不完全信息下推广的递归偏好

引入了推广的递归偏好，考察投资者由于噪音而不只是Brown运动不能观察到股票价格过程的随机漂移.通过滤波理论把这种不完全信息的问题转化成完全信息的问题.考察了完全市场中的最优消费和最优投资组合问题，给出了求最优消费的理论框架.所用的主要工具是倒向随机微分方程.

Generalized recursive preference in incomplete information

The case of an agent with generalized recursive preference is considered,who can't observe the random drift of the stock price process.This problem with partial information can be transformed into a problem with full information by filtering,in which the drift is replaced by its expected value conditional on the information given by the stock price history.The optimal consumption and portfolio in a complete market is also considered,and the framework to find the optimal consumption is got.The main method is the knowledge of Backward Stochastic Differential Equation(BSDE).