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The Stock Market Price of Commodity Risk November 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)

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the stock market price of commodity risk november 2013

The Stock Market Price of Commodity Risk November 2013

Martijn Boons, Nova School of Business and Economics

Frans de Roon, Tilburg University, CentER

Marta Szymanowska, Rotterdam School of Management

motivation
Motivation
  • Commodity Futures Modernization Act (CFMA)
  • Dramatic change in size and composition of futures markets

TOI in 33 commodities

motivation1
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
our goal
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
our approach
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
    • Commodity risk is priced in stock market in the opposite way before and after CFMA
    • Stock market risk is priced in the commodity futures market post-CFMA
the model
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 (
the investor s problem
The Investor’s problem
  • Excess portfolio return , such that
    • With limit (
    • Without limit ()
  • Optimal portfolios (1) with and (2) without limit
expected stock returns with commodity price risk
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!
risk premiums in the futures market
Risk premiums in the futures market
  • With limit: Producers and Speculators only
  • Without position limits: stock market risk is priced due to presence of Investors
data and method stock market
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
the reversal in the commodity risk premium i
The reversal in the commodity risk premium I
  • Recall:
  • Reversal obtains when (1) and (2) . Plausible:
  • 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
the reversal in the commodity risk premium ii
The reversal in the commodity risk premium II
  • 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)
commodity futures risk premiums
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!
conclusion
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
within industry sort
Within-industry sort
  • “Out-of-sample” test: spreads exist when using only within-industry variation in commodity beta
    • Hedge, while keeping industry exposure constant