Model risk in portfolio optimization


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Date

2014

Publication Type

Journal Article

ETH Bibliography

yes

Citations

Altmetric

Data

Abstract

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.

Publication status

published

Editor

Book title

Journal / series

Volume

2 (3)

Pages / Article No.

315 - 348

Publisher

MDPI

Event

Edition / version

Methods

Software

Geographic location

Date collected

Date created

Subject

Portfolio optimization; Asset allocation; Model risk; Estimation uncertainty; Covariance estimation

Organisational unit

08813 - Wüthrich, Mario Valentin (Tit.-Prof.) check_circle

Notes

Funding

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