+ the stock market price of commodity risk november 2013 martijn boons, nova school of business and...

+

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

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rrr1

rr1

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r

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rrr1

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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

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)(

+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