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.