Improved robust portfolio optimization

A robust optimization has emerged as a powerful tool for managing un- certainty in many optimization problems. This method was adapted in portfolio optimization to resolve the sensitivity issue of the mean-variance model to its inputs (i.e. mean vector and covariance matrix of returns). The solution provided by this framework presented here can be very sensitive to the choice of uncertainty sets, since the optimal portfolios are determined under "the worst-case objective value" of the inputs in their uncertainty sets. One potential consequence of this emphasis on the worst-case is that the decisions are highly influenced by extreme sce- narios in the uncertainty sets. The emergence of the extreme scenarios in the uncertainty sets can be because there are extreme observations in the data. These extreme observations frequently occur in financial sector. We proposed to tackle this issue by considering robust estimators that are incorporated to the uncertainty sets about unknown parameters. They showed both in simulated and empirical investigations that this strategy can lead to the construction of portfolios with superior out-of-sample performance in comparison to the mean-variance portfolio (classic) and robust portfolio optimization