MCMC Simulation-based Estimation in Portfolio Selection

Eric Jacquier
Finance Department, HEC MONTREAL

Nicholas Polson
Graduate School of Business, University of Chicago


This paper discusses a simulation-based approach to optimal portfolio selection. We take a Bayesian approach as it naturally accounts for estimation risk (a.k.a. parameter uncertainty), learning of state variables and models, and can incorporate prior beliefs about future return distributions. We highlight two areas of application with great potential in portfolio selection. First, for competing models of predictable returns, we show how Ūltering techniques can be used to compute time varying model probabilities. Second, we show how simulation methods can maximize expected utility, bypassing computationally awkward gradient methods. We illustrate these methods in the classic risky stock allocation framework.