Robust mean absolute deviation portfolio model under Affine Data Perturbation uncertainty set

Abstract

In this paper, based on the robust optimization techniques in Bertsimas and Sim[8], we propose a computationally tractable robust mean absolute deviation portfolio model. The purpose is to consider parameter uncertainty by controlling the impact of estimation errors on the portfolio strategy performance. The remarkable characteristic of the new method is that the robust optimization model retains the complexity of original portfolio optimization problem, i.e., the robust counterpart problem is still a linear programming problem. Empirical analysis with real market data to illustrate the behavior of the robust optimization model is efficient.