Standard methods for optimal allocation of shares in a financial portfolio are de- termined by second-order conditions which are very sensitive to outliers. The well-known Markowitz approach, which is based on the input of a mean vector and a covariance matrix, seems to provide questionable results in financial management, since small changes of in- puts might lead to irrelevant portfolio allocations. However, existing robust estimators often suffer from masking of multiple influential observations, so we propose a new robust estima- tor which suitably weights data using a forward search approach. A Monte Carlo simulation study and an application to real data show some advantages of the proposed approach.
Robust estimation of efficient mean–variance frontiers / L. GROSSI; F. LAURINI. - In: ADVANCES IN DATA ANALYSIS AND CLASSIFICATION. - ISSN 1862-5347. - 5(2011), pp. 3-22. [10.1007/s11634-010-0082-3]