Model Risk in Portfolio Optimization

Risks

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.