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

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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)

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.

Suggested Citation

  • Ghislain Yanou, 2008. "Extension of Random Matrix Theory to the L-moments for Robust Portfolio Allocation," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00349205, HAL.
    • Handle: RePEc:hal:cesptp:halshs-00349205
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00349205v2
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    Cited by:

    1. 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.

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