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Forecasting oil, coal, and natural gas prices in the pre-and post-COVID scenarios: Contextual evidence from India using time series forecasting tools

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  • Alam, Md Shabbir
  • Murshed, Muntasir
  • Manigandan, Palanisamy
  • Pachiyappan, Duraisamy
  • Abduvaxitovna, Shamansurova Zilola

Abstract

Stock market price prediction is considered a critically important issue for designing future investments and consumption plans. Besides, given the fact that the COVID-19 pandemic has adversely impacted stock markets worldwide, especially over the past two years, investment decisions have become more challenging for risky. Hence, we propose a two-phase framework for forecasting prices of oil, coal, and natural gas in India, both for pre-and post-COVID-19 scenarios. Notably, the Autoregressive Integrated Moving Average, Simple Exponential Smoothing, and K- Nearest Neighbor approaches are utilized for analyses using data from January 2020 to May 2022. Besides, the various outcomes from the analytical exercises are matched with root mean squared error and mean absolute and percentage errors. Overall, the empirical outcomes show that the Autoregressive Integrated Moving Average method is appropriate for predicting India's oil, coal, and natural gas prices. Moreover, the predictive precision of oil, coal, and natural gas in the pre-COVID-19 period seems to be better than in that the post-COVID-19 stage. Additionally, prices of these energy resources are forecasted to increase through the year 2025. Finally, in line with the findings, significant policy recommendations are made.

Suggested Citation

  • Alam, Md Shabbir & Murshed, Muntasir & Manigandan, Palanisamy & Pachiyappan, Duraisamy & Abduvaxitovna, Shamansurova Zilola, 2023. "Forecasting oil, coal, and natural gas prices in the pre-and post-COVID scenarios: Contextual evidence from India using time series forecasting tools," Resources Policy, Elsevier, vol. 81(C).
    • Handle: RePEc:eee:jrpoli:v:81:y:2023:i:c:s0301420723000508
    DOI: 10.1016/j.resourpol.2023.103342
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