PORTFOLIO OPTIMIZATION UNDER PARAMETER UNCERTAINTY USING THE RISK AVERSION FORMULA

Abstract

The Markowitz portfolio optimization model has certain difficul-ties in practise since real data are rarely certain. The robust optimization isa recently developed method that is used to overcome the uncertainty situ-ation. The technique has been recently suggested in the portfolio selectionproblems. In this study, two kinds of portfolio optimization problems are pre-sented: (i) the risk aversion portfolio optimization problem based on the clas-sical Markowitz framework, and (ii) the max-min counterpart problem basedon the robust optimization framework. In the application, the two models areperformed on a real-world data set obtained from BIST (Borsa Istanbul). Nu-merical results show that the objective function values of the classical solutionand the robust solution are similar to each other. It can be said that the robustmodel, which works as well as the classical model in the uncertainty situations,can be used instead of the classical model and also that the optimal solutionobtained in the uncertainty situation is robust to parameter perturbation.