重庆理工大学学报（自然科学版）2016，Vol.30Issue(5)：119-124,6.DOI:10.3969/j.issn.1674-8425(z).2016.05.021
基于EGARCH-GPD模型的沪深300指数的VaR度量
VaR Forecasting for CSI 300 Index Based on EGARCH-GPD Model
摘要
Abstract
This paper constructed a new EGARCH-GPD model combined the classical EGARCH model with extreme value theory based on generalized Pareto distribution,and provided the dynamic estimation problems of VaR.The results of VaR back testing on CSI 300 index show that compared with the EGARCH model based on normalized residual,the new model can effectively describe the time-varying volatility and the ‘fat tail’of financial data,hence increase the prediction accuracy of VaR in a certain extent.关键词
EGARCH模型/广义Pareto分布/POT/VaR/Kupiec失败率检验Key words
EGARCH model/generalized Pareto distribution/POT/VaR/Kupiec proportion of failures test分类
数理科学引用本文复制引用
魏正元,李娟,罗云峰..基于EGARCH-GPD模型的沪深300指数的VaR度量[J].重庆理工大学学报（自然科学版）,2016,30(5):119-124,6.基金项目
重庆市自然科学基金资助项目（cstc2012jjA00018）；重庆市教委科学技术研究项目 （）
