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中国股市波动率的广义周内特征及其预测模型
引用本文:施雅丰,艾春荣.中国股市波动率的广义周内特征及其预测模型[J].系统工程理论与实践,2016,36(8):1918-1927.
作者姓名:施雅丰  艾春荣
作者单位:1. 宁波工程学院 理学院, 宁波 315211;2. 中国人民大学 统计与大数据研究院, 北京 100872;3. 上海财经大学 统计与管理学院, 上海 200433
基金项目:国家自然科学基金(71371118);国家自然科学基金重点项目(71331006);宁波工程学院博士科研启动基金
摘    要:本文将Realized GARCH模型推广至基于周历日的时变参数情形以刻画杠杆和溢出效应的周内特征并避免传统GARCH类模型在拟合长记忆性与周内效应时两者相互干扰问题.将新模型应用于上海股票市场2001至2013数据的分析发现:我国股市波动率存在时变的杠杆效应和溢出效应.实证结果表明:新模型无论在样本外的预测能力还是在样本内的拟合度上都明显优于现有模型.

关 键 词:波动率  周内效应  杠杆效应  溢出效应  高频数据  
收稿时间:2015-05-07

A volatility model for Chinese stock market with generalized day-of-the-week effect
SHI Yafeng,AI Chunrong.A volatility model for Chinese stock market with generalized day-of-the-week effect[J].Systems Engineering —Theory & Practice,2016,36(8):1918-1927.
Authors:SHI Yafeng  AI Chunrong
Institution:1. School of Science, Ningbo University of Technology, Ningbo 315211, China;2. Institute of Statistics and Big Data, Renmin University of China, Beijing 100872, China;3. School of Statistics and Management, Shanghai University ofFinance & Economics, Shanghai 200433, China
Abstract:This paper extends the realized GARCH model to allow time varying parameters for leverage effect and spillover effects. The extended model not only fit daily volatility well, but also distinguish the day-of-the-week effect from the long memory volatility. Fitting proposed model to a sample of high-frequency data from Shanghai Composite Index (SSEC) from 2001 to 2013, we find the volatility in Chinese stock market has the time varying leverage and spillover effects. Finally, both the in sample fitness and out of sample predictability to the existing models, we find that the proposed model compares favorably.
Keywords:volatility  day-of-the-week effect  leverage effect  spillover effect  high-frequency data  
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