio? To answer this question, we study the
performance of long-short and long-only commodity
portfolios, their conditional volatility and their conditional correlations with traditional asset classes over time
and in periods of heightened volatility in traditional asset
markets.
The results highlight the superiority of long-short
strategies. Relative to long-only portfolios, long-short
portfolios offer better risk-adjusted performance. In
periods of turmoil in traditional asset markets, as contagion spreads, the volatility of long-short commodity
portfolios tends to rise less than that of long-only commodity portfolios. Likewise, long-short commodity
portfolios correlate less with the S&P 500 than longonly commodity indexes and thus act as better diversifiers of equity risk.
This is particularly true since the debacle of
Lehman Brothers. HP strategies are particularly noteworthy in this respect as they become better risk diversifiers in periods of heightened equity risk, and thus when
hedging is most needed. In contrast, the conditional
correlation between long-only commodity indexes
and the S&P 500 returns rises with the volatility of the
S&P 500 Index, suggesting that the risk reduction that
comes from diversification prevails less when needed
most, namely, during equity market downturns.
Following Daskalaki and Skiadopoulos [2011] in a
long-only setting, it would be of interest to test whether
investors can increase the out-of-sample performance
of their multi-asset portfolio by adding long, as well
as short, commodity positions. We see this topic as an
interesting avenue for future research.
ENDNOTES
We would like to thank H. Till, O. Johnson, his colleagues at the Chicago Mercantile Exchange Group and participants at the 2012 EDHEC-Risk Institute Europe and Asia
conferences for their useful comments. J. Miffre acknowledges financial support from the CME Group. This article
presents our views and conclusions, not necessarily those of
the CME Group.
1
Backwardation signals that commodity futures prices are
likely to rise as maturity approaches. It occurs when hedgers
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are net short and speculators are net long (Cootner [1960],
Bessembinder [1992], Basu and Miffre [2013]); when the
term structure of commodity futures prices slopes downward
(Fama and French [1987], Erb and Harvey [2006], Gorton
and Rouwenhorst [2006]); when inventories are low (Gorton
et al. [2013]) or when contracts exhibit good past performance (Erb and Harvey [2006]; Miffre and Rallis [2007]).
Conversely, contango signals that commodity futures prices
are likely to fall as maturity approaches. The signals are then
reversed.
2
We appreciate that motives of non commercial and
commercial market participants are often hard to recognize
and thus that the classification of traders as either speculators
or hedgers might be at times inaccurate (see Ederington and
Lee [2002] or Dewally et al. [2013]). This point notwithstanding, members of the Commodity Futures Trading Commission are here to check the declarations of non commercial
and commercial market participants and thus we hope that
their declarations as either speculators or hedgers are reliable
accounts of their true positions.
3
In the spirit of Fuertes et al. [2010] and Basu and Miffre
[2013] we also consider double-sort strategies that combine
pairs of signals. The conclusions, similar to those pertaining
to the single-sort strategies considered here, are unreported
but available upon request.
4
See also Büyük ahin et al. [2010], Chong and Miffre
[2010] and Büyük ahin and Robe [2013] for an analysis of
conditional correlations between traditional assets and longonly commodity futures positions.
5
Detailed results on individual strategies are available
upon request.
6
Equal weighting also exacerbates exposure to high
volatility contracts. Alternatively, one could force each
constituent to contribute equally to portfolio risk (see, for
example, Anderson et al. [2012]). We see the study of the
performance of risk parity strategies in commodity futures
markets as an interesting avenue of research. Likewise, the
study of capacity constraints and their impact on the feasibility of a trading signal is deemed of interest, as it is of prime
importance to practitioners.
7
We account for a plausible round-trip transaction cost
of 0.033% (Locke and Venkatesh [1997]) and for a conservative round-trip transaction cost equal to twice that amount
or 0.066%. Transaction costs are incurred not only while
implementing the strategy itself, but also when rebalancing
the portfolio to equal weights.
8
We confirm here the findings of Tang and Xiong
[2012] and Büyük ahin and Robe [2013], who show before
us that the conditional correlations between long-only commodity and equity portfolios rose following the downfall of
Lehman Brothers.
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