To explain price volatility in the U.S. agricultural, energy, and metal futures markets, we estimate a model of common and commodity-specific, high- and low-frequency factors by building on the spline-GARCH model of Engle and Rangel (2008). A better model fit results from allowing the unconditional variance to slowly change over time. Moreover, the persistence of volatility shocks is shown to be much weaker than what standard GARCH models imply. Combining the volatility results with monthly macroeconomic indicator data, we find that decomposing realized volatility into high- and low-frequency components better reveals the impact of slowly-evolving aggregate variables on price volatility. Moreover, over the period 1990–2005, most of the macroeconomic variables had similar effects within the same commodity category (e.g. grain), but their effects differed across commodity groups (e.g. grain versus livestock). Over the period 2006–2009, however, commodity-specific factors dominated common factors.