Extension of Random Matrix Theory to the L-moments for Robust Portfolio Allocation
AbstractIn this paper, we propose a methodology for building an estimator of the covariance matrix. We use a robust measure of moments called L-moments (see hosking, 1986), and their extension into a multivariate framework (see Serfling and Xiao, 2007). Random matrix theory (see Edelman, 1989) allows us to extract factors which contain real information. An empirical study in the American market shows that the Global Minimum L-variance Portfolio (GMLP) obtained from our estimator well performs the Global Minimum Variance Portfolio (GMVP) that acquired from the empirical estimator of the covariance matrix.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by HAL in its series Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) with number halshs-00349205.
Date of creation: Dec 2008
Date of revision:
Note: View the original document on HAL open archive server: http://halshs.archives-ouvertes.fr/halshs-00349205
Contact details of provider:
Web page: http://hal.archives-ouvertes.fr/
Covariance Matrix; L-variance-covariance; L-correlation; concomitance; random matrix theory.;
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Darolles, Serge & Gourieroux, Christian & Jasiak, Joann, 2009. "L-performance with an application to hedge funds," Journal of Empirical Finance, Elsevier, vol. 16(4), pages 671-685, September.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (CCSD).
If references are entirely missing, you can add them using this form.