Shrinkage and spectral filtering of correlation matrices: a comparison via the Kullback-Leibler distance

M. Tumminello, F. Lillo and Rosario Mantegna

Papers from arXiv.org

Abstract: The problem of filtering information from large correlation matrices is of great importance in many applications. We have recently proposed the use of the Kullback-Leibler distance to measure the performance of filtering algorithms in recovering the underlying correlation matrix when the variables are described by a multivariate Gaussian distribution. Here we use the Kullback-Leibler distance to investigate the performance of filtering methods based on Random Matrix Theory and on the shrinkage technique. We also present some results on the application of the Kullback-Leibler distance to multivariate data which are non Gaussian distributed.

Date: 2007-10
References: Add references at CitEc
Citations: View citations in EconPapers (7)

Published in Acta Phys. Pol. B 38 (13), 4079-4088 (2007)

Downloads: (external link)
http://arxiv.org/pdf/0710.0576 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:0710.0576

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().