  The presence of outliers in financial asset returns is a frequently occuring
phenomenon and may lead to unreliable mean-variance optimized portfolios. This
fact is due to the unbounded influence that outliers can have on the mean
returns and covariance estimators that are inputs in the optimization
procedure. In the present paper we consider new robust estimators of location
and covariance obtained by minimizing an empirical version of a pseudodistance
between the assumed model and the true model underlying the data. We prove
statistical properties of the new mean and covariance matrix estimators, such
as affine equivariance, B-robustness and efficiency. These estimators can be
easily used in place of the classical estimators, thereby providing robust
optimized portfolios. A Monte Carlo simulation study and an application to real
data show the advantages of the proposed approach.
