When do Improved Covariance Matrix Estimators Enhance Portfolio Optimization? An Empirical Comparative Study of Nine Estimators

30 Pages Posted: 27 Apr 2010

See all articles by Ester Pantaleo

Ester Pantaleo

University of Bari

Michele Tumminello

University of Palermo; Carnegie Mellon University - Department of Social and Decision Sciences

Fabrizio Lillo

Università di Bologna

Rosario N. Mantegna

University of Palermo

Date Written: April 23, 2010

Abstract

The use of improved covariance matrix estimators as an alternative to the sample estimator is considered an important approach for enhancing portfolio optimization. Here we empirically compare the performance of 9 improved covariance estimation procedures by using daily returns of 90 highly capitalized US stocks for the period 1997-2007. We find that the usefulness of covariance matrix estimators strongly depends on the ratio between estimation period T and number of stocks N, on the presence or absence of short selling, and on the performance metric considered. When short selling is allowed, several estimation methods achieve a realized risk that is significantly smaller than the one obtained with the sample covariance method. This is particularly true when T/N is close to one. Moreover many estimators reduce the fraction of negative portfolio weights, while little improvement is achieved in the degree of diversification. On the contrary when short selling is not allowed and T>N, the considered methods are unable to outperform the sample covariance in terms of realized risk but can give much more diversified portfolios than the one obtained with the sample covariance. When T

Keywords: portfolio optimization, covariance matrix estimator

JEL Classification: G11

Suggested Citation

Pantaleo, Ester and Tumminello, Michele and Lillo, Fabrizio and Mantegna, Rosario Nunzio, When do Improved Covariance Matrix Estimators Enhance Portfolio Optimization? An Empirical Comparative Study of Nine Estimators (April 23, 2010). Available at SSRN: https://ssrn.com/abstract=1596865 or http://dx.doi.org/10.2139/ssrn.1596865

Ester Pantaleo

University of Bari ( email )

Piazza Umberto I
Bari, 70121
Italy

Michele Tumminello (Contact Author)

University of Palermo ( email )

Viale delle Scienza
Palermo, Palermo 90128
Italy

Carnegie Mellon University - Department of Social and Decision Sciences ( email )

Pittsburgh, PA 15213-3890
United States

Fabrizio Lillo

Università di Bologna ( email )

Via Zamboni, 33
Bologna, 40126
Italy

Rosario Nunzio Mantegna

University of Palermo ( email )

Dipartimento di Fisica e Chimica
Viale delle Scienze, Edificio 18
Palermo, PA I-90128
Italy
+3909123899074 (Phone)
+3909123860815 (Fax)

HOME PAGE: http://www.unipa.it/persone/docenti/m/rosario.mantegna

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