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Sustainable development during the post-COVID-19 period: Role of crude oil

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  • Peng, Lijuan
  • Liang, Chao

Abstract

The COVID-19 outbreak has led to severe global inflation, potentially leading to significant alterations in the link between inflation and the crude oil market. On this basis, the purpose of this paper is to investigate the impact of inflation on the crude oil market before and during COVID-19. Our findings imply that inflation has a significant positive effect on crude oil, especially during COVID-19. Most significantly, we further discover that crude oil markets are more impacted by extreme inflation shocks than normal shocks. Furthermore, the inflation-oil relation is mainly driven by extremely negative shocks. Finally, we investigate the forecasting efficiency of inflation during the epidemic, and the results prove that inflation provides useful information for forecasting crude oil volatility. Our results can help relevant market participants and policy-makers better advance their work in the postepidemic era and achieve global sustainable development.

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  • Peng, Lijuan & Liang, Chao, 2023. "Sustainable development during the post-COVID-19 period: Role of crude oil," Resources Policy, Elsevier, vol. 85(PA).
    • Handle: RePEc:eee:jrpoli:v:85:y:2023:i:pa:s0301420723005548
    DOI: 10.1016/j.resourpol.2023.103843
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    as
    1. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    2. Cunado, J. & Perez de Gracia, F., 2005. "Oil prices, economic activity and inflation: evidence for some Asian countries," The Quarterly Review of Economics and Finance, Elsevier, vol. 45(1), pages 65-83, February.
    3. Ding, Hui & Huang, Yisu & Wang, Jiqian, 2023. "Have the predictability of oil changed during the COVID-19 pandemic: Evidence from international stock markets," International Review of Financial Analysis, Elsevier, vol. 87(C).
    4. Degiannakis, Stavros & Filis, George, 2017. "Forecasting oil price realized volatility using information channels from other asset classes," Journal of International Money and Finance, Elsevier, vol. 76(C), pages 28-49.
    5. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    6. Jia, Zhijie & Wen, Shiyan & Lin, Boqiang, 2021. "The effects and reacts of COVID-19 pandemic and international oil price on energy, economy, and environment in China," Applied Energy, Elsevier, vol. 302(C).
    7. Rizvi, Syed Kumail Abbas & Naqvi, Bushra & Boubaker, Sabri & Mirza, Nawazish, 2022. "The power play of natural gas and crude oil in the move towards the financialization of the energy market," Energy Economics, Elsevier, vol. 112(C).
    8. Christiane Baumeister & Lutz Kilian, 2016. "Understanding the Decline in the Price of Oil since June 2014," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 3(1), pages 131-158.
    9. Hooker, Mark A, 2002. "Are Oil Shocks Inflationary? Asymmetric and Nonlinear Specifications versus Changes in Regime," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 34(2), pages 540-561, May.
    10. Robert F. Engle & Eric Ghysels & Bumjean Sohn, 2013. "Stock Market Volatility and Macroeconomic Fundamentals," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 776-797, July.
    11. Olivier J. Blanchard & Jordi Galí, 2007. "The Macroeconomic Effects of Oil Price Shocks: Why Are the 2000s so Different from the 1970s?," NBER Chapters, in: International Dimensions of Monetary Policy, pages 373-421, National Bureau of Economic Research, Inc.
    12. Darby, Michael R, 1982. "The Price of Oil and World Inflation and Recession," American Economic Review, American Economic Association, vol. 72(4), pages 738-751, September.
    13. Olivier J. Blanchard & Jordi Gali, 2007. "The Macroeconomic Effects of Oil Shocks: Why are the 2000s So Different from the 1970s?," NBER Working Papers 13368, National Bureau of Economic Research, Inc.
    14. Khaskheli, Asadullah & Zhang, Hongyu & Raza, Syed Ali & Khan, Komal Akram, 2022. "Assessing the influence of news indicator on volatility of precious metals prices through GARCH-MIDAS model: A comparative study of pre and during COVID-19 period," Resources Policy, Elsevier, vol. 79(C).
    15. 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.
    16. Francis X. Diebold & Jose A. Lopez, 1995. "Forecast evaluation and combination," Research Paper 9525, Federal Reserve Bank of New York.
    17. Huawei, Tian, 2022. "Does gross domestic product, inflation, total investment, and exchanges rate matter in natural resources commodity prices volatility," Resources Policy, Elsevier, vol. 79(C).
    18. Naqvi, Bushra & Rizvi, Syed Kumail Abbas & Mirza, Nawazish & Umar, Muhammad, 2023. "Financial market development: A potentiating policy choice for the green transition in G7 economies," International Review of Financial Analysis, Elsevier, vol. 87(C).
    19. David E. Rapach & Jack K. Strauss & Guofu Zhou, 2010. "Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy," Review of Financial Studies, Society for Financial Studies, vol. 23(2), pages 821-862, February.
    20. Salisu, Afees A. & Ebuh, Godday U. & Usman, Nuruddeen, 2020. "Revisiting oil-stock nexus during COVID-19 pandemic: Some preliminary results," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 280-294.
    21. Cologni, Alessandro & Manera, Matteo, 2008. "Oil prices, inflation and interest rates in a structural cointegrated VAR model for the G-7 countries," Energy Economics, Elsevier, vol. 30(3), pages 856-888, May.
    22. Liang, Chao & Umar, Muhammad & Ma, Feng & Huynh, Toan L.D., 2022. "Climate policy uncertainty and world renewable energy index volatility forecasting," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    23. Wang, Qiang & Li, Shuyu & Zhang, Min & Li, Rongrong, 2022. "Impact of COVID-19 pandemic on oil consumption in the United States: A new estimation approach," Energy, Elsevier, vol. 239(PC).
    24. Salisu, Afees A. & Isah, Kazeem O. & Oyewole, Oluwatomisin J. & Akanni, Lateef O., 2017. "Modelling oil price-inflation nexus: The role of asymmetries," Energy, Elsevier, vol. 125(C), pages 97-106.
    25. Imen Omri, 2023. "Directional predictability and volatility spillover effect from stock market indexes to Bitcoin: evidence from developed and emerging markets," Journal of Risk Finance, Emerald Group Publishing Limited, vol. 24(2), pages 226-243, January.
    26. Salisu, Afees A. & Vo, Xuan Vinh & Lawal, Adedoyin, 2021. "Hedging oil price risk with gold during COVID-19 pandemic," Resources Policy, Elsevier, vol. 70(C).
    27. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    28. Chao Liang & Yan Li & Feng Ma & Yaojie Zhang, 2022. "Forecasting international equity market volatility: A new approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1433-1457, November.
    29. Kais Tissaoui & Taha Zaghdoudi & Abdelaziz Hakimi & Ousama Ben-Salha & Lamia Ben Amor, 2022. "Does Uncertainty Forecast Crude Oil Volatility before and during the COVID-19 Outbreak? Fresh Evidence Using Machine Learning Models," Energies, MDPI, vol. 15(15), pages 1-20, August.
    30. Zhang, Wenting & Hamori, Shigeyuki, 2021. "Crude oil market and stock markets during the COVID-19 pandemic: Evidence from the US, Japan, and Germany," International Review of Financial Analysis, Elsevier, vol. 74(C).
    31. Liang, Chao & Xia, Zhenglan & Lai, Xiaodong & Wang, Lu, 2022. "Natural gas volatility prediction: Fresh evidence from extreme weather and extended GARCH-MIDAS-ES model," Energy Economics, Elsevier, vol. 116(C).
    32. Bentes, Sónia R., 2022. "On the stylized facts of precious metals’ volatility: A comparative analysis of pre- and during COVID-19 crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    33. Chen, Zhonglu & Liang, Chao & Umar, Muhammad, 2021. "Is investor sentiment stronger than VIX and uncertainty indices in predicting energy volatility?," Resources Policy, Elsevier, vol. 74(C).
    34. Le, Thai-Ha & Le, Anh Tu & Le, Ha-Chi, 2021. "The historic oil price fluctuation during the Covid-19 pandemic: What are the causes?," Research in International Business and Finance, Elsevier, vol. 58(C).
    35. Lu Wang & Feng Ma & Guoshan Liu, 2020. "Forecasting stock volatility in the presence of extreme shocks: Short‐term and long‐term effects," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 797-810, August.
    36. Stavros Degiannakis & Alexandra Livada & Epaminondas Panas, 2008. "Rolling-sampled parameters of ARCH and Levy-stable models," Applied Economics, Taylor & Francis Journals, vol. 40(23), pages 3051-3067.
    37. Sharif, Arshian & Aloui, Chaker & Yarovaya, Larisa, 2020. "COVID-19 pandemic, oil prices, stock market, geopolitical risk and policy uncertainty nexus in the US economy: Fresh evidence from the wavelet-based approach," International Review of Financial Analysis, Elsevier, vol. 70(C).
    38. Rizvi, Syed Kumail Abbas & Naqvi, Bushra & Mirza, Nawazish & Umar, Muhammad, 2022. "Safe haven properties of green, Islamic, and crypto assets and investor's proclivity towards treasury and gold," Energy Economics, Elsevier, vol. 115(C).
    39. Li, Xiafei & Liang, Chao & Chen, Zhonglu & Umar, Muhammad, 2022. "Forecasting crude oil volatility with uncertainty indicators: New evidence," Energy Economics, Elsevier, vol. 108(C).
    40. Niu, Zibo & Ma, Feng & Zhang, Hongwei, 2022. "The role of uncertainty measures in volatility forecasting of the crude oil futures market before and during the COVID-19 pandemic," Energy Economics, Elsevier, vol. 112(C).
    41. Wang, Lu & Ma, Feng & Liu, Jing & Yang, Lin, 2020. "Forecasting stock price volatility: New evidence from the GARCH-MIDAS model," International Journal of Forecasting, Elsevier, vol. 36(2), pages 684-694.
    42. Leitch, Gordon & Tanner, J Ernest, 1991. "Economic Forecast Evaluation: Profits versus the Conventional Error Measures," American Economic Review, American Economic Association, vol. 81(3), pages 580-590, June.
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    More about this item

    Keywords

    Inflation; COVID-19; Crude oil markets; GARCH-MIDAS;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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