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| Type | Working Paper |
| Scope | Discipline-based scholarship |
| Title | R-NL: covariance matrix estimation for elliptical distributions based on nonlinear shrinkage |
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| Institution | Cornell University |
| Series Name | ArXiv.org |
| Number | 2210.14854 |
| ISSN | 2331-8422 |
| Number of Pages | 33 |
| Date | 2023 |
| Abstract Text | 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. |
| Free access at | Official URL |
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| Digital Object Identifier | 10.48550/arXiv.2210.14854 |
| Other Identification Number | merlin-id:22884 |
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| Keywords | Heavy tails, nonlinear shrinkage, portfolio optimization |
| Additional Information | Revised version ; Former title: R-NL: fast and robust covariance estimation for elliptical distributions in high dimensions |