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Signal and noise in financial correlation matrices

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  • Burda, Zdzisław
  • Jurkiewicz, Jerzy

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.

Suggested Citation

  • Burda, Zdzisław & Jurkiewicz, Jerzy, 2004. "Signal and noise in financial correlation matrices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(1), pages 67-72.
    • Handle: RePEc:eee:phsmap:v:344:y:2004:i:1:p:67-72
    DOI: 10.1016/j.physa.2004.06.089
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    References listed on IDEAS

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    1. Pafka, Szilárd & Kondor, Imre, 2003. "Noisy covariance matrices and portfolio optimization II," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 319(C), pages 487-494.
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    Cited by:

    1. 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.
    2. Núñez-Mora, José Antonio & Mata-Mata, Leovardo, 2014. "Una aplicación de la teoría de matrices aleatorias para analizar la variación del rendimiento de diferentes commodities a lo largo del periodo 2000-2012," eseconomía, Escuela Superior de Economía, Instituto Politécnico Nacional, vol. 0(41), pages 7-20, segundo s.
    3. 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.
    4. 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.
    5. 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.
    6. Peter Sinka & Peter J. Zeitsch, 2022. "Hedge Effectiveness of the Credit Default Swap Indices: a Spectral Decomposition and Network Topology Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 60(4), pages 1375-1412, December.
    7. Juan Pineiro-Chousa & Marcos Vizcaíno-González & Jérôme Caby, 2016. "Analysing voting behaviour in the United States banking sector through eigenvalue decomposition," Applied Economics Letters, Taylor & Francis Journals, vol. 23(12), pages 840-843, August.
    8. 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.

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