IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v638y2024ics0378437124001171.html
   My bibliography  Save this article

A permutation entropy analysis of Bitcoin volatility

Author

Listed:
  • Obanya, Praise Otito
  • Seitshiro, Modisane
  • Olivier, Carel Petrus
  • Verster, Tanja

Abstract

Cryptocurrencies are widely regarded as volatile and less predictable assets by financial participants. The behaviour and dynamics of Bitcoin’s daily volatility, obtained by fitting GARCH models, are investigated for a period of 8 years using permutation entropy which is represented by the variable H for calculations. The best fitting GARCH models selected are the FIGARCH(1,0.7,1) and SGARCH(1,1) models based on maximum likelihood estimation, Akaike Information Criterion and Bayesian Information Criterion. Simulated volatilities are also obtained from the best fitting GARCH models using their respective parameters, to confirm how well the models fit. The results obtained show that the H values of Bitcoin are generally low and that the dynamics of Bitcoin’s volatility is quite predictable, as Bitcoin’s volatility is most likely to decline over time than increase or have an alternating movement. Also, the simulated volatilities show good agreement with the real-world volatility, confirming the models as good fits.

Suggested Citation

  • Obanya, Praise Otito & Seitshiro, Modisane & Olivier, Carel Petrus & Verster, Tanja, 2024. "A permutation entropy analysis of Bitcoin volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).
    • Handle: RePEc:eee:phsmap:v:638:y:2024:i:c:s0378437124001171
    DOI: 10.1016/j.physa.2024.129609
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437124001171
    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.2024.129609?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:638:y:2024:i:c:s0378437124001171. 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.