Corn prices experienced enormous volatility over the last decade. In this paper, we apply a structural vector autoregression model to quantify the relative importance of various contributing factors in driving corn price movements. The identification of structural parameters is achieved through a data-determined approach—the PC algorithm of Directed Acyclic Graphs. We find that, in general, unexpected shocks in aggregate global demand and speculative trading activities do not have a statistically significant effect on corn price movements. By contrast, shocks in the crude oil market have large immediate effects that persist in the long-run. The forecast error variance decomposition suggest that at the two-year horizon, variations in crude oil prices account for over 50% of the total corn forecast error variances. We also find that, consistent with theory, unexpected shocks in market-specific fundamentals also have large negative effects on price movements. In addition, unexpected residual shocks play an important role in corn price movement, especially in the short-run.