基于粒子群优化算法的均值-VaR投资组合选择  

Mean-VaR Portfolio Selection Based on Particle Swarm Optimization Algorithm

作　　者：曾艳姗[1,2] 李仲飞[3] 

机构地区：[1]中山大学数学与计算科学学院,广东广州510275 [2]仲恺农业工程学院计算科学学院,广东广州510225 [3]中山大学岭南学院∥金融工程与风险管理研究中心,广东广州510275

出　　处：《中山大学学报（自然科学版）》2012年第6期1-9,共9页Acta Scientiarum Naturalium Universitatis Sunyatseni

基　　金：国家杰出青年科学基金资助项目(70825002);广东省高等学校高层次人才资助项目;广东省哲学社会科学规划资助项目(GD11YYJ07)

摘　　要：在现实市场中,①为防止由卖空交易引起市场操纵等问题的出现,即使在发达的证券市场,交易仍受到一定的卖空限制;②由于市场相关规定与投资者自身风险控制的需要,在某些资产上的投资比例受到一定限制;③交易过程中需支付印花税等交易成本。故结合这三方面,采用Value-at-Risk(VaR)度量风险,在收益率服从正态和非正态分布两种假设下,构建了带有限卖空约束、投资比例约束和交易成本的均值-VaR投资组合模型。首先,给出了该模型的粒子群优化(PSO)算法;其次采用A股市场的实际数据进行了数值实验;最后分析了有效前沿的特征及有限卖空约束对投资决策的影响。In the real market, （i） in order to prevent market manipulation and other problems caused by short selling, transaction is still subject to some short selling restrictions even in developed markets; （ii） due to the market＇s relevant regulations and investors＇ requirement of risk control, the proportions invested in some assets have certain limits; （iii） investors must pay stamp duty and other transaction costs during transaction. Considering these three aspects, Value-at-Risk （VaR） as risk measure is adopted, and a mean-VaR portfolio model is constructed with limited short selling, proportion of investment limits and transaction cost under two assumptions that the rate of return is normal and non-normal distribution. Firstly, a particle swarm optimization （PSO） algorithm is presented for the model; secondly, numerical experiments are provided by using the test data from A stock market of China; finally, the characteristics of the portfolio efficient frontier and the influences on investors＇ decision-making under the limited short selling constraints are discussed.

关 键 词：均值-VAR 有限卖空 交易成本 粒子群优化 

分 类 号：O22[理学—运筹学与控制论]