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Random Matrix Theory And Financial Correlations

Author

Listed:
  • 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)

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.

Suggested Citation

  • Laurent Laloux & Pierre Cizeau & Marc Potters & Jean-Philippe Bouchaud, 2000. "Random Matrix Theory And Financial Correlations," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 3(03), pages 391-397.
    • Handle: RePEc:wsi:ijtafx:v:03:y:2000:i:03:n:s0219024900000255
    DOI: 10.1142/S0219024900000255
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