基于C_TMPV的中国股市高频波动率的跳跃行为研究  

Empirical Research on Jump Behavior of High-Frequency Volatility Based on C_TMPV in Chinese Stock Markets

作　　者：杨科[1] 陈浪南[1] 

机构地区：[1]中山大学岭南学院,广州510275

出　　处：《管理科学》2011年第2期103-112,共10页Journal of Management Science

基　　金：国家自然科学基金(70673116);教育部人文社会科学重点研究基地重大项目(05JJD790075);国家社会科学基金(07BJY167);中山大学985工程产业与区域发展研究创新基地项目;广东省普通高校人文社会科学重点研究基地项目;广东省自然科学基金(9151027501000032)~~

摘　　要：运用2000年1月4日至2008年12月31日上证综指每5分钟的高频金融数据,采用核估计量估计中国股市高频波动率序列,运用修正的已实现门阀多次幂变差估计中国股市高频波动率的跳跃序列,实证分析中国股市高频波动率跳跃的各种特征,并运用ACD模型、ACH模型以及扩展的ACH模型进一步分析中国股市高频波动率跳跃的持续期的特征。研究结果表明,中国股市高频波动率及其跳跃都具有集聚的特征,高频波动率发生显著跳跃的比例相当高,高频波动率跳跃的幅度、强度和跳跃幅度的分布都具有时变性,而跳跃对高频波动率的贡献却具有相对稳定性;在样本期,中国股市高频波动率跳跃表现出较强的正相关性,且跳跃的持续期存在较强的长记忆性和周日历效应。Based on the every 5 minutes high-frequency financial data of SSEC from 2000 to 2008. We utilized kernel-based estimator to estimate the high-frequency volatility, and corrected realized threshold muhipower variation（ C_TMPV ）to investigate the jump dynamics of volatility and the ACD model and the ACH model to analyze the features of jump duration. The results that both show the volatility and jump have agglomeration effect, the proportion of the trade day with the volatility jump is very high, and the size, the intensity and distribution of jumps in Chinese stock market are time-varying, but the jump＇s contribution to high-frequency volatility is relatively stable. In addition, during our analytical term of sample, the jumps of high-frequency volatility exhibit strong positive correlations, and the jump durations have the long term memory and weekday calendar effects.

关 键 词：高频波动率 跳跃 修正的已实现门阀多次幂变差 C_TZ统计量 

分 类 号：F830.91[经济管理—金融学]