基于CVaR衍生的多期多面风险度量下的投资组合研究

为了解决多期投资组合的决策问题,本文将由CVaR衍生的多期多面风险度量作为风险控制目标,建立了一个在收益约束条件下最小化风险的多阶段投资组合模型。为求解模型,设计了多期投资组合优化流程,它将非参数抽样方法、基于聚类算法的多阶段情景树生成方法和多期多面风险度量组合在一起。该流程基于计算、容易实现、直观合理。根据我国金融市场数据进行的实证研究结果表明,这一流程具有较好的实用性。

Portfolio Selection Based on Polyhedral Multi-period Risk MeasuresDerived from Conditional Value-at-risk

In order to solve the problems of multi-period portfolio, a multi-period investment portfolio optimal process is designed. The process can solve the problems by minimizing the risk with some benefits constraints, using a series of fast algorithm modules. It is achieved by combining the nonparametric sampling method, clustering-based multistage scenario generation method, and multi-period polyhedral risk measures. The process is proposed based on some calculations, which is simple, reasonable and easy to implement. The empirical study on the financial markets data in China demonstrates good practicality of the whole process.