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针对传统孤立使用GJR模型、极值理论、Copula理论进行风险分析的不足,把GJR模型、极值理论和Copula理论有机的结合起来,给出了基于Copula和极值理论的投资组合VaR的测度方法.首先利用GJR模型刻画单个资产收益率中的自相关和异方差现象,获得近似独立同分布的新息序列,再分别应用高斯核估计的方法、极值理论拟合新息序列的分布函数的内部和两尾,利用Copula函数有效捕抓了市场之间的波动溢出效应,最后使用Monte Carlo模拟法,计算出投资组合的VaR值.实证结果表明,基于Copula和极值理论的VaR度量方法比历史模拟法更有效.
Aiming at the shortcomings of traditionally using GJR model, extremum theory and Copula theory to conduct risk analysis, GVR model, extremum theory and Copula theory are organically combined to give a measure of portfolio VaR based on Copula and extreme value theory Firstly, the GJR model is used to characterize the autocorrelation and heteroscedasticity in the returns of individual assets, and the approximate independent and identically distributed new interest sequences are obtained. Gaussian kernel estimation is applied respectively, and the extreme value theory is used to fit the distribution function of interest rates Internal and two-tail, effectively capture the volatility spillover effect between markets by using Copula function, and finally calculate the VaR of the portfolio using Monte Carlo simulation.The empirical results show that the VaR method based on Copula and extreme value theory History simulation method is more effective.