Dissecting Corn Price Movements with Directed Acyclic Graphs

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 the 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.


Issue Date:
2013
Publication Type:
Conference Paper/ Presentation
PURL Identifier:
http://purl.umn.edu/151279
Total Pages:
37
JEL Codes:
D84; Q11; Q13; Q41




 Record created 2017-08-04, last modified 2017-08-27

Fulltext:
Download fulltext
PDF

Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)