Signal and noise in financial correlation matrices
AbstractUsing 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.
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Bibliographic InfoArticle provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.
Volume (Year): 344 (2004)
Issue (Month): 1 ()
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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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- 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.
- 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.
- Thomas Conlon & Heather J. Ruskin & Martin Crane, 2010. "Cross-Correlation Dynamics in Financial Time Series," Papers 1002.0321, arXiv.org.
- Thomas Conlon & Heather J. Ruskin & Martin Crane, 2010. "Random Matrix Theory and Fund of Funds Portfolio Optimisation," Papers 1005.5021, arXiv.org.
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