IDEAS home Printed from https://ideas.repec.org/a/taf/oaefxx/v10y2022i1p2159736.html
   My bibliography  Save this article

The effects of us covid-19 policy responses on cryptocurrencies, fintech and artificial intelligence stocks: A fractional integration analysis

Author

Listed:
  • Emmanuel Joel Aikins Abakah
  • Guglielmo Maria Caporale
  • Luis Alberiko Gil-Alana

Abstract

This paper assesses the impact of US policy responses to the Covid-19 pandemic on various technology-related assets such as cryptocurrencies, financial technology, and artificial intelligence stocks using fractional integration techniques. More precisely, it analyzes the behavior of the percentage returns in the case of nine major coins (Bitcoin—BITC, Stella—STEL, Litecoin—LITE, Ethereum—ETHE, XRP (Ripple), Dash, Monero—MONE, NEM, Tether—TETH) and two technology-related stock market indices (the KBW NASDAQ Technology Index—KFTX, and the NASDAQ Artificial Intelligence index—AI) over the period 1 January 2020–5 March 2021. The results suggest that fiscal measures such as debt relief and fiscal policy announcements had positive effects on the series examined during the pandemic, when an increased mortality rate tended instead to drive them down; by contrast, monetary measures and announcements appear to have had very little impact and the Covid-19 containment measures none at all.

Suggested Citation

  • Emmanuel Joel Aikins Abakah & Guglielmo Maria Caporale & Luis Alberiko Gil-Alana, 2022. "The effects of us covid-19 policy responses on cryptocurrencies, fintech and artificial intelligence stocks: A fractional integration analysis," Cogent Economics & Finance, Taylor & Francis Journals, vol. 10(1), pages 2159736-215, December.
    • Handle: RePEc:taf:oaefxx:v:10:y:2022:i:1:p:2159736
    DOI: 10.1080/23322039.2022.2159736
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/23322039.2022.2159736
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/23322039.2022.2159736?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.

    Citations

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


    Cited by:

    1. Abderahman Rejeb & Karim Rejeb & Khalil Alnabulsi & Suhaiza Zailani, 2023. "Tracing Knowledge Diffusion Trajectories in Scholarly Bitcoin Research: Co-Word and Main Path Analyses," JRFM, MDPI, vol. 16(8), pages 1-23, July.

    More about this item

    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:taf:oaefxx:v:10:y:2022:i:1:p:2159736. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/OAEF20 .

    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.