Marc Lickes: Portfolio optimization if parameters are estimated

Adviser: Prof. Dr. Hans Rudolf Künsch

August 2011

Abstract:

In the following we discuss the effect of parameter estimation in the context of mean variance portfolio optimizations. We compare the efficient frontier under a certainty equivalent approach and Bayesian predictive posterior distribution. We will show that the sample estimators lead to a risk underestimation and we will provide corrected estimators.

In addition we will relax the assumption of identical returns and introduce dynamic linear models for time varying mean and covariance matrices. This study will conclude by analysing the performance of those estimators on a simulated multivariate normal data set and on a sample set of returns drawn from either the Dow Jones 30 or S&P500.