中文摘要：

为避免参数法和半参数法的事前模型设定偏误和参数估计误差,本文引入无需做事前分布假设的非参数核估计法对VaR和CVaR进行估计,并基于VaR和CVaR的核估计量构建风险对冲模型,实现风险估计和风险对冲同时进行。在此基础上,本文进一步在核估计框架内引入期望效用理论比较最小方差、最小VaR和最小CVaR对冲策略的对冲效率,以期解决传统文献将风险下降比率作为风险对冲效率指标,却因风险度量指标不同而导致比较结果不一致的问题。最后,将核估计框架下的风险对冲模型和期望效用理论运用到沪深300股指期货现货的风险对冲问题,实证结果表明：最小CVaR对冲策略的对冲效率优于最小方差和最小VaR对冲策略,且四种效用函数给出的比较结果一致。

英文摘要：

Value-at-Risk （VaR） and Conditional Value-at-Risk （CVaR） are two main popular risk measurement tools presently. However, the present study on risk hedging problem with VaR （CVaR） is mostly carried out under specific distribution assumptions, which is prone to resulting in model risk and limiting its scope of application in practice. In addition, the traditional literature only defines risk reducing ratio as the risk hedging efficiency index, but different risk measure indices often induce inconsistent or even contradictory results. Therefore we need to seek a hedging efficiency index which is independent of risk measure indices. Method.To overcome the shortcomings above and improve existing results, the nonparametric kernel estimation method is introduced to estimate VaR and CVaR without a distribution assumption, and then the risk hedging model is constructed based on the VaR and CVaR kernel estimators, which can avoid the ex-ante model risk and parametric estimation error. In addition, expected utility theory is further applied to compare the hedging efficiency of risk-minimizing hedging strategies so as to avoid the inconsistent or even contradictory comparison results that are often induced by different risk decline ratios in traditional literatures. Data：The historical data of CSI 300 stock index and its futures is collected to test the theorem above. The data window ranges from April 16, 2010 to February 11, 2015, a total of 1172 daily data. Its. Th utility f s more e empirical results based on CSI 300 index futures and spot market data show that four unctions used by financial economics confirm consistent efficient than the minimum variance and the minimum V ly that minimum CVaR hedging aR hedging strategies. Future research. The purpose of this paper is to provide a research framework which studies hedging problem under distribution uncertainty using kernel estimation method and expected utility theory. The research framework can be easily applied to hedging efficiency problem of other de