Model risk in mean-variance portfolio selection: an analytic solution to the worst-case approach
| Autor: | Giulia Bianchi, Roberto Baviera |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Mathematical optimization
Model risk Control and Optimization Kullback–Leibler divergence Applied Mathematics Robust portfolio selection Management Science and Operations Research Computer Science Applications Constraint (information theory) FOS: Economics and business Portfolio Management (q-fin.PM) Mean variance Portfolio Mean-variance portfolio Special case Constant (mathematics) Selection (genetic algorithm) Quantitative Finance - Portfolio Management Mathematics |
| DOI: | 10.48550/arxiv.1902.06623 |
| Popis: | 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. Comment: 22 pages, 4 figures |
| Databáze: | OpenAIRE |
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