Several attempts have been made to reduce the impact of estimation errors on the optimal portfolio composition. On the one hand, improved estimators of the necessary moments have been developed and on the other hand, heuristic methods have been generated to enhance the portfolio performance, for instance the resampled efficiency of Michaud (1998). We compare the out-ofsample performance of traditional Mean-Variance optimization by Markowitz (1952) with Michaud's resampled efficiency in a comprehensive simulation study for a large number of relevant estimators appearing in the literature. In this context we consider different estimation periods as well as unconstrained and constrained portfolio optimization problems. The main finding of our simu-lation study concerning the optimization approach is that Markowitz outperforms Michaud on average. Furthermore, the estimation strategy of Frost/Savarino (1988) proves to work excellent in all analyzed situations.
portfolio selection estimators of moments simulation study mean-variance optimization resampled efficiency