R-NL: Covariance Matrix Estimation for Elliptical Distributions Based on Nonlinear Shrinkage

被引：2

作者：

Hediger, Simon ^{[1
]}

Naf, Jeffrey ^{[2
]}

Wolf, Michael ^{[1
]}

机构：

[1] Univ Zurich, Dept Econ, CH-8006 Zurich, Switzerland

[2] Premed Inria Inserm, F-34090 Montpellier, France

来源：

IEEE TRANSACTIONS ON SIGNAL PROCESSING | 2023年 / 71卷

关键词：

Covariance matrices; Eigenvalues and eigenfunctions; Heavily-tailed distribution; Tail; Estimation; Dispersion; Convergence; Heavy tails; nonlinear shrinkage; portfolio optimization; OPTIMIZATION; EIGENVALUES;

D O I：

10.1109/TSP.2023.3270742

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

We combine Tyler's robust estimator of the dispersion matrix with nonlinear shrinkage. This approach delivers a simple and fast estimator of the dispersion matrix in elliptical models that is robust against both heavy tails and high dimensions. We prove convergence of the iterative part of our algorithm and demonstrate the favorable performance of the estimator in a wide range of simulation scenarios. Finally, an empirical application demonstrates its state-of-the-art performance on real data.

引用

页码：1657 / 1668

页数：12

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