基于CVaR两步核估计量的投资组合管理  

Investment portfolio management based on the two-step kernel estimator of CVaR

作　　者：黄金波[1] 李仲飞[2] 姚海祥[3] 

机构地区：[1]广东财经大学金融学院,广州510320 [2]中山大学管理学院,广州510275 [3]广东外语外贸大学金融学院,广州510006

出　　处：《管理科学学报》2016年第5期114-126,共13页Journal of Management Sciences in China

基　　金：国家自然科学基金重点资助项目(71231008);国家自然科学基金资助项目(71471045);中国博士后科学基金特别资助项目(2015T80896);中国博士后科学基金资助项目(2014M562246;2014M560658);全国统计科学研究计划资助项目(2013LY101);广东省自然科学基金研究团队资助项目(2014A030312003);广东省高等学校高层次人才资助项目;广东省高等院校科技创新资助项目(2012KJCX0050);广东省普通高校特色创新资助项目(人文社科类);广州市哲学社会科学规划资助项目(14G42)

摘　　要：在不做任何分布假设的条件下,利用非参数核估计方法对风险度量条件风险价值(conditional value-at-risk,CVaR)进行估计,得到CVaR的两步核估计公式.然后用估计出来的CVaR代替理论上的CVaR建立均值-CVaR模型,实现对风险估计与投资组合优化同时进行,并基于迭代思想设计求解该模型的简单算法.蒙特卡洛模拟结果表明基于两步核估计方法的投资组合优化模型和算法比现有的方法更加有效,估计出来的组合边界误差更小.引入无风险资产后,文中的模型和算法同样适用.最后,为说明其应用价值,采用中国A股市场的日收益率数据进行了实例分析.The paper first applies nonparametric kernel estimation method to estimating CVaR which is currently a popular risk measurement tool, then derives a two-step kernel estimator of CVaR with distribution-free specification. Next, a two-step kernel estimator of CVaR is embed into the mean-CVaR portfolio optimization models to derive financial risk estimation and portfolio optimization at the same time. A simple iterative algorithm is designed to solve these models. Monte Carlo simulation result shows that the portfolio optimization models and the algorithm based on the two-step kernel estimator of CVaR is feasible and effective, and that the estimated error of portfolio frontier is very small. The models and algorithm above apply to a risk-free security. Finally, an empirical analysis of daily return data from Chinese A-stock market is presented to illustrate the application of this research.

关 键 词：均值-CVAR模型 两步核估计量 组合边界 中国A股市场 

分 类 号：F830.9[经济管理—金融学] O212.7[理学—概率论与数理统计]