Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/162407
Authors: 
Ledoit, Olivier
Wolf, Michael
Year of Publication: 
2017
Series/Report no.: 
Working Paper Series, University of Zurich, Department of Economics 137
Abstract: 
Markowitz (1952) portfolio selection requires an estimator of the covariance matrix of returns. To address this problem, we promote a nonlinear shrinkage estimator that is more flexible than previous linear shrinkage estimators and has just the right number of free parameters (that is, the Goldilocks principle). This number is the same as the number of assets. Our nonlinear shrinkage estimator is asymptotically optimal for portfolio selection when the number of assets is of the same magnitude as the sample size. In backtests with historical stock return data, it performs better than previous proposals and, in particular, it dominates linear shrinkage.
Subjects: 
Large-dimensional asymptotics
Markowitz portfolio selection
nonlinear shrinkage
JEL: 
C13
C58
G11
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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