Robust Approach for Uncertain Portfolio Allocation Problems Under Box Uncertainty

Abstract

Portfolio Optimization is the process of investing the total wealth among different assets to get maximum return out of it with a least possible risk. Several risk-return optimization models can be used to determine the weights which should be given to each asset for the optimal profit. But there is a high possibility of the results being affected by the uncertainty in input parameters, namely, expected return and risk. A small perturbation in the input parameters can mislead the investor to invest in an inefficient portfolio. In uncertainty-based optimization problems, the uncertain parameters are assumed to lie in some specific uncertainty structure like box, ellipsoidal, polyhedral, etc. In the last two decades such problems are dealt with Robust Optimization approach, where the worst-case scenario problem is solved to get �immunized against uncertainty� solutions. This paper provides a discussion on robust mean�variance and robust mean-semi-variance problems under box uncertainty. � 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.