中文摘要：

本文运用非参数核估计方法对资产组合的在险价值（Value at Risk，VaR）进行估计，得到VaR的非参数核估计公式，并基于VaR的非参数核估计公式建立投资组合选择模型。理论上该模型的目标函数具有良好的光滑性，便于优化问题求解。Monte Carlo模拟结果表明该模型具有大样本性质，估计误差会随着样本容量的增大而下降，且该模型在非对称和厚尾分布下的表现优于当前文献中常用的经验分布法和Cornish-Fisher展开法。基于我国上证50指数及其成份股实际数据的实证结果说明该模型是有效的。

英文摘要：

Value at Risk （VaR）, which is widely used by fund companies, banks, securities firms and financial supervision institution, is one of the most popular risk measurement tools presently. The estimation methods of VaR and portfolio optimization models with VaR have been one of the hot spots in recent years. Since VaR is not a convex risk measure, it is difficult to obtain the global optimal solution of portfolio selection problems based on VaR. Moreover, the present study on portfolio selection with VaR is mostly carried out under normal or ellipsoidal distribution assumptions, which is not consistent with the reality of financial markets. In this paper, nonparametric kernel estimation method is firstly applied to estimate VaR and a nonparametric kernel estimator for asset portfolio＇s value at risk （VaR） is gotten with distribution-free specification. Then kernel estimator of VaR is embedded into the mean-VaR portfolio selection models and accomplish the goal that financial risk estimation and portfolio optimization are implemented at the same time. It is easy to show that the objective function of our model is smooth theoretically and easy to solve the optimization problem. Monte Carlo simulations are carried out to compare the accuracy of our method with the accuracy of classical methods. The simulation results show that our model possesses large sample properties, and outperforms empirical distribution method and Cornish-Fisher expansion method which are usually applied in the classical literatures under the asymmetric and thick tail distribution setting. Finally, our models and methods are applied to the Chinese A stock market. The daily data of SSE 50 Index and its constituent stocks are collected. The data window ranges from January 2nd 2004 to July 8th 2016, with a total of 3040 daily data. The empirical results show that our model can effectively control risk, as well as obtain excess returns relative to the stock index and support effectiveness of our model and application value of this research.