A robust bayesian approach to portfolio selection

TL;DRAbstract

This thesis aims at studying the local robustness properties of Bayesian posterior summaries and deriving a robust procedure to estimate Bayesian Mean-Variance weights in a portfolio selection problem. In the first part, we study the local robustness of Bayesian estimators. In particular, we build a framework wherein any Bayesian quantity can be seen as a posterior functional. In this way it becomes possible to construct different robustness measures. We derive local influence measures for posterior summaries with respect both to prior and sampling distributions and to observations. Then we address the issue of efficient implementation of the derived measures through MCMC algorithms. In the second part, we deal with the problem of robust estimation in a Bayesian context, providing a useful result to generalize univariate robust distributions to the multivariate case. We also propose criteria to assess in which cases a robust model is recommended and how to choose among estimates obtain

Chat with Paper

AI Agents for this Paper