沪深300股指及期货波动率聚集性研究——基于Markov机制转换SJC Copula模型  

Volatility clustering of CSI 300 stock index and futures based on Markov regime-switching SJC copula model

作　　者：罗华健 邹玉梅 LUO Huajian;ZOU Yumei(College of Mathematics and Systems Science,Shandong University of Science and Technology,Qingdao,Shandong 266590,China)

机构地区：[1]山东科技大学数学与系统科学学院,山东青岛266590

出　　处：《山东科技大学学报（自然科学版）》2021年第1期71-81,共11页Journal of Shandong University of Science and Technology(Natural Science)

基　　金：国家自然科学基金项目(11801322);山东省自然科学基金项目(ZR2018MA011)。

摘　　要：选取沪深300指数及沪深300股指期货当月连续合约日内5 min高频交易数据为研究对象,研究我国股票期现货市场波动率聚集性及尾部动态特征。结果显示:三状态Markov机制转换SJC Copula模型比其他模型能更好地刻画波动率的相关性结构,沪深300指数和股指期货波动率的尾部均存在明显非对称特征及动态特征,上尾相关性均大于下尾相关性,股指期货的高波动率聚集比沪深300指数高波动率聚集具有更高的发生概率,而低波动率聚集具有相反的发生概率。Taking high-frequency trading data of the CSI 300 index and CSI 300 stock index futures for 5 minutes within the consecutive contract days of the same month as the research object,this paper studies the volatility clustering and tail dynamic characteristics of Chinese stock futures and spot markets.The results show that the three-state Markov regime-switching SJC Copula model can better describe the correlation structure of volatility than other models.Both CSI 300 index and CSI 300 stock index futures have obvious asymmetric and dynamic tail volatility.The upper tail correlation is greater than the lower tail correlation.The probability of high volatility clustering in stock index futures is higher than that in CSI 300 index.But the probability of low volatility clustering is just the opposite.

关 键 词：Markov机制转换 SJC Copula函数 尾部相关性 波动率聚集性 

分 类 号：F830[经济管理—金融学]