The Conditional Dependence Analysis Based on Copula-EGARCH-Kernel Density Estimation Model

被引：0
|
作者：
Li Weizhen [1 ]
Li Shushan [1 ]
Hou Fei [1 ]
机构：
[1] SUST, Coll Informat Sci & Engn, Qingdao 266510, Shandong, Peoples R China
来源：
DATA PROCESSING AND QUANTITATIVE ECONOMY MODELING | 2010年
关键词：
Copula-EGARCH model; Kernel density estimation; Conditional dependence;
D O I：
暂无
中图分类号：
C93 [管理学]; O22 [运筹学];
学科分类号：
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
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.
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页码：574 / 578
页数：5
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