  In this paper we consider the worst-case model risk approach described in
Glasserman and Xu (2014). Portfolio selection with model risk can be a
challenging operational research problem. In particular, it presents an
additional optimisation compared to the classical one. We find the analytical
solution for the optimal mean-variance portfolio selection in the worst-case
scenario approach. In the minimum-variance case, we prove that the analytical
solution is significantly different from the one found numerically by
Glasserman and Xu (2014) and that model risk reduces to an estimation risk. A
detailed numerical example is provided.
