Risks | |
Model Risk in Portfolio Optimization | |
David Stefanovits2  Urs Schubiger1  | |
[1] 1741 Asset Management Ltd, Multergasse 1-3, 9000 St. Gallen, Switzerland; E-Mail:;RiskLab, Department of Mathematics, ETH Zurich, 8092 Zurich, Switzerland | |
关键词: portfolio optimization; asset allocation; model risk; estimation uncertainty; covariance estimation; | |
DOI : 10.3390/risks2030315 | |
来源: mdpi | |
【 摘 要 】
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.
【 授权许可】
CC BY
© 2014 by the authors; licensee MDPI, Basel, Switzerland.
【 预 览 】
Files | Size | Format | View |
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RO202003190022996ZK.pdf | 390KB | download |