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Forecasting Oil, Coal, and Natural Gas Prices in the Pre–and Post-COVID Scenarios

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dc.contributor.author Alam, Md Shabbir
dc.contributor.author Murshed, Muntasir
dc.contributor.author Manigandan, Palanisamy
dc.contributor.author Pachiyappa, Duraisamy
dc.contributor.author Abduvaxitovna, Shamansurova Zilola
dc.date.accessioned 2024-05-30T06:06:46Z
dc.date.available 2024-05-30T06:06:46Z
dc.date.issued 2023-02-10
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/12560
dc.description.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. en_US
dc.language.iso en_US en_US
dc.publisher Elsevier en_US
dc.subject Stock market en_US
dc.subject Covid-19 en_US
dc.subject Forecasting en_US
dc.title Forecasting Oil, Coal, and Natural Gas Prices in the Pre–and Post-COVID Scenarios en_US
dc.title.alternative Contextual Evidence From India Using Time Series Forecasting Tools en_US
dc.type Article en_US


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