Extension of random matrix theory to the L-moments for robust portfolio allocation

Ghislain Yanou ()
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Ghislain Yanou: Centre d'Economie de la Sorbonne, https://centredeconomiesorbonne.univ-paris1.fr

Documents de travail du Centre d'Economie de la Sorbonne from Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne

Abstract: In 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

Keywords: Covariance matrix; Lvariance-covariance; Lcorrelation; concomitance; random matrix theory (search for similar items in EconPapers)
JEL-codes: G10 G11 (search for similar items in EconPapers)
Pages: 41 pages
Date: 2008-12
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:mse:cesdoc:bla08103

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