A data-driven optimization heuristic for downside risk minimization

In practical portfolio choice models risk is often defined as value-at-risk (VAR), expected shortfall, maximum loss, Omega function, etc, and is computed from simulated future scenarios of the portfolio value. It is well known that the minimization of these functions cannot, in general, be performed with standard methods. We present a multipurpose data-driven optimization heuristic capable of dealing efficiently with a variety of risk functions and practical constraints on the positions in the portfolio. The efficiency and robustness of the heuristic is illustrated by solving a collection of real-world portfolio optimization problems using different risk functions such as VAR, expected shortfall, maximum loss and Omega function with the same algorithm.