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

Eigenvalue Ratio Test for the Number of Factors

Gianluca De Nard

Journal of Financial Econometrics, 2022, vol. 20, issue 4, 569-611

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 (search for similar items in EconPapers)
JEL-codes: C13 C30 C53 C58 G11 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (3)

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