摘要
本文提出非参数核密度估计-ML方法来估计Copula函数中的未知参数;再由统计检验推断得到能较好描述金融资产之间非线性相关结构的Copula。实证分析表明:可以利用Clayton Copula、Gumbel Copula来描述A股市场上证指数与深证成指之间的非线性相关结构.
In the paper, we set up a new method named as Nonparametric Kernel Density Estimation -ML method to get estimators of copulas. Then through statistical testing, we can get a suitable copula to measure the nonlinear dependent structure among financial assets. The empirical results shows that Clayton Copula and Gumbel Copula are better to descriptive the dependence between Shanghai and Shenzhen stock markets.
出处
《数理统计与管理》
CSSCI
北大核心
2009年第1期64-68,共5页
Journal of Applied Statistics and Management
基金
国家社科基金项目(07BTJ003)资助．
关键词
COPULA
参数估计
非参数核密度估计
copula, parametric estimation, nonparametric kernel density estimation
