Random matrix theory and robust covariance matrix estimation for financial data
| dc.creator | Frahm, Gabriel | |
| dc.creator | Jaekel, Uwe | |
| dc.date | 2005-03-01 | |
| dc.date.accessioned | 2026-07-07T05:54:15Z | |
| dc.date.available | 2026-07-07T05:54:15Z | |
| dc.description | The traditional class of elliptical distributions is extended to allow for asymmetries. A completely robust dispersion matrix estimator (the `spectral estimator') for the new class of `generalized elliptical distributions' is presented. It is shown that the spectral estimator corresponds to an M-estimator proposed by Tyler (1983) in the context of elliptical distributions. Both the generalization of elliptical distributions and the development of a robust dispersion matrix estimator are motivated by the stylized facts of empirical finance. Random matrix theory is used for analyzing the linear dependence structure of high-dimensional data. It is shown that the Marcenko-Pastur law fails if the sample covariance matrix is considered as a random matrix in the context of elliptically distributed and heavy tailed data. But substituting the sample covariance matrix by the spectral estimator resolves the problem and the Marcenko-Pastur law remains valid. | |
| dc.identifier | https://arxiv.org/abs/physics/0503007 | |
| dc.identifier | http://arxiv.org/abs/physics/0503007 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/86903 | |
| dc.subject | Physics and Society | |
| dc.title | Random matrix theory and robust covariance matrix estimation for financial data | |
| dc.type | text |