  Stochastic domains often involve risk-averse decision makers. While recent
work has focused on how to model risk in Markov decision processes using risk
measures, it has not addressed the problem of solving large risk-averse
formulations. In this paper, we propose and analyze a new method for solving
large risk-averse MDPs with hybrid continuous-discrete state spaces and
continuous action spaces. The proposed method iteratively improves a bound on
the value function using a linearity structure of the MDP. We demonstrate the
utility and properties of the method on a portfolio optimization problem.
