Robust Mean-Variance Portfolio Selection
AbstractThis paper investigates model risk issues in the context of mean-variance portfolio selection. We analytically and numerically show that, under model misspecification, the use of statistically robust estimates instead of the widely used classical sample mean and covariance is highly beneficial for the stability properties of the mean-variance optimal portfolios. Moreover, we perform simulations leading to the conclusion that, under classical estimation, model risk bias dominates estimation risk bias. Finally, we suggest a diagnostic tool to warn the analyst of the presence of extreme returns that have an abnormally large influence on the optimization results.
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Bibliographic InfoPaper provided by International Center for Financial Asset Management and Engineering in its series FAME Research Paper Series with number rp140.
Date of creation: Apr 2005
Date of revision:
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Mean-variance e .cient frontier; Outliers; Model risk; Robust es-timation;
Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
This paper has been announced in the following NEP Reports:
- NEP-ALL-2005-05-14 (All new papers)
- NEP-BEC-2005-05-14 (Business Economics)
- NEP-ECM-2005-05-14 (Econometrics)
- NEP-FIN-2005-05-14 (Finance)
- NEP-RMG-2005-05-14 (Risk Management)
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