作　　者： ;

机构地区： 华南理工大学

出　　处： 《管理工程学报》 2013年第2期129-136,共8页

摘　　要： 本文在考察上证综指和深证成指日已实现波动率的长记忆性特征和结构突变的基础上构建了已实现波动率自回归结构突变模型,并用于进行预测。预测结果表明,在未来结构突变已知情形下,自回归结构突变模型的预测效果比其他预测模型要好,在未来结构突变未知情形下,该模型的预测效果比ABDL模型稍微差,而ABDL模型在两种情形下都是比较稳健的预测模型。 The realized volatility method based on the high frequency of financial data has obtained the widespread application in the financial domain and been developed into a brand-new research area of financial econometrics. In comparison with GARCH model and SV model, the computation of realized volatility method is convenient and does not need complex parameter estimation. In addition, it is an unbiased estimator of real volatility under certain conditions. However, the research on the high-frequency of volatility forecasts in domestic market is still in its infancy. Although the vast majority of studies are to apply the model, they rarely take into account the various features of high-frequency volatility in Chinese stock markets. In this paper, we focus on the possibility of structural breaks, trends and long memory in the daily realized volatility series in Chinese stock markets usinghigh-frequency data from Shanghai Composite Index and Shenzhen Component Index during 2000-2010. First, we use local whittle method, ARFIMA model and SEMIFAR model to test long memory and estimate long memory models for the realized volatility series in Shanghai Composite Index and Shenzhen Component Index. We find strong evidence for long memory in these realized volatility series. Second, we use Bai and Perron＇ s （1998, 2003） methods to test and estimate a multiple mean break model. We find that there are several common structural breaks in these realized volatility series n Shanghai Composite Index and Shenzhen Component Index, and there is no smooth flexible trend in these series by using SEMIFAR models. Third, we test long memory in the structural break adjusted data. We find that there is a partial reduction of long memory in these realized volatility series in Shanghai Composite Index and Shenzhen Component Index after adjusting structural break. This evidence suggests that the presence of structural breaks in the daily realized volatility series in Chinese stock markets can account for parts of the observed long memory.

关 键 词： 已实现波动率 预测 长记忆性 结构突变

分 类 号： [F830]

领　　域： []