Please use this identifier to cite or link to this item:
Stefanovits, David
Schubiger, Urs
Wüthrich, Mario V.
Year of Publication: 
[Journal:] Risks [ISSN:] 2227-9091 [Publisher:] MDPI [Place:] Basel [Volume:] 2 [Year:] 2014 [Issue:] 3 [Pages:] 315-348
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.
portfolio optimization
asset allocation
model risk
estimation uncertainty
covariance estimation
Persistent Identifier of the first edition: 
Creative Commons License:
Document Type: 
Appears in Collections:

Files in This Item:
390.87 kB

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.