Abstract
VaR (在险价值)和CVaR (条件在险价值)是常用的金融产品风险度量工具。本文考虑沪深300、中证500和不同行业的12只股票最近两年(2014.10~2016.9)的历史数据，首先用非参数估计方法计算了相应的VaR和CVaR值，然后结合Bootstrap抽样数据，重新估计了相应股票的VaR和CVaR值，根据似然比检验得出结论：Bootstrap方法可以提高VaR和CVaR的估计的精度，更加有效地衡量股票的风险。 VaR and CVaR are used to measure risk of financial products. In this paper, based on the historical data of recent two years (Oct. 2014~Sep. 2016) of the CSI300, CSI500 and 12 stocks from different industries, at first we compute VaR and CVaR by using nonparametric estimation method. Then, by using Bootstrap method, we recalculate the values of VaR and CVaR. According to the likelihood ratio test, Bootstrap method can improve the estimation precision of VaR and CVaR and then measure the risk more effectively.
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