Arbeitspapier
The power of (non-)linear shrinking: A review and guide to covariance matrix estimation
Many econometric and data-science applications require a reliable estimate of the covariance matrix, such as Markowitz portfolio selection. When the number of variables is of the same magnitude as the number of observations, this constitutes a difficult estimation problem; the sample covariance matrix certainly will not do. In this paper, we review our work in this area, going back 15+ years. We have promoted various shrinkage estimators, which can be classified into linear and nonlinear. Linear shrinkage is simpler to understand, to derive, and to implement. But nonlinear shrinkage can deliver another level of performance improvement, especially if overlaid with stylized facts such as time-varying co-volatility or factor models.
- Language
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Englisch
- Bibliographic citation
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Series: Working Paper ; No. 323
- Classification
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Wirtschaft
Estimation: General
Financial Econometrics
Portfolio Choice; Investment Decisions
- Subject
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dynamic conditional correlations
factor models
large-dimensional asymptotics
Markowitz portfolio selection
rotation equivariance
- Event
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Geistige Schöpfung
- (who)
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Ledoit, Olivier
Wolf, Michael
- Event
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Veröffentlichung
- (who)
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University of Zurich, Department of Economics
- (where)
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Zurich
- (when)
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2020
- DOI
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doi:10.5167/uzh-170642
- Handle
- Last update
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10.03.2025, 11:42 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Ledoit, Olivier
- Wolf, Michael
- University of Zurich, Department of Economics
Time of origin
- 2020
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The power of (non-)linear shrinking: A review and guide to covariance matrix estimation
Direct nonlinear shrinkage estimation of large-dimensional covariance matrices
Shrinkage estimation of large covariance matrices: Keep it simple, statistician?
Shrinkage estimation of large covariance matrices: Keep it simple, statistician?
Shrinkage estimation of large covariance matrices: Keep it simple, statistician?
Spectrum estimation: A unified framework for covariance matrix estimation and PCA in large dimensions
Optimal estimation of a large-dimensional covariance matrix under Stein's loss
Optimal estimation of a large-dimensional covariance matrix under Stein's loss
Optimal estimation of a large-dimensional covariance matrix under Stein's loss
Nonlinear shrinkage of the covariance matrix for portfolio selection: Markowitz meets Goldilocks
Nonlinear shrinkage of the covariance matrix for portfolio selection: Markowitz meets Goldilocks
Quadratic shrinkage for large covariance matrices
The power of (non-)linear shrinking: A review and guide to covariance matrix estimation
Direct nonlinear shrinkage estimation of large-dimensional covariance matrices
Shrinkage estimation of large covariance matrices: Keep it simple, statistician?
Shrinkage estimation of large covariance matrices: Keep it simple, statistician?
Shrinkage estimation of large covariance matrices: Keep it simple, statistician?
Spectrum estimation: A unified framework for covariance matrix estimation and PCA in large dimensions
Optimal estimation of a large-dimensional covariance matrix under Stein's loss
Optimal estimation of a large-dimensional covariance matrix under Stein's loss
Optimal estimation of a large-dimensional covariance matrix under Stein's loss
Nonlinear shrinkage of the covariance matrix for portfolio selection: Markowitz meets Goldilocks
Nonlinear shrinkage of the covariance matrix for portfolio selection: Markowitz meets Goldilocks