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
纸质出版日期:2012,
网络出版日期:2012-11-25,
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曾艳姗, 李仲飞. 基于粒子群优化算法的均值-VaR投资组合选择[J]. 中山大学学报(自然科学版)(中英文), 2012,51(6):1-9.
ZENG Yanshan, LI Zhongfei. Mean-VaR Portfolio Selection Based on Particle Swarm Optimization Algorithm[J]. Acta Scientiarum Naturalium Universitatis SunYatseni, 2012,51(6):1-9.
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 markets 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.
均值-VaR有限卖空交易成本粒子群优化
mean-VaRlimited short sellingtransaction costparticle swarm optimization
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