Dynamic multiscale relationships between COVID-19 sentiment and extreme crude oil returns: Evidence from wavelet coherence analysis

Xinghe Liu, Cheng Xu, Yun Hong*, Hao Xu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

This study investigates the dynamic, multi-scale relationship between sentiment related to the COVID-19 pandemic and extreme returns in crude oil. The recently developed COVID-19 indices are employed to gauge pandemic sentiment. Utilizing daily data spanning from January 2020 to December 2021, Granger’s linear and nonlinear causality tests reveal that indices nonlinearly influence extreme fluctuations in West Texas Intermediate and Brent crude oil prices. Interestingly, a reciprocal causation is also identified: extreme crude oil returns significantly affect the indices. Furthermore, the wavelet transform coherence analysis sheds light on the indices’ ability to predict extreme crude oil price volatility across specific time-frequency domains, displaying diverse distributions and lead-lag patterns among the sub-indices. Our study underscores the efficacy of indices in anticipating extreme fluctuations in crude oil values during the COVID-19 pandemic, carrying important implications for investors, scholars, and policymakers.

Original languageEnglish
Pages (from-to)2533-2548
Number of pages16
JournalEmerging Markets Finance and Trade
Volume60
Issue number11
DOIs
Publication statusPublished - 24 Feb 2024

Keywords

  • COVID-19 sentiment
  • Granger causality tests
  • extreme crude oil returns
  • wavelet coherence analysis

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