Time-frequency co-movement between COVID-19, crude oil prices, and atmospheric CO2 emissions: Fresh global insights from partial and multiple coherence approach

This paper endeavors to analyze and provide fresh global insights from the asymmetric nexus between the recent outbreak of COVID-19, crude oil prices, and atmospheric CO2 emissions. The analysis employs a unique Morlet's wavelet method. More precisely, this paper implements comprehensive wavelet coherence analysis tools, including continuous wavelet coherence, partial wavelet coherence, and multiple wavelet coherence to the daily dataset spanning from December 31, 2019 to May 31, 2020. From the frequency perspective, this paper finds significant wavelet coherence and vigorous lead and lag connections. This analysis ascertains significant movement in variables over frequency and time domain. These results demonstrate strong but varying connotations between studied variables. The results also indicate that COVID-19 impacts crude oil prices and the most contributor to the reduction in CO2 emissions during the pandemic period. This study offers practical and policy implications and endorsements for individuals, environmental experts, and investors.

Automated summary

This study utilizes Morlet's wavelet method to analyze the relationship between COVID-19, crude oil prices, and atmospheric CO2 emissions, revealing significant connections in both frequency and time domains.