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The Stock Market Price of Commodity RiskNovember 2013
Martijn Boons, Nova School of Business and Economics
Frans de Roon, Tilburg University, CentER
Marta Szymanowska, Rotterdam School of Management
+Motivation Commodity Futures Modernization Act (CFMA)
Dramatic change in size and composition of futures markets
2
1962011965111969091973071977051981031985011988111992091996072000052004032008010
2
4
6
8
10
12
14
16
Energy
Agriculture
Metals & Fibers
Livestock & Meats
EW Average
TOI in 33 commodities
+Motivation
CFMA: break point in the behavior of (institutional) investors
Pre-CFMA commodity exposure
position limits in futures markets
commodity-related equity, physical commodities (Lewis, 2007)
Post-CFMA commodity exposure
commodity index investment (CII) by institutions from 6% of total open interest (< 10$ bln) in 1998 to 40% (> 200$ bln) in 2009
3
4+Our goal
We want to understand commodity prices as a source of risk price of this risk in the stock and commodity futures
markets impact of CFMA / changing investment behavior
This will allow us to shed light on a link between stock and commodity futures markets “financialization” of commodities stock market strategies to hedge or speculate on
commodity prices
5+Our Approach
A model with investors exposed to commodity price risk in the spirit of Hirshleifer (1988,1989), Bessembinder and Lemmon
(2002) Study the effect of position limits on demand and prices
Testable implications Sort stocks on commodity beta Sort commodity futures on stock market risk
Main empirical findings 1. Commodity risk is priced in stock market in the opposite way before
and after CFMA
2. Stock market risk is priced in the commodity futures market post-CFMA
6+The model
Agents Commodity Producers (business exposed to commodity price
risk and trade futures contract ) Specialized Speculators (e.g. CTA's, trade futures contract) Investors
Position limit (pre-CFMA): invest in stocks () only No limit (post-CFMA): invest in both stocks and futures
contract
Standard, two-date, mean-variance framework
Investors are exposed to commodity price risk: inflation, state variable
Today: available futures contract is a perfect hedge (
7+The Investor’s problem
Excess portfolio return , such that1. With limit (
2. Without limit ()
Optimal portfolios (1) with and (2) without limit
11,111
IspecF,
K
specF,
specF,rS1
rrr1
rr1
I
F
r
rS1
rrr1
rr1
Ir
' using γ wwhere
,0
w
wΣΣμΣγ
w
w 2.
ΣΣμΣγ w1.
ttrF,tee erba ra
)Σw'2wΣw'(2
γμμ w'max 2
rSrrrrrI
Srrw r SS
)Σw'2Σww'(2
γμμ w'max 2
SI
Sw SS
8+Expected stock returns with commodity price risk
With limits
Cross-hedging demand implies a negative (positive) risk premium when φ < 0 (φ > 0) and high commodity prices are bad (good) news
Without limits
Risk premium determined by speculative investment in commodities
If zero CAPM!
iSimtirE II1, γγ)(
iSimtirE specF,II1, wγγ)(
9+Risk premiums in the futures market1. With limit: Producers and Speculators only
2. Without position limits: stock market risk is priced due to presence of Investors
SPiN
rE
i
FFtF
, ,γ/
)1(γ)(
ii
PSP
P1,
rrrtTtFFT
ee
FFi
FTFFtF
rr
ISPiN
rE
1T1,1,
IIii
ISP
II
ISP
IIPP1,
w where),cov(
and ,, ,γ/
withγγ)1(γ
)(
+Data and method: stock market
All CRSP stocks, French’s 48 industry portfolios
OIW index of 33 commodities (from CRB and FII) Robust: EW index, S&P-GSCI index Variation across commodity sectors
Sorts on rolling 60 month commodity beta Mean and risk-adjusted returns (CAPM, FF3M and FFCM) of
High minus Low (HLCB) portfolios Pre- versus Post-CFMA: split around December 2003
Robust Different break points Different rebalancing Fama-MacBeth cross-sectional estimates Between/within industry sort Controlling for inflation
10
+Stock market: pre-CFMA
11
Low 2 3 4 High
-10.00
-5.00
0.00
5.00
10.00
15.00
Stocks
FFCMFF3MCAPM Means
Low 2 3 4 High
-10.00
-5.00
0.00
5.00
10.00
15.00 48 Industries
FFCMFF3MCAPM Means
12+Stock market: post-CFMA
Low 2 3 4 High
-10.00
-5.00
0.00
5.00
10.00
15.00
Stocks
FFCMFF3MCAPM Means
Low 2 3 4 High
-10.00
-5.00
0.00
5.00
10.00
15.00
48 Industries
FFCMFF3MCAPM Means
13+Means and FFCM alphas
Pre-CFMA Post-CFMA
Size quintile One-way Size quintile One-way
OIW OIW OIW OIW OIW EW OIW OIW OIW OIW OIW EW
S 3 B Stocks 48 Ind. Stocks S 3 B Stocks 48 Ind. Stocks
Means H 5.88 3.55 2.33 1.91 5.00 4.45 12.13 15.29 15.10* 14.85* 14.57 11.93
4 8.88* 6.90* 7.04* 6.58* 8.23* 5.77 12.02 9.97 4.78 5.64 5.97 7.33
3 10.56* 9.44* 6.32* 7.04* 7.84* 8.25* 11.07 8.58 2.08 3.58 6.62 5.16
2 10.55* 11.32* 9.24* 9.53* 10.07* 8.81* 9.25 7.91 3.08 3.87 6.47 5.07
L 8.93* 13.03* 10.01* 10.02* 9.72* 9.33* 1.88 1.98 3.25 2.77 2.35 3.24
HLCB -3.04 -9.47* -7.68* -8.11* -4.72* -4.88 10.25* 13.31* 11.85* 12.08* 12.22* 8.69
FFCM H -1.73 -6.12* -5.52* -6.67* -4.75* -3.52 1.65 6.81 11.30* 9.82* 8.60* 6.23
4 0.69 -3.23* -0.97 -1.73 -0.92 0.40 2.40 2.46 1.67 1.33 -0.82 1.76
3 2.41 0.43 -0.61 -0.13 -1.99 0.76 1.60 1.66 -1.83 -0.93 1.08 1.16
2 2.82 3.48* 3.22* 3.33* 2.13 1.08 0.77 1.53 -0.47 -0.19 1.23 1.18
L 2.75 5.59* 5.88* 4.99* 2.12 2.77* -6.66* -4.67* 0.36 -1.08 -2.01 -0.09
HLCB -4.48* -11.71* -11.39* -11.66* -6.87* -6.30* 8.31* 11.48* 10.94* 10.90* 10.60* 6.32
* Indicates significance at the 5%-level
+The reversal in the commodity risk premium I
Recall:
Reversal obtains when (1) and (2) . Plausible:
1. Negative exposureto commodity price risk for Investors from
Inflation: commodity prices are most volatile components State-variable risk: Energy and Metals prices predict
negative stock returns (e.g., Driesprong et al. (JFE, 2008), Jacobsen et al. (2013)) Results driven by commodities from Energy and Metals
sectors
14
γγ)( vsγγ)( ,II1,II1, iSspecFimtiiSimti wrErE
15+The reversal in the commodity risk premium II
2. A positive speculative investment in commodity futures () obtains when
Hedging pressure from Producers is sufficiently large, i.e., the group of Producers is relatively large and risk averse (“normal backwardation”) Indeed, we find that commercial hedger’s short positions
are sufficient to cover non-commercial speculators long positions
Cheng et al. (2011): hedgers short positions increase in lockstep with CIT’s long positions
Consistent with diversification benefits in Gorton and Rouwenhorst (FAJ, 2006) and Erb and Harvey (FAJ, 2006)
16+Hedgers versus Speculators
17+Commodity futures risk premiums
With and without limit: a “classic“ hedging pressure effect In both sub-periods, sorting on hedging pressure works
Without limits, stock market risk is priced in the futures market Using that T=M+H, sort commodities on beta with respect to the
MKT and HLCB portfolio High stock market beta commodities outperform ONLY post-
CFMA, as predicted!Pre-CFMA Post-CFMA
HLCB HLCB
MKT Low High MKT Low High
Low 1.36% 7.43% Low -2.23% 8.09%
High -2.25% 3.83% High 9.06% 9.16%
HH-LL 2.48% HH-LL 11.38%
t(HH-LL) (0.55) t(HH-LL) (1.77)
18+Conclusion
Focus on the structural break in investor’s behavior Study a model with Investors exposed to commodity price risk Analyze the effect of position limits related to CFMA
We find Commodity risk is priced in stock market in the opposite way
pre- versus post-CFMA Stock market risk is priced in the commodity futures market
post-CFMA Consistent with Investors seeking commodity exposure in the
stock market pre-CFMA and subsequently in the commodity futures markets
Stocks to hedge or speculate on commodity prices
19+Within-industry sort
“Out-of-sample” test: spreads exist when using only within-industry variation in commodity beta
Hedge, while keeping industry exposure constant
1980-2003 (Pre-CFMA) 2004-2010 (Post-CFMA)
Industries sorted on commodity beta Industries sorted on commodity beta
Within-industry H 4 3 2 L Average H 4 3 2 L Average
Means HLCB -3.39 -6.13* -4.17 -3.34 -4.72 -4.35* 13.64* 11.01* 5.38 19.05* 9.37 11.69*
FFCM HLCB -6.92* -7.58* -4.37 -4.86* -9.01* -6.55* 13.92* 9.76 2.17 14.58* 5.48 9.18*
20+Industry composition of High and Low portfolio
Oil [2
1; 3
8; 6
3]
Busin
ess Se
rvice
s [1
4; 6
; 9]
Chips
[23;
7; 1
3]
Utiliti
es [3
; 8; 2
5]
Compu
ters
[9; 4
; 9]
Stee
l [38
; 22;
33]
Drugs
[1; 2
; 8]
Machi
nery
[13;
6; 1
8]
Gold
[99;
87;
85]
Mines
[66;
27;
50]
0.00
0.10
0.20
0.30
0.40
1980-19901991-20002001-2010
Retai
l [46
; 32;
24]
Tele
com
[46;
14;
36]
Busin
ess Se
rvice
s [1
3; 1
7; 1
9]
Drugs
[39;
10;
15]
Utiliti
es [4
1; 1
0; 4
]
Compu
ters
[6; 2
2; 1
3]
Consu
mer
Goo
ds [3
2; 1
0; 1
7]
Chips
[10;
21;
8]
food
pro
duct
s [39
; 17;
15]
Toba
cco
[48;
26;
7]
0.00
0.05
0.10
0.15
0.20
0.25
1980-19901991-20002001-2010