多元GARCH 建模及其在中国股市分析中的应用

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.

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

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. Fi2 nally , 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.