基于贝叶斯随机规划方法的养老保险基金投资策略研究

Research of Investment Strategy of Pension Fund Based on the Bayesian Stochastic Programming Approach

结合中国养老保险基金投资现状,利用随机规划建立中国养老基金投资策略模型,依据Minnesota法则改进贝叶斯向量自回归参数分布的确定方法。根据改进的贝叶斯向量自回归模型生成资本市场未来收益情景,得到养老基金最优投资策略并给出模拟计算具体步骤。最后结合历史数据进行模拟分析,结果表明模型能够根据实际情况优化资产配置。

In this paper,according to the China pension fund situation,we develop the optimization dynamic investment strategy models based on the Bayesian stochastic programming approach,in which we improve the Bayesian vector autoregressive by using the Minnesota Prior.According to the improved model,we estimate the asset future returns and give the concreted calculation steps for solving the models.Finally and combining with the historical data,we conduct a simulation,the result shows that the optimal investment strategy can be solved according to the reality.