IDEAS home Printed from https://ideas.repec.org/a/eee/riibaf/v67y2024ipas0275531923002726.html
   My bibliography  Save this article

Uncovering dynamic connectedness of Artificial intelligence stocks with agri-commodity market in wake of COVID-19 and Russia-Ukraine Invasion

Author

Listed:
  • Yadav, Miklesh Prasad
  • Abedin, Mohammad Zoynul
  • Sinha, Neena
  • Arya, Vandana

Abstract

This paper investigates the connectedness of Artificial intelligence stocks with agri-commodity stocks during COVID-19 and Russia-Ukraine invasion. To measure the Artificial intelligence stocks, we consider Microsoft, Google, Amazon, Meta and NVIDA while US wheat, US corn, US soyabean, US oats and US Rice are proxied to represent the agri-commodity stocks. The daily closing price of these stocks is taken from December 31, 2019 to February 23, 2022 (COVID-19) and February 24, 2022 to August 10, 2022 (Russia-Ukraine Invasion). For an empirical estimation, Diebold & Yilmaz (2012) and Barunik & Krehlik (2018) models are employed to investigate the connectedness among these assets class. The result reveals that Microsoft is highest receiver as well as highest contributor of the shocks; US rice and US corn are least receiver and contributor of the shocks respectively during COVID-19 period.

Suggested Citation

  • Yadav, Miklesh Prasad & Abedin, Mohammad Zoynul & Sinha, Neena & Arya, Vandana, 2024. "Uncovering dynamic connectedness of Artificial intelligence stocks with agri-commodity market in wake of COVID-19 and Russia-Ukraine Invasion," Research in International Business and Finance, Elsevier, vol. 67(PA).
    • Handle: RePEc:eee:riibaf:v:67:y:2024:i:pa:s0275531923002726
    DOI: 10.1016/j.ribaf.2023.102146
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0275531923002726
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ribaf.2023.102146?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:riibaf:v:67:y:2024:i:pa:s0275531923002726. 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.elsevier.com/locate/ribaf .

    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.