基于等熵一致性风险测度的组合选择  

Portfolio selection based on relative entropy coherent risk measure

作　　者：郑承利[1] 陈燕[2] 

机构地区：[1]华中师范大学经济与管理学院,武汉430079 [2]华中师范大学数学与统计学学院,武汉430079

出　　处：《系统工程理论与实践》2014年第3期648-655,共8页Systems Engineering-Theory & Practice

基　　金：国家自然科学基金(71171095)

摘　　要：本文基于一种新的一致性风险测度——等熵风险测度,进行组合优化,以检验其择股能力,从而检验其风险识别能力.先就风险识别能力,尤其随机占优一致性对三种基于分位数的风险测度:VaR,ES(expected shortfall)和等熵风险测度进行了介绍与对比.VaR具有一阶随机占优一致性,而ES具有二阶随机占优一致性;等熵风险测度利用了整个分布的信息,不再是简单的0-1风险测度,这与VaR和ES显著不同.而且,等熵风险测度具有更高阶的随机占优一致性,这使得该风险测度具有更好的风险分辨能力.而后采用Spearman秩检验方法来检验和预测不同风险测度的风险识别能力,这与随机占优一致性阶数相呼应.最后,在上证50指数成份股中采用组合优化方法,考察标准差,VaR,ES以及等熵风险测度情况下,优化组合持有期的不同业绩指标.结果表明,等熵风险测度优化组合的业绩指标最好,表明该测度风险识别能力最高.This article optimizes stocks portfolio through a new proposed coherent risk nleasure called relative-entropic risk measure. And then the ability of the optimization model is tested, which is the test of the discrimination for risk of the risk measure. Firstly, it introduces the consistence with stochastic dominances for three kinds of quantile-based risk measures： VaR, ES and relative-entropic risk measure. It is pointed that ES only utilizes local information as VaR, and is consistent with stochastic dominances lower than second-order. However, relative-entropic risk measure utilizes the whole information to measure the risk, and is consistent with stochastic dominances of higher orders. So, it is most powerful for discrimination of risk. Then, Spearman rank test is used to test the discrimination for risk of tile risk measure. Lastly, four kinds of portfolio optimization models based on standard error, VaR, ES and relative-entropic risk measure arc used in Chinese markets. The results turn out that all the performance indexes of portfolios optimized by relative-entropic risk measure are the best, which reveals that it has the highest discrimination of risk in the three risk measures.

关 键 词：组合选择 等熵风险测度 风险识别能力 随机占优一致 

分 类 号：F830[经济管理—金融学]