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The Volatility Spillover of Global Oil Price Uncertainty

Author

Listed:
  • Kamil Pícha

    (Department of Trade, Faculty of Economics, Tourism, and Languages, University of South Bohemia, Czech Republic)

  • Lucie Tichá

    (Department of Trade, Faculty of Economics, Tourism, and Languages, University of South Bohemia, Czech Republic)

  • Sanat Chuponov

    (Department of Accounting, Non-government Educational Institution Mamun University, Khiva, Uzbekistan)

  • Jasur Ataev

    (Department of Economics, Urgench State University, Urgench, Uzbekistan)

  • Dilshod Hudayberganov

    (Department of Economics, Urgench State University, Urgench, Uzbekistan)

  • Bekhzod Kuziboev

    (Department of Economics, Urgench State University, Urgench, Uzbekistan; & University of Tashkent for Applied Sciences, Tashkent, Uzbekistan)

Abstract
This manuscript, for the first time, analyses the volatility spillover of oil price uncertainty in the world using data from oil price uncertainty recently developed by Abdul and Qureshi (2023), spanning the time 1996-2019 on a monthly frequency. ARCH/GARCH (Autoregressive Conditional Heteroskedasticity and Generalized Autoregressive Conditional Heteroskedasticity) models are employed as an econometric tool. The findings suggest that ARCH model is more consistent than GARCH model in assessing the volatility of oil price uncertainty in the world. The results show that the volatility of oil price uncertainty is high in the world. The transition to renewable energy sources is proposed as a way to resist unexpected oil shocks since the production of renewables does not depend on the fluctuations of oil prices. Consequently, uncertainties in the oil price do not hinder economic activities.

Suggested Citation

  • Kamil Pícha & Lucie Tichá & Sanat Chuponov & Jasur Ataev & Dilshod Hudayberganov & Bekhzod Kuziboev, 2024. "The Volatility Spillover of Global Oil Price Uncertainty," International Journal of Energy Economics and Policy, Econjournals, vol. 14(3), pages 619-624, May.
    • Handle: RePEc:eco:journ2:2024-03-63
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    References listed on IDEAS

    as
    1. Christiane Baumeister & James D. Hamilton, 2019. "Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Demand Shocks," American Economic Review, American Economic Association, vol. 109(5), pages 1873-1910, May.
    2. Basher, Syed A. & Sadorsky, Perry, 2006. "Oil price risk and emerging stock markets," Global Finance Journal, Elsevier, vol. 17(2), pages 224-251, December.
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    4. Abiad, Abdul & Qureshi, Irfan A., 2023. "The macroeconomic effects of oil price uncertainty," Energy Economics, Elsevier, vol. 125(C).
    5. Breusch, T S & Pagan, A R, 1979. "A Simple Test for Heteroscedasticity and Random Coefficient Variation," Econometrica, Econometric Society, vol. 47(5), pages 1287-1294, September.
    6. Albulescu, Claudiu Tiberiu & Ajmi, Ahdi Noomen, 2021. "Oil price and US dollar exchange rate: Change detection of bi-directional causal impact," Energy Economics, Elsevier, vol. 100(C).
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Oil Price; Uncertainty; Volatility; Autoregressive Conditional Heteroskedasticity; Generalized Autoregressive Conditional Heteroskedasticity;
    All these keywords.

    JEL classification:

    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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