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Random, but not so much: A parameterization for the returns and correlation matrix of financial time series

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  • Andre C. R. Martins

Abstract

A parameterization that is a modified version of a previous work is proposed for the returns and correlation matrix of financial time series and its properties are studied. This parameterization allows easy introduction of non-stationarity and it shows several of the characteristics of the true, observed realizations, such as fat tails, volatility clustering, and a spectrum of eigenvalues of the correlation matrix that can be understood as an extension of Random Matrix Theory results. The predicted behavior of this parameterization for the eigenvalues is compared with the eigenvalues of Brazilian assets and it is shown that those predictions fit the data better than Random Matrix Theory.

Suggested Citation

  • Andre C. R. Martins, 2007. "Random, but not so much: A parameterization for the returns and correlation matrix of financial time series," Papers physics/0701025, arXiv.org.
    • Handle: RePEc:arx:papers:physics/0701025
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    Cited by:

    1. Sandoval, Leonidas & Franca, Italo De Paula, 2012. "Correlation of financial markets in times of crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(1), pages 187-208.
    2. Ormerod, Paul, 2008. "Random Matrix Theory and Macro-Economic Time-Series: An Illustration Using the Evolution of Business Cycle Synchronisation, 1886-2006," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 2, pages 1-10.
    3. Camilo Rodrigues Neto & Andr' e C. R. Martins, 2007. "Multifractality in the Random Parameters Model," Papers 0710.5497, arXiv.org.
    4. Leonidas Sandoval Junior & Italo De Paula Franca, 2011. "Correlation of financial markets in times of crisis," Papers 1102.1339, arXiv.org, revised Mar 2011.

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