Dynamic Hedging with Futures: A Copula-Based GARCH Model

In a number of earlier studies it has been demonstrated that the traditional regression‐based static approach is inappropriate for hedging with futures, with the result that a variety of alternative dynamic hedging strategies have emerged. In this study the authors propose a class of new copula‐based GARCH models for the estimation of the optimal hedge ratio and compare their effectiveness with that of other hedging models, including the conventional static, the constant conditional correlation (CCC) GARCH, and the dynamic conditional correlation (DCC) GARCH models. With regard to the reduction of variance in the returns of hedged portfolios, the empirical results show that in both the in‐sample and out‐of‐sample tests, with full flexibility in the distribution specifications, the copula‐based GARCH models perform more effectively than other dynamic hedging models. © 2008 Wiley Periodicals, Inc. Jrl Fut Mark 28:1095–1116, 2008

[1]  B. Hansen Autoregressive Conditional Density Estimation , 1994 .

[2]  R. Myers Estimating timevarying optimal hedge ratios on futures markets , 1991 .

[3]  Stephen Figlewski,et al.  Estimation of the Optimal Futures Hedge , 1988 .

[4]  Taufiq Choudhry Short-run deviations and optimal hedge ratio: evidence from stock futures , 2003 .

[5]  Andrew Ang,et al.  Asymmetric Correlations of Equity Portfolios , 2001 .

[6]  W. Tong,et al.  An examination of dynamic hedging , 1996 .

[7]  Andrew J. Patton Modelling Asymmetric Exchange Rate Dependence , 2006 .

[8]  Pedro J. F de Lima,et al.  Nonlinearities and Nonstationarities in Stock Returns , 1998 .

[9]  L. Ederington,et al.  The Hedging Performance of the New Futures Markets , 1979 .

[10]  R. Engle Dynamic Conditional Correlation , 2002 .

[11]  Ling Hu Dependence patterns across financial markets: a mixed copula approach , 2006 .

[12]  K. Kroner,et al.  Time-Varying Distributions and Dynamic Hedging with Foreign Currency Futures , 1993, Journal of Financial and Quantitative Analysis.

[13]  Stephen L Taylor,et al.  The Euro and European Financial Market Integration , 2004 .

[14]  Chris Brooks,et al.  The Cross‐Currency Hedging Performance of Implied Versus Statistical Forecasting Models , 2001 .

[15]  Robert J. Myers,et al.  Bivariate garch estimation of the optimal commodity futures Hedge , 1991 .

[16]  E. Luciano,et al.  Copula methods in finance , 2004 .

[17]  E. Luciano,et al.  Copula Methods in Finance: Cherubini/Copula , 2004 .

[18]  T. Bollerslev,et al.  Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model , 1990 .

[19]  R. Nelsen An Introduction to Copulas , 1998 .

[20]  H. Joe Multivariate models and dependence concepts , 1998 .

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

[22]  Yannick Malevergne,et al.  Testing the Gaussian copula hypothesis for financial assets dependences , 2001, cond-mat/0111310.

[23]  Stephen Figlewski,et al.  Hedging Performance and Basis Risk in Stock Index Futures , 1984 .

[24]  Bruce A. Benet Hedge period length and Ex‐ante futures hedging effectiveness: The case of foreign‐exchange risk cross hedges , 1992 .

[25]  Lorne N. Switzer,et al.  Bivariate GARCH estimation of the optimal hedge ratios for stock index futures: A note , 1995 .

[26]  F. Longin,et al.  Extreme Correlation of International Equity Markets , 2000 .

[27]  L. Glosten,et al.  On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks , 1993 .

[28]  Andrew J. Patton Estimation of multivariate models for time series of possibly different lengths , 2006 .

[29]  Söhnke M. Bartram,et al.  The Euro and European Financial Market Dependence , 2007 .