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Nonlinear shrinkage estimation of large integrated covariance matrices

Lam, Clifford, Feng, Phoenix and Hu, Charlie (2017) Nonlinear shrinkage estimation of large integrated covariance matrices. Biometrika, 104 (2). pp. 481-488. ISSN 0006-3444

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Pending embargo until 1 January 2100.

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Identification Number: 10.1093/biomet/asx021

Abstract

Integrated covariance matrices arise in intra-day models of asset returns, which allow volatility to change across the trading day. When the number of assets is large, the natural estimator of such a matrix suffers from bias, contributed from extreme eigenvalues. We introduce a novel nonlinear shrinkage estimator for the integrated covariance matrix which shrinks the extreme eigenvalues of a realized covariance matrix back to an acceptable level, and enjoys a certain asymptotic efficiency when the number of assets is of the same order as the number of data points. Novel maximum exposure and actual risk bounds are derived when our estimator is used in constructing the minimum variance portfolio. Compared to other methods, our estimator performs favorably in both simulations and a real data analysis.

Item Type: Article
Official URL: https://academic.oup.com/biomet
Additional Information: © 2017 Biometrika Trust
Divisions: Statistics
Subjects: H Social Sciences > HA Statistics
Sets: Departments > Statistics
Date Deposited: 14 Mar 2017 15:53
Last Modified: 20 Mar 2019 03:12
URI: http://eprints.lse.ac.uk/id/eprint/69812

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