IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1708.06586.html
   My bibliography  Save this paper

Dynamic correlations at different time-scales with Empirical Mode Decomposition

Author

Listed:
  • Noemi Nava
  • T. Di Matteo
  • Tomaso Aste

Abstract

The Empirical Mode Decomposition (EMD) provides a tool to characterize time series in terms of its implicit components oscillating 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, at different lags and over time. We uncover a rich heterogeneity of interactions which depends on the time-scale and has important led-lag relations which can have practical use for portfolio management, risk estimation and investments.

Suggested Citation

  • Noemi Nava & T. Di Matteo & Tomaso Aste, 2017. "Dynamic correlations at different time-scales with Empirical Mode Decomposition," Papers 1708.06586, arXiv.org.
  • Handle: RePEc:arx:papers:1708.06586
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1708.06586
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Mantegna,Rosario N. & Stanley,H. Eugene, 2007. "Introduction to Econophysics," Cambridge Books, Cambridge University Press, number 9780521039871, October.
    4. 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.
    5. Aste, T. & Di Matteo, T., 2006. "Dynamical networks from correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 370(1), pages 156-161.
    6. 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.
    7. 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.
    8. 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.
    9. T. Di Matteo, 2007. "Multi-scaling in finance," Quantitative Finance, Taylor & Francis Journals, vol. 7(1), pages 21-36.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nava, Noemi & Di Matteo, Tiziana & Aste, Tomaso, 2018. "Financial time series forecasting using empirical mode decomposition and support vector regression," LSE Research Online Documents on Economics 91028, London School of Economics and Political Science, LSE Library.
    2. Noemi Nava & Tiziana Di Matteo & Tomaso Aste, 2018. "Financial Time Series Forecasting Using Empirical Mode Decomposition and Support Vector Regression," Risks, MDPI, vol. 6(1), pages 1-21, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Ioannis P. Antoniades & Giuseppe Brandi & L. G. Magafas & T. Di Matteo, 2020. "The use of scaling properties to detect relevant changes in financial time series: a new visual warning tool," Papers 2010.08890, arXiv.org, revised Dec 2020.
    3. Antoniades, I.P. & Brandi, Giuseppe & Magafas, L. & Di Matteo, T., 2021. "The use of scaling properties to detect relevant changes in financial time series: A new visual warning tool," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    4. Lyócsa, Štefan & Výrost, Tomáš & Baumöhl, Eduard, 2019. "Return spillovers around the globe: A network approach," Economic Modelling, Elsevier, vol. 77(C), pages 133-146.
    5. Barunik, Jozef & Aste, Tomaso & Di Matteo, T. & Liu, Ruipeng, 2012. "Understanding the source of multifractality in financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(17), pages 4234-4251.
    6. 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.
    7. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485, arXiv.org, revised Nov 2020.
    8. 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.
    9. Francesco Caravelli & James Requeima & Cozmin Ududec & Ali Ashtari & Tiziana Di Matteo & Tomaso Aste, 2015. "Multi-scaling of wholesale electricity prices," Papers 1507.06219, arXiv.org.
    10. 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.
    11. Nava, Noemi & Di Matteo, T. & Aste, Tomaso, 2016. "Anomalous volatility scaling in high frequency financial data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 434-445.
    12. Morales, Raffaello & Di Matteo, T. & Gramatica, Ruggero & Aste, Tomaso, 2012. "Dynamical generalized Hurst exponent as a tool to monitor unstable periods in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(11), pages 3180-3189.
    13. Morales, Raffaello & Di Matteo, T. & Aste, Tomaso, 2013. "Non-stationary multifractality in stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(24), pages 6470-6483.
    14. Esmalifalak, Hamidreza, 2022. "Euclidean (dis)similarity in financial network analysis," Global Finance Journal, Elsevier, vol. 53(C).
    15. Noemi Nava & T. Di Matteo & Tomaso Aste, 2015. "Anomalous volatility scaling in high frequency financial data," Papers 1503.08465, arXiv.org, revised Dec 2015.
    16. 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.
    17. Marco Bartolozzi, 2010. "A Multi Agent Model for the Limit Order Book Dynamics," Papers 1005.0182, arXiv.org, revised Oct 2010.
    18. Chun-Xiao Nie, 2021. "Studying the correlation structure based on market geometry," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 16(2), pages 411-441, April.
    19. Marfatia, Hardik A., 2017. "A fresh look at integration of risks in the international stock markets: A wavelet approach," Review of Financial Economics, Elsevier, vol. 34(C), pages 33-49.
    20. Wu, Kai & Zhu, Jingran & Xu, Mingli & Yang, Lu, 2020. "Can crude oil drive the co-movement in the international stock market? Evidence from partial wavelet coherence analysis," The North American Journal of Economics and Finance, Elsevier, vol. 53(C).

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:1708.06586. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.