Economic linkages across commodity futures: Hedging and trading implications

We investigate cross-market trading dynamics in futures contracts written on seemingly unrelated commodities that are consumed by a common industry. On the Tokyo Commodity Exchange, we find such evidence in natural rubber (NR), palladium (PA) and gasoline (GA) futures markets. The automobile industry is responsible for more than 50% of global demand for each of these commodities. VAR estimation reveals short-run cross-market interaction between NR and GA, and from NR to PA. Cross-market influence exerted by PA is felt in longer dynamics, with PA volatility (volume) affecting NR (GA) volume (volatility). Our findings are robust to lag-specification, volatility measure, and consistent with full BEKK-GARCH estimation results. Further analysis, which benchmarks against silver futures market, TOCOM index and TOPIX transportation index, confirms that our results are driven by a common industry exposure, and not a commodity market factor. A simple trading rule that incorporates short-run GA and long-run PA dynamics to predict NR return yields positive economic profit. Our study offers new insights into how commodity and equity markets relate at an industry level, and implications for multi-commodity hedging.

[1]  Kewei Hou,et al.  Industry Information Diffusion and the Lead-Lag Effect in Stock Returns , 2007 .

[2]  Cheol S. Eun,et al.  Cross‐Border Listings and Price Discovery: Evidence from U.S.‐Listed Canadian Stocks , 2003 .

[3]  R. Chiang,et al.  Relative informational efficiency of cash, futures, and options markets: The case of an emerging market , 2001 .

[4]  M. Parkinson.  The Extreme Value Method for Estimating the Variance of the Rate of Return , 1980 .

[5]  Shmuel Hauser,et al.  International Transfer of Pricing Information between Dually Listed Stocks , 1998 .

[6]  R. Engle,et al.  Multivariate Simultaneous Generalized ARCH , 1995, Econometric Theory.

[7]  Angelos Kanas,et al.  Mean Variance Causality between the Official and Parallel Currency Markets: Evidence from Four Latin American Countries , 2000 .

[8]  Shuh-Chyi Doong,et al.  Price and Volatility Spillovers between Stock Prices and Exchange Rates: Empirical Evidence from the G-7 Countries , 2004 .

[9]  Kalok Chan,et al.  A Further Analysis of the Lead–Lag Relationship Between the Cash Market and Stock Index Futures Market , 1992 .

[10]  F. Caramazza,et al.  International financial contagion in currency crises , 2004 .

[11]  B. Jacobsen,et al.  Striking Oil: Another Puzzle? , 2003 .

[12]  H. Johnson,et al.  Why Option Prices Lag Stock Prices: A Trading-Based Explanation , 1993 .

[13]  Michael T. Chng.  The trading dynamics of close-substitute futures markets: evidence of margin policy spillover effects , 2004 .

[14]  S. Sapp,et al.  Temporal Patterns in Foreign Exchange Returns and Options , 2007 .

[15]  Cetin Ciner.  On the long run relationship between gold and silver prices A note , 2001 .

[16]  Hélyette Geman,et al.  WTI crude oil Futures in portfolio diversification: The time-to-maturity effect , 2008 .

[17]  Lior Menzly,et al.  Cross-Industry Momentum , 2006 .

[18]  G. Schwert.  Why Does Stock Market Volatility Change Over Time? , 1988 .

[19]  Joëlle Miffre,et al.  Momentum Strategies in Commodity Futures Markets , 2006 .

[20]  Amir H. Alizadeh,et al.  A Markov regime switching approach for hedging energy commodities , 2008 .

[21]  Jiang Wang,et al.  A Model of Competitive Stock Trading Volume , 1994, Journal of Political Economy.

[22]  Christopher G. Lamoureux,et al.  Heteroskedasticity in Stock Return Data: Volume versus GARCH Effects , 1990 .

[23]  M. J. Klass,et al.  On the Estimation of Security Price Volatilities from Historical Data , 1980 .

[24]  Takatoshi Ito,et al.  Meteor Showers or Heat Waves? Heteroskedastic Intra-Daily Volatility in the Foreign Exchange Market , 1988 .

[25]  Mark Grinblatt,et al.  Do Industries Explain Momentum , 1999 .

[26]  Ronald W. Masulis,et al.  Correlations in Price Changes and Volatility Across International Stock Markets , 1990 .

[27]  Robert I. Webb.  Futures trading in less 'noisy' markets , 1995 .

[28]  Ben R. Marshall,et al.  Can Commodity Futures be Profitably Traded with Quantitative Market Timing Strategies? , 2007 .

[29]  H. Bessembinder,et al.  Price Volatility, Trading Volume, and Market Depth: Evidence from Futures Markets , 1993, Journal of Financial and Quantitative Analysis.

[30]  F. Klaassen,et al.  Foreign Exchange Market Contagion in the Asian Crisis: A Regression-Based Approach , 2005 .

[31]  J. Fung.  The Information Content of Option Implied Volatility Surrounding the 1997 Hong Kong Stock Market Crash , 2007 .

[32]  The relationship between volume and price variability in futures markets , 2000 .

[33]  S. Viswanathan,et al.  The Effect of Public Information and Competition on Trading Volume and Price Volatility , 1993 .

[34]  P. Clark.  A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices , 1973 .

[35]  Andrea Frazzini,et al.  Economic Links and Predictable Returns , 2007 .

[36]  W. Torous,et al.  Do industries lead stock markets , 2007 .

[37]  Return Volatility, Trading Imbalance and the Information Content of Volume , 2000 .

[38]  Yu Chuan Huang.  The market microstructure and relative performance of Taiwan stock index futures: a comparison of the Singapore exchange and the Taiwan futures exchange , 2004 .

[39]  G. Andrew Karolyi,et al.  A Multivariate GARCH Model of International Transmissions of Stock Returns and Volatility: The Case of the United States and Canada , 1995 .

[40]  Information content of volume: An investigation of Tokyo commodity futures markets , 2002 .

[41]  James D. Hamilton.  Time Series Analysis , 1994 .

[42]  Chu-Sheng Tai.  Looking for contagion in currency futures markets , 2003 .

[43]  A. Malliaris,et al.  Volume and price relationships: Hypotheses and testing for agricultural futures , 1998 .