Lipschitzian Estimates in Discrete-Time Constrained Stochastic Optimal Control

This paper is devoted to the analysis of a finite horizon discrete-time stochastic optimal control problem, in presence of constraints. We study the regularity of the value function which comes from the dynamic programming algorithm. We derive accurate estimates of the Lipschitz constant of the value function, by means of a regularity result of the multifunction that defines the admissible control set. In the last section we discuss an application to an optimal asset-allocation problem.