A mean-CVaR-skewness portfolio optimization model based on asymmetric Laplace distribution
A mean-CVaR-skewness portfolio optimization model based on asymmetric Laplace distribution
复制标题
基于非对称拉普拉斯分布的均值CVaR偏度投资组合优化模型
DOI：
10.1007/s10479-014-1654-y
复制
发表时间：
2015
影响因子：
4.8
通讯作者：
Fei Hu
中科院分区：
文献类型：
--
作者：
Shangmei Zhao;Qing Lu;Liyan Han;Yong Liu;Fei Hu
在资产收益率不确定的情况下，选择合适的风险度量和确定投资组合中资产的最优权重仍然是一个艰巨而具有挑战性的问题。本文提出并研究了一个基于非对称拉普拉斯分布的均值-条件风险价值-偏度投资组合优化模型，该模型适用于描述金融资产的尖峰厚尾和偏度特征.此外，在投资组合优化模型中加入了偏度，以满足投资者的多样化需求。为了解决这个多目标问题，我们提出了一个简化的模型与完全相同的解决方案。这种改进的模型大大降低了问题的复杂性。因此，均值-条件风险值-偏度模型可以得到相应的求解。为了说明该方法，我们提供了一个应用程序的19个成分股的标准普尔500指数使用我们的模型的投资组合配置。我们表明，该模型可以作出重要贡献的研究投资决策。
In the presence of uncertainty of asset returns, choosing an appropriate risk measure and determining the optimal weights of assets in a portfolio remain formidable and challenging problems. In this paper, we propose and study a mean-conditional value at risk-skewness portfolio optimization model based on the asymmetric Laplace distribution, which is suitable for describing the leptokurtosis, fat-tail, and skewness characteristics of financial assets. In addition, skewness is added into the portfolio optimization model to meet the diverse needs of investors. To solve this multi-objective problem, we suggest a simplified model with exactly the same solution. This modified model greatly reduces the complexity of the problem. Therefore, the mean-conditional value at risk-skewness model can be correspondingly solved. In order to illustrate the method, we provide an application concerning the portfolio allocation of 19 constituent stocks of S&P 500 index using our model. We show that this model could make important contributions to research on investment decision making.