本文以美國道瓊工業指數之股價指數期貨與現貨為主要研究對象，研究期間取自2001年1月1日至2009年12月31日止。運用不同避險績效的衡量方法，包括變異數(Variance) 、半變異數(semi-variance) 與效用函數(Utility function)來評估Naive、OLS、CCC-GARCH、DCC-GARCH、EWMA、Hybrid EWMA等避險模型之樣本外避險績效。在周雨田(2005)文獻中探討以變幅為基礎之避險模型優於以報酬為基礎之避險模型，本文試著重新檢視以變幅為基礎之Hybrid EWMA模型優於以報酬為基礎之EWMA模型。實證結果發現:1.本文將Hybrid EWMA做敏感度分析，實証發現λ=0.98之避險績較佳。2.本文利用六大避險模型及三種避險績效評估道瓊股價指數期、現貨樣本外期間之避險績效，整體而言以Hybrid EWMA避險模型效果較佳。3.若僅比較EWMA及Hybrid EWMA模型，用Hybrid EWMA 模型做為波動性預測指標的動態模型，比EWMA 模型做為波動性預測指標的動態模型估計更準確。 Building on the earlier results of Parkinson (1980) and Garman and Klass (1980)show that intraday range is more efficient than the squared return. Chou (2005) develops a conditional autoregressive range(CARR) estimator, the range-based GARCH estimators generate more accurate volatility forecasts than the return-based model. There are a number of well-established approaches to estimating the variance-covariance matrix, including the EWMA and GARCH model. Hybrid EWMA offers an improvement over the standard EWMA estimator, in terms of forecasting accuracy and yielding superior hedge performance.
