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In this paper we consider the worst-case model risk approach described inGlasserman and Xu (2014). Portfolio selection with model risk can be achallenging operational research problem. In particular, it presents anadditional optimisation compared to the classical one. We find the analyticalsolution for the optimal mean-variance portfolio selection in the worst-casescenario approach. In the minimum-variance case, we prove that the analyticalsolution is significantly different from the one found numerically byGlasserman and Xu (2014) and that model risk reduces to an estimation risk. Adetailed numerical example is provided.

Model risk in mean-variance portfolio selection: an analytic solution to the worst-case approach