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Markov-switching dependence between artificial intelligence and carbon price: The role of policy uncertainty in the era of the 4th industrial revolution and the effect of COVID-19 pandemic

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  • Tiwari, Aviral Kumar
  • Abakah, Emmanuel Joel Aikins
  • Le, TN-Lan
  • Leyva-de la Hiz, Dante I.

Abstract

This paper investigates the dependence structure and dynamics between artificial intelligence (AI) and carbon prices in the era of the 4th industrial revolution. Using the NASDAQ AI price index as a measure of AI and the European Energy Exchange EU emissions trading system (i.e. certificate prices for CO2 emissions) as a measure of carbon prices, we employ time-varying Markov switching copula models from December 2017 to July 2020 that provide evidence of a time-varying Markov tail dependence structure and dynamics between AI and carbon prices. The result shows a negative dependence structure for the return series between AI and carbon prices. However, the relationship is asymmetric, indicating that there is a stronger tail dependence in the lower tails instead of the upper tails. The finding implies that AI is a favourable hedge against carbon prices, therefore indicating the diversification benefits of AI. To understand the issue in detail, we examine the effect of economic policy uncertainty, equity market volatility, and the recent COVID-19 pandemic; we find their negative effect on the dynamic dependence structure between AI and carbon prices at lower and higher quantiles. This evidence offers additional support for the safe-haven ability of AI for carbon prices.

Suggested Citation

  • Tiwari, Aviral Kumar & Abakah, Emmanuel Joel Aikins & Le, TN-Lan & Leyva-de la Hiz, Dante I., 2021. "Markov-switching dependence between artificial intelligence and carbon price: The role of policy uncertainty in the era of the 4th industrial revolution and the effect of COVID-19 pandemic," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    • Handle: RePEc:eee:tefoso:v:163:y:2021:i:c:s0040162520312609
    DOI: 10.1016/j.techfore.2020.120434
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    20. Anwer, Zaheer & Farid, Saqib & Khan, Ashraf & Benlagha, Noureddine, 2023. "Cryptocurrencies versus environmentally sustainable assets: Does a perfect hedge exist?," International Review of Economics & Finance, Elsevier, vol. 85(C), pages 418-431.
    21. Po Yun & Chen Zhang & Yaqi Wu & Yu Yang, 2022. "Forecasting Carbon Dioxide Price Using a Time-Varying High-Order Moment Hybrid Model of NAGARCHSK and Gated Recurrent Unit Network," IJERPH, MDPI, vol. 19(2), pages 1-19, January.
    22. Yue-Jun Zhang & Han Zhang & Rangan Gupta, 2023. "A new hybrid method with data-characteristic-driven analysis for artificial intelligence and robotics index return forecasting," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-23, December.
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    Keywords

    Time-varying dependence; Artificial intelligence; Carbon price;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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