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Dynamic correlations at different time-scales with empirical mode decomposition

Author

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  • Nava, Noemi
  • Di Matteo, T.
  • Aste, Tomaso

Abstract

We introduce a simple approach which combines Empirical Mode Decomposition (EMD) and Pearson’s cross-correlations over rolling windows to quantify dynamic dependency at different time scales. The EMD is a tool to separate time series into implicit components which oscillate at different time-scales. We apply this decomposition to intraday time series of the following three financial indices: the S&P 500 (USA), the IPC (Mexico) and the VIX (volatility index USA), obtaining time-varying multidimensional cross-correlations at different time-scales. The correlations computed over a rolling window are compared across the three indices, across the components at different time-scales and across different time lags. We uncover a rich heterogeneity of interactions, which depends on the time-scale and has important lead–lag relations that could have practical use for portfolio management, risk estimation and investment decisions.

Suggested Citation

  • Nava, Noemi & Di Matteo, T. & Aste, Tomaso, 2018. "Dynamic correlations at different time-scales with empirical mode decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 534-544.
  • Handle: RePEc:eee:phsmap:v:502:y:2018:i:c:p:534-544
    DOI: 10.1016/j.physa.2018.02.108
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    References listed on IDEAS

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    1. Nicolo Musmeci & Tomaso Aste & Tiziana Di Matteo, 2014. "Relation between Financial Market Structure and the Real Economy: Comparison between Clustering Methods," Papers 1406.0496, arXiv.org, revised Jan 2015.
    2. Aymanns, Christoph & Caccioli, Fabio & Farmer, J. Doyne & Tan, Vincent W.C., 2016. "Taming the Basel leverage cycle," Journal of Financial Stability, Elsevier, vol. 27(C), pages 263-277.
    3. Christian Borghesi & Matteo Marsili & Salvatore Miccich`e, 2007. "Emergence of time-horizon invariant correlation structure in financial returns by subtraction of the market mode," Papers physics/0702106, arXiv.org.
    4. Musmeci, Nicoló & Aste, Tomaso & Di Matteo, T., 2015. "Relation between financial market structure and the real economy: comparison between clustering methods," LSE Research Online Documents on Economics 61644, London School of Economics and Political Science, LSE Library.
    5. Chester Curme & Michele Tumminello & Rosario N. Mantegna & H. Eugene Stanley & Dror Y. Kenett, 2015. "Emergence of statistically validated financial intraday lead-lag relationships," Quantitative Finance, Taylor & Francis Journals, vol. 15(8), pages 1375-1386, August.
    6. Istvan Varga-Haszonits & Fabio Caccioli & Imre Kondor, 2016. "Replica approach to mean-variance portfolio optimization," Papers 1606.08679, arXiv.org.
    7. M. Bartolozzi & C. Mellen & T. Di Matteo & T. Aste, 2007. "Multi-scale correlations in different futures markets," Papers 0707.3321, arXiv.org, revised Aug 2007.
    8. Varga-Haszonits, Istvan & Caccioli, Fabio & Kondor, Imre, 2016. "Replica approach to mean-variance portfolio optimization," LSE Research Online Documents on Economics 68955, London School of Economics and Political Science, LSE Library.
    9. Nicoló Musmeci & Tomaso Aste & T Di Matteo, 2015. "Relation between Financial Market Structure and the Real Economy: Comparison between Clustering Methods," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-24, March.
    10. M. Tumminello & T. Di Matteo & T. Aste & R. N. Mantegna, 2007. "Correlation based networks of equity returns sampled at different time horizons," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 55(2), pages 209-217, January.
    11. Mantegna,Rosario N. & Stanley,H. Eugene, 2007. "Introduction to Econophysics," Cambridge Books, Cambridge University Press, number 9780521039871, October.
    12. M. Bartolozzi & C. Mellen & T. Di Matteo & T. Aste, 2007. "Multi-scale correlations in different futures markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 58(2), pages 207-220, July.
    13. Aste, T. & Di Matteo, T., 2006. "Dynamical networks from correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 370(1), pages 156-161.
    14. Longin, Francois & Solnik, Bruno, 1995. "Is the correlation in international equity returns constant: 1960-1990?," Journal of International Money and Finance, Elsevier, vol. 14(1), pages 3-26, February.
    15. Musmeci, Nicoló & Nicosia, Vincenzo & Aste, Tomaso & Di Matteo, Tiziana & Latora, Vito, 2017. "The multiplex dependency structure of financial markets," LSE Research Online Documents on Economics 85337, London School of Economics and Political Science, LSE Library.
    16. Dror Y. Kenett & Xuqing Huang & Irena Vodenska & Shlomo Havlin & H. Eugene Stanley, 2015. "Partial correlation analysis: applications for financial markets," Quantitative Finance, Taylor & Francis Journals, vol. 15(4), pages 569-578, April.
    17. Zhang, Xiaonei & Zeng, Ming & Meng, Qinghao, 2018. "Multivariate multifractal detrended fluctuation analysis of 3D wind field signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 513-523.
    18. Uechi, Lisa & Akutsu, Tatsuya & Stanley, H. Eugene & Marcus, Alan J. & Kenett, Dror Y., 2015. "Sector dominance ratio analysis of financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 488-509.
    19. Tomaso Aste & T. Di Matteo, 2017. "Sparse Causality Network Retrieval from Short Time Series," Complexity, Hindawi, vol. 2017, pages 1-13, November.
    20. Kantelhardt, Jan W. & Zschiegner, Stephan A. & Koscielny-Bunde, Eva & Havlin, Shlomo & Bunde, Armin & Stanley, H.Eugene, 2002. "Multifractal detrended fluctuation analysis of nonstationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 87-114.
    21. Rua, António & Nunes, Luís C., 2009. "International comovement of stock market returns: A wavelet analysis," Journal of Empirical Finance, Elsevier, vol. 16(4), pages 632-639, September.
    22. Nicol'o Musmeci & Tomaso Aste & Tiziana Di Matteo, 2014. "Risk diversification: a study of persistence with a filtered correlation-network approach," Papers 1410.5621, arXiv.org.
    23. Nicolò Musmeci & Vincenzo Nicosia & Tomaso Aste & Tiziana Di Matteo & Vito Latora, 2017. "The Multiplex Dependency Structure of Financial Markets," Complexity, Hindawi, vol. 2017, pages 1-13, September.
    24. Fabio Caccioli & Imre Kondor & Matteo Marsili & Susanne Still, 2016. "Liquidity Risk And Instabilities In Portfolio Optimization," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 19(05), pages 1-28, August.
    25. Daniel J. Fenn & Mason A. Porter & Stacy Williams & Mark McDonald & Neil F. Johnson & Nick S. Jones, 2010. "Temporal Evolution of Financial Market Correlations," Papers 1011.3225, arXiv.org, revised May 2011.
    26. T. Di Matteo, 2007. "Multi-scaling in finance," Quantitative Finance, Taylor & Francis Journals, vol. 7(1), pages 21-36.
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    6. Mao, Xuegeng & Yang, Albert C. & Peng, Chung-Kang & Shang, Pengjian, 2020. "Analysis of economic growth fluctuations based on EEMD and causal decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
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    8. Miriam Sosa & Edgar Ortiz & Alejandra Cabello, 2022. "ESG Green Equity Finance Risk and Links in Mexico: Conditional Volatility and Markov Switching Vector Analyses," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 17(4), pages 1-21, Octubre -.
    9. Chengzhao, Zhang & Heping, Pan & Yu, Ma & Xun, Huang, 2019. "Analysis of Asia Pacific stock markets with a novel multiscale model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).

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