Abstract
Forecasts regarding the prices of energy commodities have long been significant to many market players. Our research examines the price of Brent crude oil on a daily basis in order to address the issue. The price series under investigation has significant financial ramifications, and the sample under investigation spans 10 years, from April 7, 2014 to March 28, 2024. In this case, cross-validation procedures and Bayesian optimization approaches are used to construct Gaussian process regression methods, and the resulting strategies are used to generate price estimates. For the out-of-sample evaluation period of March 24, 2022 to March 28, 2024, our empirical prediction technique yields relatively accurate projections of prices, as indicated by the relative root mean square error of 0.2814%. Price prediction models provide governments and investors with the knowledge they need to make informed decisions regarding the crude oil market.
Published Version
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