A heuristic approach for value at risk based portfolio optimization

Portfolio optimization under classic mean-variance framework of Markowitz must be revised as variance fails to be a good risk measure. This is especially true when the asset returns are not normal. In this paper, we utilize Value at Risk (VaR) as the risk measure and Historical Simulation (HS) is used to obtain an acceptable estimate of the VaR. Also, a well known multi-objective evolutionary approach is used to address the inherent bi-objective problem; In fact, NSGA-II is incorporated here. This method is tested on a set of past return data of 12 assets on Tehran Stock Exchange (TSE). A comparison of the obtained results, shows that the proposed method offers high quality solutions and a wide range of risk return trade-offs.