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Shrinkage estimation of large covariance matrices: keep it simple, statistician?

Olivier Ledoit and Michael Wolf

No 327, ECON - Working Papers from Department of Economics - University of Zurich

Abstract: Under rotation-equivariant decision theory, sample covariance matrix eigenvalues can be optimally shrunk by recombining sample eigenvectors with a (potentially nonlinear) function of the unobservable population covariance matrix. The optimal shape of this function reflects the loss/risk that is to be minimized. We solve the problem of optimal covariance matrix estimation under a variety of loss functions motivated by statistical precedent, probability theory, and differential geometry. A key ingredient of our nonlinear shrinkage methodology is a new estimator of the angle between sample and population eigenvectors, without making strong assumptions on the population eigenvalues. We also introduce a broad family of covariance matrix estimators that can handle all regular functional transformations of the population covariance matrix under large-dimensional asymptotics. In addition, we compare via Monte Carlo simulations our methodology to two simpler ones from the literature, linear shrinkage and shrinkage based on the spiked covariance model.

Keywords: Large-dimensional asymptotics; random matrix theory; rotation equivariance (search for similar items in EconPapers)
JEL-codes: C13 (search for similar items in EconPapers)
Date: 2019-07, Revised 2021-06
New Economics Papers: this item is included in nep-ore
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