Robust estimation of efficient mean–variance frontiers

Standard methods for optimal allocation of shares in a financial portfolioare determined by second-order conditions which are very sensitive to outliers. Thewell-known Markowitz approach, which is based on the input of a mean vector anda covariance matrix, seems to provide questionable results in financial management,since small changes of inputs might lead to irrelevant portfolio allocations. However,existing robust estimators often suffer from masking of multiple influential observations,so we propose a new robust estimator which suitably weights data using aforward search approach. A Monte Carlo simulation study and an application to realdata show some advantages of the proposed approach.