IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v527y2019ics0378437119308271.html
   My bibliography  Save this article

Multivariate large deviations spectrum for the multiscale analysis of stock markets

Author

Listed:
  • Wu, Yue
  • Shang, Pengjian

Abstract

We extend the classical roughness exponent (detrended fluctuation analysis) into multivariate signals, aiming at detecting the effectiveness and advantages of the multivariate multifractal detrended fluctuation analysis (MMDFA) based large deviations spectrum. Results show that large deviations spectrum is sensitive to non-concavities and contains more information than the Legendre spectrum. Further, we investigate the large deviations spectrum for stock markets with the closing prices and volumes. Volumes have a tendency of scale invariance. For global markets, we construct 9 univariate series and 3 multivariate time series separately for Asia, Europe and America and quantify the scale invariance and generating mechanism of multifractality for multivariate stock markets through comparison. It is shown that markets of the same region have a more similar evolution and the q ranges for slope in the fit process are different from each other for both univariate and multivariate situations. Besides, European markets have better scaling properties.

Suggested Citation

  • Wu, Yue & Shang, Pengjian, 2019. "Multivariate large deviations spectrum for the multiscale analysis of stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    • Handle: RePEc:eee:phsmap:v:527:y:2019:i:c:s0378437119308271
    DOI: 10.1016/j.physa.2019.121423
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437119308271
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2019.121423?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:eee:phsmap:v:527:y:2019:i:c:s0378437119308271. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.