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Model Risk in Portfolio Optimization

1
RiskLab, Department of Mathematics, ETH Zurich, 8092 Zurich, Switzerland
2
1741 Asset Management Ltd, Multergasse 1-3, 9000 St. Gallen, Switzerland
3
Swiss Finance Institute SFI Professor, 8006 Zurich, Switzerland
*
Author to whom correspondence should be addressed.
Risks 2014, 2(3), 315-348; https://doi.org/10.3390/risks2030315
Received: 19 February 2014 / Revised: 17 June 2014 / Accepted: 30 July 2014 / Published: 6 August 2014
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. View Full-Text
Keywords: portfolio optimization; asset allocation; model risk; estimation uncertainty; covariance estimation portfolio optimization; asset allocation; model risk; estimation uncertainty; covariance estimation
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Stefanovits, D.; Schubiger, U.; Wüthrich, M.V. Model Risk in Portfolio Optimization. Risks 2014, 2, 315-348.

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