Posterior Inference for Portfolio Weights


Speaker


Abstract

We investigate estimation uncertainty in portfolio weights through their posterior distributions in a Bayesian regression framework. While we derive analytical posterior results for shrinkage variants of the global minimum variance portfolio (GMVP), the main advantage of our novel approach is that we specify the prior directly on the optimal portfolio weights. This avoids estimating the moments of the asset return distribution and substantially reduces the dimensionality of the estimation problem. In a series of empirical experiments we explore the effect of estimation errors on the performance of the optimal portfolio and propose various practical trading strategies derived from the posterior distribution, which are highly beneficial to the investor. We further show how to incorporate economic views about asset returns in our framework as shrinkage targets and how to account for the investor’s uncertainty about these views through a hierarchical set-up.