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中文摘要：
条件风险价值（CVaR）是近几年发展起来的金融风险量化工具．构建基于CVaR核估计量的风险优化和风险对冲模型，并设计数值算法对其进行求解，实现金融风险的估计与风险的优化同时进行．中国A股市场历史数据的算例分析说明，非参数核估计方法能够捕捉风险因子分布的尾部特征，给出更为准确的风险估计结果；基于CVaR核估计量的风险优化模型能够找到真实的最小风险组合和最小风险值；相对于CVaR估计的方差协方差法和Cornish-Fisher展开，基于CVaR核估计量的风险对冲效果最佳．
英文摘要：
Conditional Value-at-Risk （CVaR） model developed recently is a powerful mathematical tool to measure financial risk. By constructing on the risk optimization and risk hedging models based on the CVaR kernel estimator and designing an optimization algorithm to solve these models, this paper accomplishes the goal that financial risk estimation and risk management are implemented at the same time. These models are applied to Chinese A stock market, and the following conclusions are obtained： nonparametric kernel estima tion method can capture the tail feature of the risk factor distribution and give more accurate risk estimation re suits. The risk optimization model based on CVaR kernel estimator can find out the true minimum risk and corresponding portfolio. Compared with Varianee-Covariance method and Cornish-Fisher expansion of CVaR, the risk hedging effect based on CVaR kernel estimator is the best.
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