Model risk in portfolio optimization

Artikel

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Risks ; ISSN: 2227-9091 ; Volume: 2 ; Year: 2014 ; Issue: 3 ; Pages: 315-348 ; Basel: MDPI

Classification and Topics

• Classification: Wirtschaft
• Subject: portfolio optimization ; asset allocation ; model risk ; estimation uncertainty ; covariance estimation

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Stefanovits, David ; Schubiger, Urs ; Wüthrich, Mario V.
• Event: Veröffentlichung
• (who): MDPI
• (where): Basel
• (when): 2014

Further information

• DOI: doi:10.3390/risks2030315
• Handle: http://hdl.handle.net/10419/103617
• Last update: 10.03.2025, 11:45 AM CET