Forecasting Commodity Futures Prices Under Macroeconomic Uncertainty

Abstract

This study examines the predictability of commodity futures prices in the context of heightened macroeconomic uncertainty. Commodity markets are increasingly influenced by global economic fluctuations, policy shifts, and geopolitical risks, which complicate accurate price forecasting. The primary objective of this research is to evaluate the efficacy of advanced econometric models, including vector autoregression (VAR) and machine learning algorithms, in forecasting commodity futures prices under such uncertain conditions. Using high-frequency data from major commodity exchanges and macroeconomic indicators from 2000 to 2023, the analysis reveals that models incorporating uncertainty proxies—such as the Global Economic Policy Uncertainty Index—significantly enhance forecasting accuracy. Key findings indicate that macroeconomic uncertainty not only amplifies price volatility but also alters the predictive power of traditional financial variables. These results underscore the importance of integrating uncertainty measures into commodity pricing models, offering valuable insights for investors, policymakers, and risk managers in mitigating exposure to volatile markets.