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Crude oil prices in times of crisis: The role of Covid-19 and historical events

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  • Bouazizi, Tarek
  • Guesmi, Khaled
  • Galariotis, Emilios
  • Vigne, Samuel A.

Abstract

Oil markets have faced several crises since 1986, with the most recent global economic downturn linked to the Covid-19 pandemic leading to significant demand and supply shocks in the critical oil market. Given that the impact of crises on oil prices has not been uniform we aim to accurately assess oil price volatility during crises by implementing an ARMA(r,s)-Spline-GJR(p,q) model that accounts for volatility persistence. Principal components analysis (PCA) is employed to study the correlation between various crises and their impact on oil prices, while because of the limited number of observations available, PLS regressions are used to study the Covid-19 Crisis. The findings reveal higher levels of spline conditional volatility during the Covid-19 Crisis, providing novel evidence of the correlation between this pandemic and other major crises. Through the NIPALS algorithm, we identify 13 primary components that show a correlation between historical crises and the Covid-19 Crisis. In contrast, PLS-2 regressions indicate a strong correlation between historical crises and the Covid-19 Crisis in only six principal components. These results can help investors, hedgers, speculators, and governments to estimate, predict and make informed decisions as soon as signs of a crisis appear.

Suggested Citation

  • Bouazizi, Tarek & Guesmi, Khaled & Galariotis, Emilios & Vigne, Samuel A., 2024. "Crude oil prices in times of crisis: The role of Covid-19 and historical events," International Review of Financial Analysis, Elsevier, vol. 91(C).
    • Handle: RePEc:eee:finana:v:91:y:2024:i:c:s1057521923004714
    DOI: 10.1016/j.irfa.2023.102955
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    More about this item

    Keywords

    NIPALS algorithm; PLS regression; ARMA-spline-GJR models; GARCH model; WTI crude oil prices; Covid-19 crisis;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • H12 - Public Economics - - Structure and Scope of Government - - - Crisis Management
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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