en
Report
Open access
English

Robust Mean-Variance Portfolio Selection

Number of pages45
PublisherGenève
Collection
  • Cahiers du département d'économétrie; 2003.02
Publication date2003
Abstract

This 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.

Keywords
  • Mean-variance efficient frontier
  • Outliers
  • Model risk
  • Robust estimation
Citation (ISO format)
PERRET-GENTIL, Cédric, VICTORIA-FESER, Maria-Pia. Robust Mean-Variance Portfolio Selection. 2003
Main files (1)
Report
accessLevelPublic
Identifiers
  • PID : unige:6625
777views
2026downloads

Technical informations

Creation05/11/2010 3:13:00 PM
First validation05/11/2010 3:13:00 PM
Update time03/14/2023 3:29:07 PM
Status update03/14/2023 3:29:07 PM
Last indexation01/15/2024 7:58:58 PM
All rights reserved by Archive ouverte UNIGE and the University of GenevaunigeBlack