Commodity Price Forecasting and Risk Dynamics Across Major Commodity Sectors: Evidence from Linear and Tree-Based Machine Learning Models
This paper intends to predict the prices of commodities in metals, energy, agriculture, and other industrial products sectors. This study covers a large span of about ten and a half years with 3484 daily observations starting from 7 April 2016 to 7 April 2026. The commodities taken into account for this study are: gold, silver, copper, Brent oil, natural gas, coal, corn, wheat, soybean, aluminum, nickel, and lead. On the methodological front, this paper is a little different as it conducts a comparative analysis of out-of-sample forecasting capability of four models, namely, a naïve one-day-lag (random walk) benchmark, a linear autoregressive model, a bagged decision-tree ensemble model, and a gradient-boosted decision-tree ensemble model. Forecast accuracy is measured in terms of error-based and directional forecasting metrics, while Diebold-Mariano tests, sector-level comparisons, volatility-regime analysis, and robustness checks serve as evaluation tools for qualitative assessments. The results highlight that forecasting performance differs not only with the time horizon but also with the specific sectors. A linear autoregressive model ranks as the most reliable and regularly successful among the four models under comparison. A bagged decision-tree ensemble model and a gradient-boosted decision-tree ensemble model, on the other hand, as machine learning tree-based models, may only offer fleeting and unstable gains. The further the forecast extends in time (from 1 day to 5 and 10 days), the less accurate it gets. Energy commodities are the hardest to predict, while industrial commodities seem to be relatively more predictable.