多风险控制目标下的资产配置模型

相比于VaR风险测度,CVaR风险测度因满足次可加性能够更好地描述金融资产组合风险,而广义极值分布和Copula函数较好地拟合了金融资产收益率的厚尾特征和相依性。本文尝试使用CVaR风险测度和Copula-GEV分布描述金融资产组合的极端值风险,并将其作为风险控制目标引入传统均值-方差模型,构建多风险控制目标下的资产配置优化模型,实现在金融资产配置决策中综合考虑期望收益、波动性风险和极端值风险,并设计PSO-MC优化算法对模型进行求解。通过对我国上市公司股票收益率数据的实证分析,验证了模型及求解算法的有效性。

Asset Allocation under Multi-risk Contrlo Targets

Compared with VaR risk measure,CVaR could measure the portfolio risk more suitable under the sub-additivity condition.And besides,generalized extreme value distribution and copula function could fit the portfolio return rate’s fat tails and dependence quite well.In this paper,CVaR and Copula-GEV distribution are adopted to describe the biggest risk,which the portfolio would face with during a sharp drop in the market.We introduce CVaR in Mean-Variance model as a target of risk control.An optimization model of asset allocation under multiple risk targets is proposed,considering the return rate,the volatility and the extreme value risk comprehensively.In accordance with the features of this model,we combine particle swarm optimization method with Monte Carlo simulation to solve it.Through an empirical analysis of stock returns of some listed companies in China,the feasibility and effectiveness of proposed model and algorithm have been validated.