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Thomas Conlon (), H. J. Ruskin and M. Crane
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H. J. Ruskin: School of Computing, Dublin City University, Glasnevin, Dublin 9, Ireland
M. Crane: School of Computing, Dublin City University, Glasnevin, Dublin 9, Ireland

Advances in Complex Systems (ACS), 2009, vol. 12, issue 04n05, 439-454

Abstract: The cross-correlation matrix between equities comprises multiple interactions between traders with varying strategies and time horizons. In this paper, we use the Maximum Overlap Discrete Wavelet Transform to calculate correlation matrices over different time–scales and then explore theeigenvalue spectrumover sliding time-windows. The dynamics of the eigenvalue spectrum at different times and scales provides insight into the interactions between the numerous constituents involved.Eigenvalue dynamics are examined for both medium, and high-frequency equity returns, with the associated correlation structure shown to be dependent on both time and scale. Additionally, theEppseffect is established using this multivariate method and analyzed at longer scales than previously studied. A partition of the eigenvalue time-series demonstrates, at very short scales, the emergence of negative returns when the largest eigenvalue is greatest. Finally, a portfolio optimization shows the importance of time–scale information in the context of risk management.

Keywords: Cross correlation; eigenspectrum analysis; wavelet multiscaling; Epps effect; high-frequency data; econophysics (search for similar items in EconPapers)
Date: 2009
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Working Paper: Multiscaled Cross-Correlation Dynamics in Financial Time-Series (2010) Downloads
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DOI: 10.1142/S0219525909002325

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