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Oil and Stock Markets in Ongoing Flux: Impact of Current Events on Oil Price and Stock Market Performance

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  • Zwak-Cantoriu Maria-Cristina

    (Bucharest University of Economic Studies, Bucharest, Romania)

Abstract

The price of oil and, indirectly, the performance of stock indices have been significantly affected by the events of recent years, with a negative impact on the global economy, including the evolution of the energy market in the context of the transition to renewable energy sources and changes in global energy policy. This paper proposes a comprehensive analysis of the evolution of stock indices, oil market volatility and investors' response to the "black swan" events that have recently caused changes in the global economy. Using advanced econometric and statistical models, the paper presents the complex relationship between the analyzed stock indices, the trends and changes that occurred within them and the relevant influencing factors, such as the price of Brent Oil and the Baltic Dry Index. The results obtained, based on univariate GARCH models (GJR-GARCH) and multivariate DCC-GARCH models are in accordance with specialized studies in the field and show that oil price fluctuations were higher at the beginning of the Covid-19 pandemic compared to the period after the military conflicts (stage in which no significant influence of the oil price on the stock market is observed). Also, using the Chow test, in the analyzed period there are 3 important breaking points that coincide with the dates when the COVID-19 pandemic began, the military conflict between Russia and Ukraine as well as the military conflict between Israel and Gaza, events with a strong impact on the economy. The obtained results also show another important aspect, namely that, during the selected period January 2019-November 2023, the price of oil and the stock market were much more affected by the COVID-19 pandemic than by military conflicts, which cannot be also stated in the case of the price of the Baltic-Dry Index.

Suggested Citation

  • Zwak-Cantoriu Maria-Cristina, 2024. "Oil and Stock Markets in Ongoing Flux: Impact of Current Events on Oil Price and Stock Market Performance," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 18(1), pages 3320-3331.
    • Handle: RePEc:vrs:poicbe:v:18:y:2024:i:1:p:3320-3331:n:1039
    DOI: 10.2478/picbe-2024-0271
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    References listed on IDEAS

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    1. Lutz Kilian & Cheolbeom Park, 2009. "The Impact Of Oil Price Shocks On The U.S. Stock Market," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 50(4), pages 1267-1287, November.
    2. Joo, Young C. & Park, Sung Y., 2021. "The impact of oil price volatility on stock markets: Evidences from oil-importing countries," Energy Economics, Elsevier, vol. 101(C).
    3. Managi, Shunsuke & Yousfi, Mohamed & Ben Zaied, Younes & Ben Mabrouk, Nejah & Ben Lahouel, Béchir, 2022. "Oil price, US stock market and the US business conditions in the era of COVID-19 pandemic outbreak," Economic Analysis and Policy, Elsevier, vol. 73(C), pages 129-139.
    4. Stavros Degiannakis, George Filis, and Vipin Arora, 2018. "Oil Prices and Stock Markets: A Review of the Theory and Empirical Evidence," The Energy Journal, International Association for Energy Economics, vol. 0(Number 5).
    5. Rosella Giacometti & Gabriele Torri & Kamonchai Rujirarangsan & Michela Cameletti, 2023. "Spatial Multivariate GARCH Models and Financial Spillovers," JRFM, MDPI, vol. 16(9), pages 1-23, September.
    6. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    7. Engle, Robert F & Sheppard, Kevin K, 2001. "Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH," University of California at San Diego, Economics Working Paper Series qt5s2218dp, Department of Economics, UC San Diego.
    8. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
    9. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    10. Engle, Robert F & Sheppard, Kevin K, 2001. "Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH," University of California at San Diego, Economics Working Paper Series qt5s2218dp, Department of Economics, UC San Diego.
    11. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
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