Using, Taming or Avoiding the Factor Zoo? A Double-Shrinkage Estimator for Covariance Matrices

64 Pages Posted: 30 Jul 2021 Last revised: 23 Feb 2023

See all articles by Gianluca De Nard

Gianluca De Nard

University of Zurich - Department of Economics; New York University - Volatility and Risk Institute; OLZ AG

Zhao Zhao

Huazhong University of Science and Technology - Department of Economics

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Date Written: July 10, 2021

Abstract

Existing factor models struggle to model the covariance matrix for a large number of stocks and factors. Therefore, we introduce a new covariance matrix estimator that first shrinks the factor model coefficients and then applies nonlinear shrinkage to the residuals and factors. The estimator blends a regularized factor structure with conditional heteroskedasticity of residuals and factors and displays superior all-around performance against various competitors. We show that for the proposed double- shrinkage estimator, it is enough to use only the market factor or the most important latent factor(s). Thus there is no need for laboriously taking into account the factor zoo.

Keywords: Double-shrinkage, Factor models, Markowitz portfolio selection, Multivariate GARCH, Nonlinear shrinkage, Regularization.

Suggested Citation

De Nard, Gianluca and Zhao, Zhao, Using, Taming or Avoiding the Factor Zoo? A Double-Shrinkage Estimator for Covariance Matrices (July 10, 2021). Journal of Empirical Finance, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3883957 or http://dx.doi.org/10.2139/ssrn.3883957

Gianluca De Nard (Contact Author)

University of Zurich - Department of Economics ( email )

Zürichbergstrasse 14
Zürich, Zurich 8032
Switzerland

New York University - Volatility and Risk Institute ( email )

Department of Finance
New York, NY
United States

HOME PAGE: http://https://vlab.stern.nyu.edu

OLZ AG ( email )

Gessnerallee 38
Zurich, Zurich 8001
Switzerland

Zhao Zhao

Huazhong University of Science and Technology - Department of Economics ( email )

Wuhan, Hubei 430074
China

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