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Noise dressing of financial correlation matrices

  • Laurent Laloux

    (Science & Finance, Capital Fund Management)

  • Pierre Cizeau

    (Science & Finance, Capital Fund Management)

  • Jean-Philippe Bouchaud

    (Science & Finance, Capital Fund Management
    CEA Saclay;)

  • Marc Potters

    (Science & Finance, Capital Fund Management)

We show that results from the theory of random matrices are potentially of great interest to understand the statistical structure of the empirical correlation matrices appearing in the study of price fluctuations. The central result of the present study is the 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). In particular the present study raises serious doubts on the blind use of empirical correlation matrices for risk management.

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Paper provided by Science & Finance, Capital Fund Management in its series Science & Finance (CFM) working paper archive with number 500051.

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Date of creation: Oct 1998
Date of revision:
Publication status: Published in Physical Review Letters 83(7), 1467 (1999)
Handle: RePEc:sfi:sfiwpa:500051
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