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Multiscaled Cross-Correlation Dynamics in Financial Time-Series

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  • Thomas Conlon
  • Heather J. Ruskin
  • Martin Crane

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 timescales and then explore the eigenvalue spectrum over 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, the Epps effect 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 timescale information in the context of risk management.

Suggested Citation

  • Thomas Conlon & Heather J. Ruskin & Martin Crane, 2010. "Multiscaled Cross-Correlation Dynamics in Financial Time-Series," Papers 1001.0497, arXiv.org.
    • Handle: RePEc:arx:papers:1001.0497
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    1. Ren, Yinghua & Zhao, Wanru & You, Wanhai & Zhai, Kaikai, 2021. "Multiscale and partial correlation networks analysis of risk connectedness in global equity markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    2. Gang-Jin Wang & Chi Xie & Shou Chen, 2017. "Multiscale correlation networks analysis of the US stock market: a wavelet analysis," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 12(3), pages 561-594, October.
    3. Silvo Dajcman, 2012. "The Dynamics of Return Comovement and Spillovers Between the Czech and European Stock Markets in the Period 1997–2010," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 62(4), pages 368-390, August.
    4. Jovanovic, Franck & Mantegna, Rosario N. & Schinckus, Christophe, 2019. "When financial economics influences physics: The role of Econophysics," International Review of Financial Analysis, Elsevier, vol. 65(C).
    5. Bommarito, Michael J. & Duran, Ahmet, 2018. "Spectral analysis of time-dependent market-adjusted return correlation matrix," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 273-282.
    6. S. Valeyre & D. S. Grebenkov & S. Aboura, 2018. "Emergence of correlations between securities at short time scales," Papers 1807.05015, arXiv.org.
    7. Zhang, Yiting & Lee, Gladys Hui Ting & Wong, Jian Cheng & Kok, Jun Liang & Prusty, Manamohan & Cheong, Siew Ann, 2011. "Will the US economy recover in 2010? A minimal spanning tree study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(11), pages 2020-2050.
    8. Wang, Yan-Jun & Zhu, Yun-Feng & Zhu, Chen-Ping & Wu, Fan & Yang, Hui-Jie & Yan, Yong-Jie & Hu, Chin-Kun, 2019. "Indicator of serious flight delays with the approach of time-delay stability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 518(C), pages 363-373.
    9. Henryk Gurgul & Artur Machno, 2017. "The impact of asynchronous trading on Epps effect on Warsaw Stock Exchange," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 25(2), pages 287-301, June.
    10. Nie, Chun-Xiao & Song, Fu-Tie, 2019. "Global Rényi index of the distance matrix," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 902-915.
    11. Henryk Gurgul & Artur Machno, 2016. "The impact of asynchronous trading on Epps effect. Comparative study on Warsaw Stock Exchange and Vienna Stock Exchange," Managerial Economics, AGH University of Science and Technology, Faculty of Management, vol. 17(1), pages 57-75.
    12. Sebastien Valeyre & Denis S Grebenkov & Sofiane Aboura, 2019. "Emergence of correlations between securities at short time scales," Post-Print hal-02343888, HAL.
    13. Ren, Yinghua & Zhao, Wanru & You, Wanhai & Zhu, Huiming, 2022. "Multiscale features of extreme risk spillover networks among global stock markets," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    14. Shen, Keren & Yao, Jianfeng & Li, Wai Keung, 2019. "On a spiked model for large volatility matrix estimation from noisy high-frequency data," Computational Statistics & Data Analysis, Elsevier, vol. 131(C), pages 207-221.

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