Cross-Correlation Dynamics in Financial Time Series
AbstractThe dynamics of the equal-time cross-correlation matrix of multivariate financial time series is explored by examination of the eigenvalue spectrum over sliding time windows. Empirical results for the S&P 500 and the Dow Jones Euro Stoxx 50 indices reveal that the dynamics of the small eigenvalues of the cross-correlation matrix, over these time windows, oppose those of the largest eigenvalue. This behaviour is shown to be independent of the size of the time window and the number of stocks examined. A basic one-factor model is then proposed, which captures the main dynamical features of the eigenvalue spectrum of the empirical data. Through the addition of perturbations to the one-factor model, (leading to a 'market plus sectors' model), additional sectoral features are added, resulting in an Inverse Participation Ratio comparable to that found for empirical data. By partitioning the eigenvalue time series, we then show that negative index returns, (drawdowns), are associated with periods where the largest eigenvalue is greatest, while positive index returns, (drawups), are associated with periods where the largest eigenvalue is smallest. The study of correlation dynamics provides some insight on the collective behaviour of traders with varying strategies.
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Bibliographic InfoPaper provided by arXiv.org in its series Papers with number 1002.0321.
Date of creation: Feb 2010
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Publication status: Published in Physica A 388 (5) (2009), 705-714
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Web page: http://arxiv.org/
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-02-13 (All new papers)
- NEP-ECM-2010-02-13 (Econometrics)
- NEP-ETS-2010-02-13 (Econometric Time Series)
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- Vasiliki Plerou & Parameswaran Gopikrishnan & Bernd Rosenow & Luis A. Nunes Amaral & H. Eugene Stanley, 1999. "Universal and non-universal properties of cross-correlations in financial time series," Papers cond-mat/9902283, arXiv.org.
- Drożdż, S & Grümmer, F & Ruf, F & Speth, J, 2001. "Towards identifying the world stock market cross-correlations: DAX versus Dow Jones," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 294(1), pages 226-234.
- Y. Malevergne & D. Sornette, 2002. "Collective Origin of the Coexistence of Apparent RMT Noise and Factors in Large Sample Correlation Matrices," Papers cond-mat/0210115, arXiv.org.
- Karpoff, Jonathan M., 1987. "The Relation between Price Changes and Trading Volume: A Survey," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 22(01), pages 109-126, March.
- 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.
- Zdzislaw Burda & Jerzy Jurkiewicz, 2003. "Signal and Noise in Financial Correlation Matrices," Papers cond-mat/0312496, arXiv.org, revised Feb 2004.
- Wilcox, Diane & Gebbie, Tim, 2004. "On the analysis of cross-correlations in South African market data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(1), pages 294-298.
- Laurent Laloux & Pierre Cizeau & Jean-Philippe Bouchaud & Marc Potters, 1999. "Random matrix theory and financial correlations," Science & Finance (CFM) working paper archive 500053, Science & Finance, Capital Fund Management.
- Malevergne, Y. & Sornette, D., 2004. "Collective origin of the coexistence of apparent random matrix theory noise and of factors in large sample correlation matrices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 331(3), pages 660-668.
- Laurent Laloux & Pierre Cizeau & Jean-Philippe Bouchaud & Marc Potters, 1998. "Noise dressing of financial correlation matrices," Science & Finance (CFM) working paper archive 500051, Science & Finance, Capital Fund Management.
- repec:att:wimass:9725 is not listed on IDEAS
- LeBaron, Blake & Arthur, W. Brian & Palmer, Richard, 1999.
"Time series properties of an artificial stock market,"
Journal of Economic Dynamics and Control,
Elsevier, vol. 23(9-10), pages 1487-1516, September.
- Drożdż, S & Grümmer, F & Górski, A.Z & Ruf, F & Speth, J, 2000. "Dynamics of competition between collectivity and noise in the stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 287(3), pages 440-449.
- Conlon, T. & Crane, M. & Ruskin, H.J., 2008. "Wavelet multiscale analysis for Hedge Funds: Scaling and strategies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(21), pages 5197-5204.
- Gabor Papp & Szilard Pafka & Maciej A. Nowak & Imre Kondor, 2005. "Random Matrix Filtering in Portfolio Optimization," Papers physics/0509235, arXiv.org.
- Sharifi, S. & Crane, M. & Shamaie, A. & Ruskin, H., 2004. "Random matrix theory for portfolio optimization: a stability approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 335(3), pages 629-643.
- 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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