An out-of-sample comparative analysis of hedging performance of stock index futures: dynamic versus static hedging

The purpose of this study is to examine the hedging performance of the major international stock index futures, including DJIA, S&P500, NASDAQ100, FTSE100, CAC40, DAX30 and Nikkei225 index futures, by using the various dynamic hedging strategies and the traditional static hedging strategies. The objective functions of the expected utility maximization and portfolio variance minimization were employed to measure the optimal hedge ratios and hedging effectiveness for the out-of-sample data. The results are summarized as follows: (1) The volatility specification test results indicate that information asymmetry exists in the second moments of most stock index and index futures return series; (2) The empirical results of hedging performance demonstrate that most of the models examined in the study can substantially improve investors' expected utility or reduce portfolio risk; (3) The comparative analysis results also reveal that the Error Correction (EC) models are superior to the other models for investors with different degrees of risk aversion. Overall, the empirical findings suggest that for aggressive investors, the hedging strategies based on the bivariate asymmetric Glosten-Jagannathan-Runkle-Error Correction-Generalized Autoregressive Conditional Heteroscedastic (GJR-EC-GARCH) model would achieve the better hedging performance. As for conservative investors, both the GJR-EC-GARCH and Error Correction-Ordinary Least Square (EC-OLS) models can perform very well. The results remain the same after considering the transaction costs.