Forecasting Oil Prices with Non-Linear Dynamic Regression Modeling

Pedro Moreno (), Isabel Figuerola-Ferretti and Antonio Muñoz
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Pedro Moreno: ICAI School of Engineering, Comillas Pontifical University, 28015 Madrid, Spain
Isabel Figuerola-Ferretti: ICADE and Center for Low Carbon Hydrogen Studies, Comillas Pontifical University, 28015 Madrid, Spain
Antonio Muñoz: Institute for Research in Technology (IIT), ICAI School of Engineering, Comillas Pontifical University, 28015 Madrid, Spain

Energies, 2024, vol. 17, issue 9, 1-29

Abstract: The recent energy crisis has renewed interest in forecasting crude oil prices. This paper focuses on identifying the main drivers determining the evolution of crude oil prices and proposes a statistical learning forecasting algorithm based on regression analysis that can be used to generate future oil price scenarios. A combination of a generalized additive model with a linear transfer function with ARIMA noise is used to capture the existence of combinations of non-linear and linear relationships between selected input variables and the crude oil price. The results demonstrate that the physical market balance or fundamental is the most important metric in explaining the evolution of oil prices. The effect of the trading activity and volatility variables are significant under abnormal market conditions. We show that forecast accuracy under the proposed model supersedes benchmark specifications, including the futures prices and analysts’ forecasts. Four oil price scenarios are considered for expository purposes.

Keywords: oil prices forecasting; Brent futures; GAM model; transfer function models; scenarios analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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