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

Aviral Tiwari, Emmanuel Abakah, TN-Lan Le and Dante I. Leyva-de la Hiz

Technological Forecasting and Social Change, 2021, vol. 163, issue C

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.

Keywords: Time-varying dependence; Artificial intelligence; Carbon price (search for similar items in EconPapers)
JEL-codes: C22 Q54 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (36)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:163:y:2021:i:c:s0040162520312609

DOI: 10.1016/j.techfore.2020.120434

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