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Signal and noise in correlation matrix

Author

Listed:
  • Burda, Z.
  • Görlich, A.
  • Jarosz, A.
  • Jurkiewicz, J.

Abstract

Using random matrix technique we determine an exact relation between the eigenvalue spectrum of the covariance matrix and of its estimator. This relation can be used in practice to compute eigenvalue invariants of the covariance (correlation) matrix. Results can be applied in various problems where one experimentally estimates correlations in a system with many degrees of freedom, like for instance those in statistical physics, lattice measurements of field theory, genetics, quantitative finance and other applications of multivariate statistics.

Suggested Citation

  • Burda, Z. & Görlich, A. & Jarosz, A. & Jurkiewicz, J., 2004. "Signal and noise in correlation matrix," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 343(C), pages 295-310.
    • Handle: RePEc:eee:phsmap:v:343:y:2004:i:c:p:295-310
    DOI: 10.1016/j.physa.2004.05.048
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    Citations

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    Cited by:

    1. S. Valeyre & D. S. Grebenkov & S. Aboura, 2018. "Emergence of correlations between securities at short time scales," Papers 1807.05015, arXiv.org.
    2. Martins, André C.R., 2007. "Non-stationary correlation matrices and noise," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 379(2), pages 552-558.
    3. Raddant, Matthias & Wagner, Friedrich, 2013. "Phase transition in the S&P stock market," Kiel Working Papers 1846, Kiel Institute for the World Economy (IfW Kiel).
    4. Fabio Caccioli & Imre Kondor & Matteo Marsili & Susanne Still, 2014. "$L_p$ regularized portfolio optimization," Papers 1404.4040, arXiv.org.
    5. Varga-Haszonits, I. & Kondor, I., 2007. "Noise sensitivity of portfolio selection in constant conditional correlation GARCH models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 385(1), pages 307-318.
    6. Jovanovic, Franck & Mantegna, Rosario N. & Schinckus, Christophe, 2019. "When financial economics influences physics: The role of Econophysics," International Review of Financial Analysis, Elsevier, vol. 65(C).
    7. Sebastien Valeyre & Denis S Grebenkov & Sofiane Aboura, 2019. "Emergence of correlations between securities at short time scales," Post-Print hal-02343888, HAL.
    8. Dalibor Eterovic & Nicolas Eterovic, 2012. "Separating the Wheat from the Chaff: Understanding Portfolio Returns in an Emerging Market," Working Papers wp_025, Adolfo Ibáñez University, School of Government.
    9. Jean-Philippe Bouchaud & Laurent Laloux & M. Augusta Miceli & Marc Potters, 2005. "Large dimension forecasting models and random singular value spectra," Science & Finance (CFM) working paper archive 500066, Science & Finance, Capital Fund Management.
    10. 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.
    11. Joel Bun & Jean-Philippe Bouchaud & Marc Potters, 2016. "Cleaning large correlation matrices: tools from random matrix theory," Papers 1610.08104, arXiv.org.
    12. 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.
    13. Fabio Caccioli & Imre Kondor & Matteo Marsili & Susanne Still, 2016. "Liquidity Risk And Instabilities In Portfolio Optimization," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 19(05), pages 1-28, August.
    14. Giacomo Livan & Jun-ichi Inoue & Enrico Scalas, 2012. "On the non-stationarity of financial time series: impact on optimal portfolio selection," Papers 1205.0877, arXiv.org, revised Jul 2012.
    15. Nick James, 2021. "Evolutionary correlation, regime switching, spectral dynamics and optimal trading strategies for cryptocurrencies and equities," Papers 2112.15321, arXiv.org, revised Mar 2022.

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