Expected Commodity Futures Returns

1. Expected Commodity Futures Returns Saqib KhanZeigham KhokherTimothy Simin July 2008 Tim Simin: tsimin@psu.edu

2. IntroductionWSJ, 11/12/08, Opinion section

3. IntroductionThe Motivation Predictability and market efficiency Markets are inefficient Markets are efficient and findings of predictability are spurious or data mined [Ferson, Sarkissian, and Simin (2003)] Markets are efficient but expectations of returns are time-varying Time-varying expected returns Motivated by Merton’s ICAPM Ferson and Harvey (1991, 1993), Ilmanen (1995) Fama (1970, 1991) Tim Simin: tsimin@psu.edu 3

4. OverviewWhat we do Posit futures returns are a linear function of 3 factors Systematic component Hedging demands Supply conditions Allow for time-varying risk premium Let bF vary with predetermined commodityspecific info Let E(F) vary with predetermined business cycle info Test if predictable variation in future returns are: Significantly explained by the model Related to hedging demands and/or supply conditions Tim Simin: tsimin@psu.edu 4

5. OverviewTake-away Are E(R|Z)’s a function of hedging demands and/or supply conditions? Yes, even if Z = Ø Hedging more than level of scarcity Are the risk premia time varying? Yes! Conditional model pricing errors Vary with business cycle What drives the time variation? Both bt,F and Et(F)’s [but mostly Et(F)] Tim Simin: tsimin@psu.edu 5

6. Futures returns, hedging demands, supply conditions Overview of model and data Unconditional model Three versions of the conditional model Test for predictable variation left in pricing errors Estimate predictable variation explained Decompose predictable variation OverviewOutline Tim Simin: tsimin@psu.edu

7. Futures Returns Background Why commodity specific factors? Hirshleifer (1988, 1990) and De Roon, Nijman, and Veld (2000) Why hedging pressure? Keynes (1930) Why inventories? Kaldor (1939) Why model conditionally? Mean reversion: Bessembinder, Coughenour, Seguin, and Smoller (1995)] commodity futures returns vary with business cycles: Fama and French (1988), Bessembinder and Chan (1992), Bessembinder (1992), Ng and Pirrong (1994) Tim Simin: tsimin@psu.edu 7

8. Futures Returns Our Model Based on conditional CAPM In the spirit of Hirshleifer (1988, 1990) Factors are both systematic and ‘idiosyncratic’ In the spirit of Ferson and Harvey (1991, 1993) and Ilmanen (1995) b’s vary with commodity specific instruments l’s vary with macro-economic instruments Tim Simin: tsimin@psu.edu 8

9. DataOverview Dependent data Crude oil, Copper, Gold, and Natural gas NYMEX monthly prices, Jan. 1987 – Dec. 2005 Spot price = nearest-to-maturity contract Factors Systematic: Market returns (S&P 500) Commodity specific: Net hedging pressure & Withdrawals Instruments Macroeconomic: Jan, Def, Term, Infl, TB, DY Commodity specific: Basis, Inventory levels Tim Simin: tsimin@psu.edu 9

10. DataNet Hedging Pressure De Roon, Nijman, Veld (2000) Weekly trader’s commitment data from (CFTC) Proxy for aggregate non-marketable risks Tim Simin: tsimin@psu.edu 10

11. DataSupply conditions Proxies for economic inventory: Crude = API’s weekly bulletin Natural gas = “Working gas”, Energy Info Admin Copper and Gold = stocks at COMEX Caveats: Not aggregate measures Dual role of gold Discretionary inventories: remove expected seasonal Scarcity = withdrawals from inventory (Invt-1- Invt) Tim Simin: tsimin@psu.edu 11

14. DataBasis Negative of “convenience yield” Fama and French (1987) S = spot price of commodity at t Ft,T= future price at t of T=10 month contract Benefit of holding product vs. futures contract Tim Simin: tsimin@psu.edu 14

15. ResultsUnconditional Model Tim Simin: tsimin@psu.edu 15

16. ResultsTime-varying betas Start with Use OLS to estimate Tim Simin: tsimin@psu.edu 16

17. ResultsTime-varying betas Testing in conditional framework Marginal impact of Ftconditional on Zt Implications Testing if b0 = 0 is appropriate only if we assume Zt= 0 Marginal impact of F can be significant even with insignificant b1 Tim Simin: tsimin@psu.edu 17

18. ResultsTime-varying betas Tim Simin: tsimin@psu.edu 18

19. ResultsTime-varying E(F)’s Get Et(F) = Zt-1g from the regression Ft = Zt-1g + et Coefficients from model are Tim Simin: tsimin@psu.edu 19

20. ResultsTime-varying E(F)’s Tim Simin: tsimin@psu.edu 20

21. ResultsTime-varying betas and E(F)’s Same as above except Tim Simin: tsimin@psu.edu 21

22. ResultsTime-varying betas and E(F)’s Tim Simin: tsimin@psu.edu 22

23. Comparing ModelsUnconditional vs. Conditional Regress et on Zt-1 eu = errors from unconditional model eb,rp = errors from conditional model eb,rp = rt– (Z’t-1g)(k’Zt-1) Tim Simin: tsimin@psu.edu 23

24. Comparing ModelsCapturing predictable variation Define two ratios VR1 = predictable variation captured by the model VR2 = predictable variation not captured by the model Tim Simin: tsimin@psu.edu 24

25. Time-variationBetas vs. E(F)’s Decompose predictable variation of model Var{E(b’l|Z)}= E(b)’Var{E(l|Z)}E(b) + E(l)’Var{b(Z)}E(l) + f Tim Simin: tsimin@psu.edu 25

26. ConclusionTake-aways Are E(R|Z)’s a function of hedging demands and/or supply conditions? Yes, even if Z = Ø Are the risk premia time varying? Yes, and they vary with the business cycle, i.e. investment opportunity set What drives the time variation? Both bt,F and Et(F)’s [but mostly Et(F)] Tim Simin: tsimin@psu.edu 26

27. Extensions Other commodities Sub-sample analysis Accounting for cross-market effects Include NHP from other markets in model (DeRoon) Jointly estimate the models in GMM Include other factors/instruments Exchange rates Tim Simin: tsimin@psu.edu 27