我国沪市波动聚集性GARCH效应的实证研究  

The Empirical Research of the Volatility Clustering of the GARCH Effect of the Shanghai Exchanges in China

作　　者：皮天雷[1] 

机构地区：[1]西南财经大学中国金融研究所,四川成都610074

出　　处：《管理科学》2003年第6期31-35,共5页Journal of Management Science

摘　　要：股票价格的频繁波动是股票市场最明显的特征之一,自回归条件异方差类模型可以很好地预测金融资产收益率的方差。通过对我国股价指数的统计描述,表明我国金融资产收益率存在自回归条件异方差的特征,并表现出非正态性。利用自回归条件异方差类模型,采用1993年～2003年的数据对上证指数的波动进行拟合,结果表明,广义自回归条件异方差模型对我国股市波动具有较好的拟合效果。Frequent volatility is a feature of stock market. Autoregressive Conditional Heteroscedasticity (ARCH) model is often used to forecast the variance of the benefit of financial capitals. Statistic descriptions indicate that the benefit of finance capitals in China has the characteristic of autoregressive conditional hetero -skedasticity and abnormality. The ARCH models by Eviews are adopted in this paper to forecast the variance of the benefit of financial capitals from 1993 to 2003. Results obtained show that the Generalized Autoregressive Conditional Heteroscedasticity( GARCH) model is a better model for frequent volatility of stock market in Chi-

关 键 词：股市波动 聚集性 自回归条件异方差模型 广义自回归条件异方差模型 

分 类 号：F830.91[经济管理—金融学]