Forecasting Oil and Natural Gas Prices: A Model Combination Approach

This paper compares forecasting approaches for oil and natural gas prices within a unified pseudo-real-time framework. While oil price forecasting is well established, natural gas markets remain less explored and are characterized by more regionalized and less globally integrated pricing. By adapting established oil forecasting models to natural gas, we systematically assess how differences in market structure shape model transferability and predictive accuracy. Forecast combinations consistently outperform individual models for both commodities, underscoring the value of model averaging. However, the forecast gains are considerably larger for natural gas, reflecting greater potential for improvement in a more localized market. Optimal weighting schemes also differ: equal weights dominate for oil, while performance-based weights yield superior accuracy for gas. Overall, the results demonstrate that forecasting performance is both commodity- and market-structure-dependent, offering new insights into reliable energy price prediction across global and regional markets.