Model Risk in Portfolio Optimization

David Stefanovits, Urs Schubiger and Mario V. Wüthrich
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David Stefanovits: RiskLab, Department of Mathematics, ETH Zurich, 8092 Zurich, Switzerland
Urs Schubiger: 1741 Asset Management Ltd, Multergasse 1-3, 9000 St. Gallen, Switzerland
Mario V. Wüthrich: RiskLab, Department of Mathematics, ETH Zurich, 8092 Zurich, Switzerland

Risks, 2014, vol. 2, issue 3, 1-34

Abstract: We consider a one-period portfolio optimization problem under model uncertainty. For this purpose, we introduce a measure of model risk. We derive analytical results for this measure of model risk in the mean-variance problem assuming we have observations drawn from a normal variance mixture model. This model allows for heavy tails, tail dependence and leptokurtosis of marginals. The results show that mean-variance optimization is seriously compromised by model uncertainty, in particular, for non-Gaussian data and small sample sizes. To mitigate these shortcomings, we propose a method to adjust the sample covariance matrix in order to reduce model risk.

Keywords: portfolio optimization; asset allocation; model risk; estimation uncertainty; covariance estimation (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jrisks:v:2:y:2014:i:3:p:315-348:d:38890

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