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Contribution Details

Type Working Paper
Scope Discipline-based scholarship
Title Nonlinear shrinkage of the covariance matrix for portfolio selection: Markowitz meets Goldilocks
Organization Unit
  • Olivier Ledoit
  • Michael Wolf
  • English
Institution University of Zurich
Series Name Working paper series / Department of Economics
Number 137
ISSN 1664-7041
Number of Pages 59
Date 2017
Abstract Text 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.
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Keywords Large-dimensional asymptotics, Markowitz portfolio selection, nonlinear shrinkage, Portfolio-Investition, Portfolio Selection, Kovarianzmatrix
Additional Information Revised version