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Signal and Noise in Financial Correlation Matrices

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  • Zdzislaw Burda
  • Jerzy Jurkiewicz
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    Abstract

    Using Random Matrix Theory one can derive exact relations between the eigenvalue spectrum of the covariance matrix and the eigenvalue spectrum of its estimator (experimentally measured correlation matrix). These relations will be used to analyze a particular case of the correlations in financial series and to show that contrary to earlier claims, correlations can be measured also in the ``random'' part of the spectrum. Implications for the portfolio optimization are briefly discussed.

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    File URL: http://arxiv.org/pdf/cond-mat/0312496
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    Bibliographic Info

    Paper provided by arXiv.org in its series Papers with number cond-mat/0312496.

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    Date of creation: Dec 2003
    Date of revision: Feb 2004
    Publication status: Published in Physica A 344, 67 (2004)
    Handle: RePEc:arx:papers:cond-mat/0312496

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    Web page: http://arxiv.org/

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    Cited by:
    1. Thomas Conlon & Heather J. Ruskin & Martin Crane, 2010. "Cross-Correlation Dynamics in Financial Time Series," Papers 1002.0321, arXiv.org.
    2. Conlon, T. & Ruskin, H.J. & Crane, M., 2009. "Cross-correlation dynamics in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(5), pages 705-714.
    3. Conlon, T. & Ruskin, H.J. & Crane, M., 2007. "Random matrix theory and fund of funds portfolio optimisation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 382(2), pages 565-576.
    4. Eterovic, Nicolas A. & Eterovic, Dalibor S., 2013. "Separating the wheat from the chaff: Understanding portfolio returns in an emerging market," Emerging Markets Review, Elsevier, vol. 16(C), pages 145-169.
    5. Thomas Conlon & Heather J. Ruskin & Martin Crane, 2010. "Random Matrix Theory and Fund of Funds Portfolio Optimisation," Papers 1005.5021, arXiv.org.

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