Model Risk in Portfolio Optimization
AbstractWe 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. View Full-Text
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Stefanovits, D.; Schubiger, U.; Wüthrich, M.V. Model Risk in Portfolio Optimization. Risks 2014, 2, 315-348.
Stefanovits D, Schubiger U, Wüthrich MV. Model Risk in Portfolio Optimization. Risks. 2014; 2(3):315-348.Chicago/Turabian Style
Stefanovits, David; Schubiger, Urs; Wüthrich, Mario V. 2014. "Model Risk in Portfolio Optimization." Risks 2, no. 3: 315-348.