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Asymmetric correlation matrices: an analysis of financial data

  • Giacomo Livan
  • Luca Rebecchi

We analyze the spectral properties of correlation matrices between distinct statistical systems. Such matrices are intrinsically non symmetric, and lend themselves to extend the spectral analyses usually performed on standard Pearson correlation matrices to the realm of complex eigenvalues. We employ some recent random matrix theory results on the average eigenvalue density of this type of matrices to distinguish between noise and non trivial correlation structures, and we focus on financial data as a case study. Namely, we employ daily prices of stocks belonging to the American and British stock exchanges, and look for the emergence of correlations between two such markets in the eigenvalue spectrum of their non symmetric correlation matrix. We find several non trivial results, also when considering time-lagged correlations over short lags, and we corroborate our findings by additionally studying the asymmetric correlation matrix of the principal components of our datasets.

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File URL: http://arxiv.org/pdf/1201.6535
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Paper provided by arXiv.org in its series Papers with number 1201.6535.

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Date of creation: Jan 2012
Date of revision: Apr 2012
Publication status: Published in Eur. Phys. J. B 85, 213 (2012)
Handle: RePEc:arx:papers:1201.6535
Contact details of provider: Web page: http://arxiv.org/

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  1. Livan, Giacomo & Alfarano, Simone & Scalas, Enrico, 2011. "The fine structure of spectral properties for random correlation matrices: an application to financial markets," MPRA Paper 28964, University Library of Munich, Germany.
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