Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/200186
Authors: 
Ledoit, Olivier
Wolf, Michael
Year of Publication: 
2019
Series/Report no.: 
Working Paper 323
Abstract: 
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.
Subjects: 
dynamic conditional correlations
factor models
large-dimensional asymptotics
Markowitz portfolio selection
rotation equivariance
JEL: 
C13
C58
G11
Document Type: 
Working Paper
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