IDEAS home Printed from https://ideas.repec.org/p/ces/ceswps/_12143.html
   My bibliography  Save this paper

Tail Connectedness Between Robotics and AI ETFs and Traditional Us Assets Under Different Market Conditions: A Quantile Var Approach

Author

Listed:
  • Fekria Belhouichet
  • Guglielmo Maria Caporale
  • Luis Alberiko Gil-Alana

Abstract

This paper examines tail connectedness between various exchange-traded funds (ETFs) focused on artificial intelligence (AI) and some traditional assets such as bonds, equities, Bitcoin, and oil, as well as the VIX uncertainty index, using US daily data over the period from 1 January 2023 to 23 June 2025. The investigation is carried out following the QVAR (Quantile VAR) approach introduced by Ando et al. (2022); this is an extension of the connectedness measure of Diebold and Yilmaz (2012, 2014) which captures the dynamic relationships between assets under different market conditions. The results show that AI and robotics ETFs, along with the S&P 500 Index, act as net transmitters of shocks, while other assets and the VIX serve as net receivers. Furthermore, connectedness intensifies under extreme market conditions. These findings suggest that technology ETFs play a central role in shock transmission and could be effectively employed for hedging purposes. Our findings provide valuable information to investors for diversification and hedging purposes, and to policy makers for maintaining financial stability, particularly during periods of market turbulence.

Suggested Citation

  • Fekria Belhouichet & Guglielmo Maria Caporale & Luis Alberiko Gil-Alana, 2025. "Tail Connectedness Between Robotics and AI ETFs and Traditional Us Assets Under Different Market Conditions: A Quantile Var Approach," CESifo Working Paper Series 12143, CESifo.
    • Handle: RePEc:ces:ceswps:_12143
    as

    Download full text from publisher

    File URL: https://www.ifo.de/DocDL/cesifo1_wp12143.pdf
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

    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:ces:ceswps:_12143. 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: Klaus Wohlrabe (email available below). General contact details of provider: https://edirc.repec.org/data/cesifde.html .

    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.