Global Minimum Variance Portfolio Optimisation under Some Model Risk: A Robust Regression-based Approach

Maillet, Bertrand & Tokpavi, Sessi & Vaucher, Benoit, 2015. "Global minimum variance portfolio optimisation under some model risk: A robust regression-based approach," European Journal of Operational Research, Elsevier, vol. 244(1), pages 289-299.

32 Pages Posted: 5 May 2021

See all articles by Bertrand B. Maillet

Bertrand B. Maillet

EMLyon Business School (Paris Campus)

sessi tokpavi

affiliation not provided to SSRN

Benoit Vaucher

Edhec Scientific Analytics

Date Written: April 28, 2015

Abstract

The global minimum variance portfolio computed using the sample covariance matrix is known to be negatively affected by parameter uncertainty, an important component of model risk. Using a robust approach, we introduce a portfolio rule for investors who wish to invest in the global minimum variance portfolio due to its strong historical track record, but seek a rule that is robust to parameter uncertainty. Our robust portfolio corresponds theoretically to the global minimum variance portfolio in the worst-case scenario, with respect to a set of plausible alternative estimators of the covariance matrix, in the neighbourhood of the sample covariance matrix. Hence, it provides protection against errors in the reference sample covariance matrix. Monte Carlo simulations illustrate the dominance of the robust portfolio over its non-robust counterpart, in terms of portfolio stability, variance and risk-adjusted returns. Empirically, we compare the out-of-sample performance of the robust portfolio to various competing minimum variance portfolio rules in the literature. We observe that the robust portfolio often has lower turnover and variance and higher Sharpe ratios than the competing minimum variance portfolios.

JEL Classification: G11, C44, D81

Suggested Citation

Maillet, Bertrand B. and tokpavi, sessi and Vaucher, Benoit, Global Minimum Variance Portfolio Optimisation under Some Model Risk: A Robust Regression-based Approach (April 28, 2015). Maillet, Bertrand & Tokpavi, Sessi & Vaucher, Benoit, 2015. "Global minimum variance portfolio optimisation under some model risk: A robust regression-based approach," European Journal of Operational Research, Elsevier, vol. 244(1), pages 289-299., Available at SSRN: https://ssrn.com/abstract=3835873

Bertrand B. Maillet

EMLyon Business School (Paris Campus) ( email )

23 Avenue Guy de Collongue
Ecully, 69132
France

Sessi Tokpavi

affiliation not provided to SSRN

Benoit Vaucher (Contact Author)

Edhec Scientific Analytics ( email )

58 rue du Port
Lille, 59046
France

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
36
Abstract Views
215
PlumX Metrics