多元GARCH建模及其在中国股市分析中的应用

Multivariate GARCH modeling and its application in volatility analysis of Chinese stock markets

简要回顾了一元ARCH类模型的发展过程,介绍了多元GARCH类模型的四种形式.针对传统基于梯度信息的多元GARCH模型估计方法的不足,提出了基于遗传算法的似然估计方法,并利用中国股市数据进行了实证研究.结果说明中国股市存在着波动的持续性和显著的二元GARCH效应,并且沪、深股市不存在协同持续性.

This paper first briefly reviews the evolvement of univariate ARCH class models, and introduces several multivariate GARCH class models. Considering the shortage of traditional estimation methods for multivariate GARCH based on gradient information, we give out the likelihood estimating method based on genetic algorithm. Finally, the paper presents the demonstration of Chinese stock markets: Both Shanghai and Shenzhen stock markets show volatility persistence in variance. When combined together, the two markets show obvious bivariate GARCH effect, and there is no common persistence in Shanghai and Shenzhen stock markets.