IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v663y2025ics0378437125000950.html
   My bibliography  Save this article

Multi-scale dynamic correlation and information spillover effects between climate risks and digital cryptocurrencies: Based on wavelet analysis and time-frequency domain QVAR

Author

Listed:
  • Shu, Mingyu
  • Liu, Baoliu
  • ouyang, Wenpei
  • Sun, Rengui
  • Lin, Yaoyang

Abstract

This study investigates the multi-scale dynamic correlations and information spillover effects between climate risks and digital cryptocurrencies using wavelet analysis and the Time-frequency Domain QVAR model. By analyzing non-stationary financial time-series data, we uncover latent patterns and quantify the dynamic interactions between climate risks and cryptocurrency markets across different time scales. The findings reveal significant spillover effects, highlighting how climate risks, particularly through energy-intensive mining and extreme weather disruptions, influence cryptocurrency volatility. The research contributes to the understanding of risk transmission mechanisms in emerging financial markets, offering insights into the broader implications of climate risks on global financial stability. The results underscore the importance of integrating climate risk assessments into cryptocurrency market analyses, providing a foundation for informed policy-making and risk management strategies.

Suggested Citation

  • Shu, Mingyu & Liu, Baoliu & ouyang, Wenpei & Sun, Rengui & Lin, Yaoyang, 2025. "Multi-scale dynamic correlation and information spillover effects between climate risks and digital cryptocurrencies: Based on wavelet analysis and time-frequency domain QVAR," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 663(C).
    • Handle: RePEc:eee:phsmap:v:663:y:2025:i:c:s0378437125000950
    DOI: 10.1016/j.physa.2025.130443
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437125000950
    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.2025.130443?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:663:y:2025:i:c:s0378437125000950. 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.