混合数据抽样波动模型  

Mixed Data Sampling Volatility Model

作　　者：徐剑刚[1] 张晓蓉[1] 唐国兴[1] 

机构地区：[1]复旦大学管理学院

出　　处：《数量经济技术经济研究》2007年第11期77-85,共9页Journal of Quantitative & Technological Economics

基　　金：国家自然科学基金项目(70573025)资助

摘　　要：混合数据抽样(MIDAS)模型能处理不同频率变量间的动态关系。本文比较了MIDAS波动模型和ABDL模型,发现我国股市对数实现波动呈长期记忆性。在预测波动方面,ABDL模型优于MIDAS模型;利用MIDAS波动模型预测实现波动水平,日绝对值报酬是最好的回归项;利用MIDAS模型预测未来波动,至少应采用一个月的历史数据。This paper compares MIDAS （Mixed Data Sampling） regression models and ABDL model in the ability of predicting volatility. MIDAS models can take different specification of regressors, such as realized volatility, realized power, squared returns, absolute returns, and return ranges. Using 5-minute stock return data in Shanghai and Shenzhen Stock Exchange, we find MIDAS models do not excel ABDL model in pre- dicting volatility. However, measured by increments in quadratic variation, daily absolute return is the best predictor in MIDAS models. Furthermore, historical data of one month are sufficient to capture the persistence in volatility.

关 键 词：MIDAS波动模型 ABDL模型 BETA函数 

分 类 号：F224.0[经济管理—国民经济]