不對稱變幅條件相關係數模型之經濟價值分析

Abstract

本篇論文根據Engle(2002)提出的動態條件相關係數(Dynamic Conditional Correlation, DCC)模型與Cappiello et al.(2006)提出的不對稱動態條件相關係數(Asymmetric Dynamic Conditional Correlation, ADCC)模型配合GARCH、GJR-GARCH與CARR波動模型，利用標準普爾500(S&P 500)指數期貨與美國十年期公債(10-year T-bond)期貨來估計波動時變性的經濟價值。在本文的實證分析上，支持以變幅(range)為基礎的估計模型得到較高的經濟價值，若從投資者的角度來看，投資者願意支付較高的轉換費用使用CARR計量模型，以最適化資產配置。實證結果也支持以變幅當作較佳的波動代理變數。

This paper employs the return-based (GARCH and GJR-GARCH) and range-based (CARR) volatility models to go with the symmetric dynamic correlation (DCC) and asymmetric dynamic correlation (ADCC) model. We apply these models to measure the economic value of volatility in a mean-variance framework with three assets – stock, bond, and cash. Under consideration of asymmetric effect on conditional variance and correlation, we find that the CARR-DCC and CARR-ADCC models are superior in the different target returns and risk aversions. From the viewpoints of the investors, it is shown that the predictable ability captured by the dynamic volatility models is economically significant, and investors may choose the CARR model to allocate their assets and optimize their portfolio. The empirical results give robust inferences for supporting the range-based model in forecasting volatility.