Risk sensitive control of finite state Markov chains in discrete time, with applications to portfolio management

In this paper we extend standard dynamic programming results for the risk sensitive optimal control of discrete time Markov chains to a new class of models. The state space is only ®nite, but now the assumptions about the Markov transition matrix are much less restrictive. Our results are then applied to the ®nancial problem of managing a portfolio of assets which are a ̈ected by Markovian microeconomic and macroeconomic factors and where the investor seeks to maximize the portfolio's risk adjusted growth rate.