基于GARCH类模型的空头市场情景生成与投资组合

Scenario Generation and Portfolio Selection in Bear Market with Garch Models

2015年中,中国股票市场经历了一轮较强的空头市场,投资者面临市场价格的极大不确定性.而若能有效模拟此类型市场收益波动,对于投资者优化金融决策有积极意义.文章试验了当前主要的几种非对称GARCH类模型及多种残差分布模型,最后发现,利用ARMA-EGARCH-SSTD的组合模型能较好地模拟空头股票数据收益率效果.同时文中采用中国股票市场数据进行算例分析,发现将GARCH类模型情景拟合结果与均值-CVaR模型结合得到的投资组合优化方法能有效管理空头市场环境下的投资组合风险.

In this paper, we apply several asymmetric GARCH models to simulate the return features of bear market in recent China, and built two portfolio selection models. Then we test the models with data from real markets. Numerical results show that the ARMA-EGARCH-SSTD model can well simulate the stock returns in bear market, and the mean-CVaR model is helpful in risk management.