Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/111195
Authors: 
Ledoit, Olivier
Wolf, Michael
Year of Publication: 
2014
Series/Report no.: 
Working Paper Series, University of Zurich, Department of Economics 137
Abstract: 
Markowitz (1952) portfolio selection requires estimates of (i) the vector of expected returns and (ii) the covariance matrix of returns. Many proposals to address the first question exist already. This paper addresses the second question. We promote a new nonlinear shrinkage estimator of the covariance matrix that is more flexible than previous linear shrinkage estimators and has 'just the right number' of free parameters (that is, the Goldilocks principle). In a stylized setting, the nonlinear shrinkage estimator is asymptotically optimal for portfolio selection. In addition to theoretical analysis, we establish superior real-life performance of our new estimator using backtest exercises.
Subjects: 
Large-dimensional asymptotics
Markowitz portfolio selection
nonlinear shrinkage
JEL: 
C13
G11
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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