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

机译：股指期货套期保值的样本外比较分析：动态套期保值与静态套期保值

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摘要

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.
机译：这项研究的目的是通过使用各种动态对冲策略和传统的静态对冲策略来检验包括DJIA，S＆P500，NASDAQ100，FTSE100，CAC40，DAX30和Nikkei225指数期货在内的主要国际股票指数期货的对冲表现。预期效用最大化和投资组合方差最小化的目标函数用于测量样本外数据的最佳套期比率和套期有效性。结果总结如下：（1）波动率规格检验结果表明，大多数股指和指数期货收益序列的第二时刻存在信息不对称性； （2）套期保值绩效的实证结果表明，研究中研究的大多数模型都可以大大改善投资者的预期效用或降低投资组合风险； （3）对比分析结果还表明，对于不同程度规避风险的投资者，误差校正（EC）模型优于其他模型。总体而言，经验结果表明，对于激进投资者而言，基于二元不对称Glosten-Jagannathan-Runkle误差校正-广义自回归条件异方差（GJR-EC-GARCH）模型的对冲策略将获得更好的对冲表现。对于保守的投资者，GJR-EC-GARCH模型和纠错-普通最小二乘（EC-OLS）模型都可以很好地表现。考虑交易成本后，结果保持不变。

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  • 来源
    《Applied financial economics》 |2009年第15期|1059-1072|共14页
  • 作者

    Ming Jing Yang; Yi-Chuan Lai;

  • 作者单位

    Department and Graduate Institute of Finance, Feng Chia University, Taichung, Taiwan, ROC;

    Department and Graduate Institute of Finance, Feng Chia University, Taichung, Taiwan, ROC;

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