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arXiv · 1902.06623

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

Roberto Baviera·Giulia Bianchi
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Abstract

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.

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BibTeXRIS

Roberto Baviera, Giulia Bianchi. 2019-02-18. Model risk in mean-variance portfolio selection: an analytic solution to the worst-case approach. https://arxiv.org/abs/1902.06623

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