Extension of Random Matrix Theory to the L-moments for Robust Portfolio Allocation

Ghislain Yanou ()
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Ghislain Yanou: CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique

Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) from HAL

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; L-variance-covariance; L-correlation; concomitance; Random matrix theory; Matrice de variance-covariance; théorie de la matrice aléatoire; Covariance Matrix (search for similar items in EconPapers)
Date: 2008-12
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00349205v2
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Published in 2008

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Persistent link: https://EconPapers.repec.org/RePEc:hal:cesptp:halshs-00349205

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