部分信息下均值-方差准则下的投资组合问题研究

Mean-variance Portfolio Under Incomplete Information

研究了部分信息下,投资组合效用最大化的问题.在风险资产(股票)价格满足跳扩散过程,对同时该过程中的系数受马尔科夫调制参数的影响.通过运用非线性滤波技术,将部分信息的问题转化完全信息的问题.并运用随机优化与倒向随机微分方程得到在均值-方差准则的最优投资策略.

In a market with incomplete information we consider the portfolio for unility maximizing investors. The risk asset（stock） price process satifies a Jump-Diffusion process where the coefficient is driven by a Markov chain of finite states. By using the nonlinear filtering technology, the incomplete information problem is transformed into a complete information problem. The main result of this paper is that we drive the approximation of the optimal trading strategy under the mean-variance problem.