Oops! I Shrunk the Sample Covariance Matrix Again: Blockbuster Meets Shrinkage

Journal of Financial Econometrics (2020, forthcoming)

50 Pages Posted: 17 Jun 2019 Last revised: 7 Oct 2020

See all articles by Gianluca De Nard

Gianluca De Nard

University of Zurich - Department of Banking and Finance

Date Written: June 2, 2019

Abstract

Existing shrinkage techniques struggle to model the covariance matrix of asset returns in the presence of multiple-asset classes. Therefore, we introduce a Blockbuster shrinkage estimator that clusters the covariance matrix accordingly. Besides the definition and derivation of a new asymptotically optimal linear shrinkage estimator we propose an adaptive Blockbuster algorithm that clusters the covariance matrix even if the (number of) asset classes are unknown and change over time. It displays superior all-around performance on historical data against a variety of state-of-the-art linear shrinkage competitors. Additionally, we find that for small and medium-sized investment universes the proposed estimator outperforms even recent nonlinear shrinkage techniques. Hence, this new estimator can be used to deliver more efficient portfolio selection and detection of anomalies in the cross-section of asset returns. Furthermore, due to the general structure of the proposed Blockbuster shrinkage estimator the application is not restricted to financial problems.

Keywords: Blockbuster, large-dimensional covariance matrix estimation, linear and nonlinear shrinkage, Markowitz portfolio selection

JEL Classification: C13, C30, C53, C58, G11

Suggested Citation

De Nard, Gianluca, Oops! I Shrunk the Sample Covariance Matrix Again: Blockbuster Meets Shrinkage (June 2, 2019). Journal of Financial Econometrics (2020, forthcoming), Available at SSRN: https://ssrn.com/abstract=3400062 or http://dx.doi.org/10.2139/ssrn.3400062

Gianluca De Nard (Contact Author)

University of Zurich - Department of Banking and Finance ( email )

Zürichbergstrasse 14
Zürich, Zürich CH-8032
Switzerland

HOME PAGE: http://denard.ch

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