The Omega ratio is a recent performance measure proposed to overcome the known shortcomings of the Sharpe ratio. Until recently, the Omega ratio was thought to be computationally intractable, and research was focused on heuristic optimization procedures. We have shown elsewhere that the Omega ratio optimization is equivalent to a linear program and hence can be solved exactly in polynomial time. This permits the investigation of more complex and realistic variants of the problem. The standard formulation of the Omega ratio requires perfect information for the probability distribution of the asset returns. In this paper, we investigate the problem arising from the probability distribution of the asset returns being only partially known. We introduce the robust variant of the conventional Omega ratio that hedges against uncertainty in the probability distribution. We examine the worst-case Omega ratio optimization problem under three types of uncertainty – mixture distribution, box and ellipsoidal uncertainty – and show that the problem remains tractable.
