Signal and noise in correlation matrix
AbstractUsing 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.
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Bibliographic InfoArticle provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.
Volume (Year): 343 (2004)
Issue (Month): C ()
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Web page: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/
Random matrix theory; Correlation matrix; Eigenvalue spectrum;
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- Thomas Conlon & Heather J. Ruskin & Martin Crane, 2010. "Random Matrix Theory and Fund of Funds Portfolio Optimisation," Papers 1005.5021, arXiv.org.
- Jean-Philippe Bouchaud & Laurent Laloux & M. Augusta Miceli & Marc Potters, 2005.
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500066, Science & Finance, Capital Fund Management.
- Jean-Philippe Bouchaud & Laurent Laloux & M. Augusta Miceli & Marc Potters, 2005. "Large dimension forecasting models and random singular value spectra," Papers physics/0512090, arXiv.org.
- 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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