Stochastic Differential Utility
给出递归效用函数的随机微分形式，证明存在性、唯一性等性质，并在布朗信息下区分递归与跨期期望效用，为资产定价提供基础。
A stochastic differential formulation of recursive utility is given sufficient conditions for existence, uniqueness, time consistency, monotonicity, continuity, risk aversion, concavity, and other properties. In the setting of Brownian information, recursive and intertemporal expected utility functions are observationally distinguishable. However, one cannot distinguish between a number of non-expected-utility theories of one-shot choice under uncertainty after they are suitably integrated into an intertemporal framework. In a smooth Markov setting, the stochastic differential utility model produces a generalization of the Hamilton-Bellman-Jacobi characterization of optimality. A companion paper explores the implications for asset prices. Copyright 1992 by The Econometric Society.