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Oil price volatility in the context of Covid-19

Author

Listed:
  • David Bourghelle

    (LUMEN - Lille University Management Lab - ULR 4999 - Université de Lille, IAE Lille - IAE Lille University School of Management - Lille - Université de Lille)

  • Fredj Jawadi

    (LUMEN - Lille University Management Lab - ULR 4999 - Université de Lille, IAE Lille - IAE Lille University School of Management - Lille - Université de Lille)

  • Philippe Rozin

    (LUMEN - Lille University Management Lab - ULR 4999 - Université de Lille, IAE Lille - IAE Lille University School of Management - Lille - Université de Lille)

Abstract

The recent coronavirus pandemic (COVID-19) has negatively impacted the whole economy, especially the oil industry, in at least two ways. First, it created a demand shock as COVID-19 reduced global demand for crude oil, increased uncertainty, and triggered a serious economic recession in most developed and emerging countries. Second, it led to a supply shock as the pandemic resulted in an oil trade war between the major oil-producing nations (Saudi Arabia and Russia). Both shocks led to very high levels of oil price volatility. Our paper explores the dynamics of this volatility and explains the effects of these two shocks (induced by an adjustment of oil demand and supply) on West Texas Intermediate (WTI) crude oil price volatility. Accordingly, we show that oil price volatility reacted substantially to the pandemic-induced oil shocks. In particular, we document the impact of uncertainty caused by these shocks and investor anxiety on oil price volatility. We show that greater uncertainty leads to more oil price volatility. Our findings remained unchanged even after controlling for modeling robustness.

Suggested Citation

  • David Bourghelle & Fredj Jawadi & Philippe Rozin, 2021. "Oil price volatility in the context of Covid-19," Post-Print hal-04448635, HAL.
  • Handle: RePEc:hal:journl:hal-04448635
    DOI: 10.1016/j.inteco.2021.05.001
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    Keywords

    Coronavirus; Oil price volatility; Uncertainty; VAR modelling; Impulse-response functions;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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