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

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  • 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
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    References listed on IDEAS

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    1. Caporale, Guglielmo Maria & Gil-Alana, Luis A. & Poza, Carlos, 2020. "Persistence, non-linearities and structural breaks in European stock market indices," The Quarterly Review of Economics and Finance, Elsevier, vol. 77(C), pages 50-61.
    2. Maria Zakia Papanikolaou Mostafa & Stavros Stavroyiannis, 2016. "BRIC dynamic conditional correlations, portfolio diversification and rebalancing after the global financial crisis of 2008-2009," Global Business and Economics Review, Inderscience Enterprises Ltd, vol. 18(1), pages 28-40.
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    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

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