This study introduces a set of multivariate models with the aim of forecasting global prices of 1) crude oil, 2) natural gas, 3) iron ore, and 4) steel. Various versions of vector autoregression and error-correction models are applied to monthly data for the short-term prediction of nominal commodity prices six months ahead, and to examine forecast accuracy. The fundamentals for metal and energy price predictions include inter alia, stock changes, changes in commodity production volumes, export volumes by the largest players, changes in the manufacturing sector of the largest consumers, the state of global real economic activity, freight rates, recession, and so on. Kilian's (2009) index of global real economic activity is found to be a useful proxy for global demand and a reliable input in forecasting both energy and metal prices. The findings suggest that models with smaller lag orders tend to outperform those with a higher number of lags. At the same time, selected individual models, while showing a standalone high performance, have varying forecast precision during different periods, and no individual model outperforms others consistently throughout the forecast horizon.