RANDOM MATRIX THEORY AND FINANCIAL CORRELATIONS

Laurent Laloux, Pierre Cizeau, Marc Potters and Jean-Philippe Bouchaud
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Laurent Laloux: Science & Finance, 109-111 rue Victor Hugo, 92532 Levallois Cedex, France
Pierre Cizeau: Science & Finance, 109-111 rue Victor Hugo, 92532 Levallois Cedex, France
Marc Potters: Science & Finance, 109-111 rue Victor Hugo, 92532 Levallois Cedex, France
Jean-Philippe Bouchaud: Science & Finance, and Service de Physique de l'État Condensé, Centre d'études de Saclay, Orme des Merisiers, 91191 Gif-s-Yvette cédex, France

International Journal of Theoretical and Applied Finance (IJTAF), 2000, vol. 03, issue 03, 391-397

Abstract: We show that results from the theory of random matrices are potentially of great interest when trying to understand the statistical structure of the empirical correlation matrices appearing in the study of multivariate financial time series. We find a remarkable agreement between the theoretical prediction (based on the assumption that the correlation matrix is random) and empirical data concerning the density of eigenvalues associated to the time series of the different stocks of the S&P500 (or other major markets). Finally, we give a specific example to show how this idea can be sucessfully implemented for improving risk management.

Date: 2000
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Citations: View citations in EconPapers (86)

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DOI: 10.1142/S0219024900000255

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