IDEAS home Printed from https://ideas.repec.org/a/spr/empeco/v69y2025i4d10.1007_s00181-025-02784-3.html
   My bibliography  Save this article

Novel modeling for assessment of extreme values risk in cryptocurrencies portfolio

Author

Listed:
  • Shafique Ur Rehman

    (University of Chinese Academy of Sciences)

  • Touqeer Ahmad

    (CREST, ENSAI, University of Rennes)

  • Desheng Wu

    (University of Chinese Academy of Sciences)

Abstract

This study addresses risk management for a ten-dimensional cryptocurrency portfolio, focusing on value at risk, expected shortfall, and range value at risk. It introduces a hybrid model combining the ARMA-EGARCH approach with flexible bulk and tails extreme value distribution and copula functions to improve risk measurement. The results show that the ARMA-EGARCH-EVT copula model better captures dependencies and outperforms traditional risk assessment methods (such as historical simulations and the variance covariance paradigm). This study underscores the potential of using a novel extreme value theory probability model to enhance risk assessment and provide valuable insights for investors and financial policymakers.

Suggested Citation

  • Shafique Ur Rehman & Touqeer Ahmad & Desheng Wu, 2025. "Novel modeling for assessment of extreme values risk in cryptocurrencies portfolio," Empirical Economics, Springer, vol. 69(4), pages 2065-2092, October.
    • Handle: RePEc:spr:empeco:v:69:y:2025:i:4:d:10.1007_s00181-025-02784-3
    DOI: 10.1007/s00181-025-02784-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00181-025-02784-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00181-025-02784-3?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

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:spr:empeco:v:69:y:2025:i:4:d:10.1007_s00181-025-02784-3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.