The accuracy and time scale invariance of value-at-risk （VaR） measurement methods for different stock indices and at different confidence levels are tested. Extreme value theory （EVT） is applied to model the extre...The accuracy and time scale invariance of value-at-risk （VaR） measurement methods for different stock indices and at different confidence levels are tested. Extreme value theory （EVT） is applied to model the extreme tail of standardized residual series of daily/weekly indices losses, and parametric and nonparametric methods are used to estimate parameters of the general Pareto distribution （GPD）, and dynamic VaR for indices of three stock markets in China. The accuracy and time scale invariance of risk measurement methods through back-testing approach are also examined. Results show that not all the indices accept time scale invariance; there are some differences in accuracy between different indices at various confidence levels. The most powerful dynamic VaR estimation methods are EVT-GJR-Hill at 97.5% level for weekly loss to Shanghai stock market, and EVT-GARCH-MLE （Hill） at 99.0% level for weekly loss to Taiwan and Hong Kong stock markets, respectively.展开更多
以风险价值(value at risk,VaR)为金融风险度量,结合Copula函数及其相关函数建立金融风险模型.考虑到金融时间序列的时变性和厚尾特性,根据GARCH(generalized autoregressive conditional heteroscedasticity)模型和极值理论的POT(peak ...以风险价值(value at risk,VaR)为金融风险度量,结合Copula函数及其相关函数建立金融风险模型.考虑到金融时间序列的时变性和厚尾特性,根据GARCH(generalized autoregressive conditional heteroscedasticity)模型和极值理论的POT(peak over threshold)模型,运用Copula方法来估计VaR的值.给出实例验证,将上述方法用于刻画美国纳斯达克指数和标准普尔指数的相关性,并计算了等权重下资产组合的VaR估计值.结果表明:VaR估计值的大小与所取的置信水平以及持有期有关;t-Copula和Clayton Copula方法较其他方法能更好地捕捉资产组合的相关关系,从而可以得到更好的VaR估计值.展开更多
基金The National Natural Science Foundation of China (No70501025 & 70572089)
文摘The accuracy and time scale invariance of value-at-risk （VaR） measurement methods for different stock indices and at different confidence levels are tested. Extreme value theory （EVT） is applied to model the extreme tail of standardized residual series of daily/weekly indices losses, and parametric and nonparametric methods are used to estimate parameters of the general Pareto distribution （GPD）, and dynamic VaR for indices of three stock markets in China. The accuracy and time scale invariance of risk measurement methods through back-testing approach are also examined. Results show that not all the indices accept time scale invariance; there are some differences in accuracy between different indices at various confidence levels. The most powerful dynamic VaR estimation methods are EVT-GJR-Hill at 97.5% level for weekly loss to Shanghai stock market, and EVT-GARCH-MLE （Hill） at 99.0% level for weekly loss to Taiwan and Hong Kong stock markets, respectively.
文摘以风险价值(value at risk,VaR)为金融风险度量,结合Copula函数及其相关函数建立金融风险模型.考虑到金融时间序列的时变性和厚尾特性,根据GARCH(generalized autoregressive conditional heteroscedasticity)模型和极值理论的POT(peak over threshold)模型,运用Copula方法来估计VaR的值.给出实例验证,将上述方法用于刻画美国纳斯达克指数和标准普尔指数的相关性,并计算了等权重下资产组合的VaR估计值.结果表明:VaR估计值的大小与所取的置信水平以及持有期有关;t-Copula和Clayton Copula方法较其他方法能更好地捕捉资产组合的相关关系,从而可以得到更好的VaR估计值.
