Why did commodity price fluctuate so much over the last 20 years? Are speculators to be blamed? Do prices reflect full information? These are the main questions I address in this paper, in the context of the corn market. I formulate and calibrate two quantitative models of corn prices formation. The first model is designed to explain prices in the the long run (annual frequency), while the second model applies to prices in the short run (quarterly frequency). For the long run analysis, I find that deviations of theoretical prices from observed ones are very small after 1996, and before 1996 they can be explained by government intervention. For the short run analysis, my model is designed to mimics the typical seasonality seen in agriculture markets, incorporate supply and demand shocks as well as news shocks, and allows for speculative storage decisions. I find that demand and supply fundamentals can account for around 52% of past price changes from 1975 to 2016. I also estimate the impact of information shocks to explain an additional 18% of quarterly deviations. Finally, find that at least 30% of short run price changes seem to have other explanation than supply or demand fundamentals or information, showing that when analyzing quarterly data, prices do not always track closely fundamentals.
Although competitive storage theory has proven successful in explaining many patterns for commodity prices, some important features remain unexplained. Particularly, while standard models predict low correlation between future prices with delivery dates before and after the harvest, the data suggests otherwise. To correct this, I assume that harvests appear continuously rather than at a single moment. This addition to the standard model allows me to link pre-harvest and post-harvest markets to the same source of supply, hence obtaining the high correlation observed in the data. Empirical evidence also suggests that assumptions used are realistic. Results are robust to different parameter specifications.
Work In Progress
Arroyo Marioli F., Carrera F. and Richardson G.: “Liquidity, Networks, and Unintended Consequences: The Founding of the Fed and the Great Depression ".
Arroyo Marioli F., Nanda V. and Toscani F. (2018): “Revisiting Phillips Curves in Latin America”.
We build on recent work for EMs, the US and the Euro area by estimating country-specific hybrid Phillips curves for major Latin American countries, augmented with external factors such as the exchange rate and foreign inflation. We find an important role for persistence, a relatively flat Phillips curve and an important role for external factors. We benchmark our results against findings in the literature and highlight how the LA5 compare to other EMs. We then decompose deviations from target inflation into the contribution of each of the drivers and discuss differences and similarities between LA5 countries. In a second exercise, we disaggregate core inflation into a number of key sub-indexes (education, health care, other services, durable goods, other goods) and study the role component-by-component differences have in explaining differences in aggregate inflation dynamics between the LA5(+Uruguay).