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Contribution Details

Type Working Paper
Scope Discipline-based scholarship
Title The power of (non-)linear shrinking: a review and guide to covariance matrix estimation
Organization Unit
  • Olivier Ledoit
  • Michael Wolf
  • English
Institution University of Zurich
Series Name Working paper series / Department of Economics
Number 323
ISSN 1664-705X
Number of Pages 41
Date 2020
Abstract Text 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.
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Keywords Dynamic conditional correlations, factor models, large-dimensional asymptotics, Markowitz portfolio selection, rotation equivariance, Leverage-Effekt, Portfolio Selection, Lineare Schätztheorie, Nichtlineare Schätzung
Additional Information Revised version