Shrinkage estimation of large covariance matrices: Keep it simple, statistician? - Olivier Ledoit; Michael Wolf (University of Zurich, Department of Economics, 2021)
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: C13; 330; Large-dimensional asymptotics; random matrix theory; rotation equivariance; Kovarianzfunktion; Risikomanagement; Verlust; Modellierung; Eigenwert; Monte-Carlo-Simulation