The Conditional Dependence Analysis Based on Copula-EGARCHKernel Density Estimation Model

摘要：

In this paper, kernel density estimation method is used to improve Copula-EGARCH-GED model, this new model is named as Copula-EGARCH-kernel density estimation model. We make conditional dependence analysis for the Shanghai and Shenzhen Stock market index, the results show that this new model is an effective tool for conditional dependence analysis in Chinese stock markets.

关键词： Copula-EGARCH model kernel density estimation conditional dependence

作者: LI Weizhen LI Shushan HOU Fei

作者单位: College of Information Science and Engineering, SUST, Qingdao, Shandong, P.R.China, 266510

会议类型: 国际会议

会议名称: The 3rd International Institute of Statistics & Management Engineering Symposium(2010 国际统计与管理工程研讨会 IISMES)

会议地点: 威海

会议语种:英文

页码: 574-578

在线出版日期: 2010-07-24（万方平台首次上网日期，不代表论文的发表时间）