UCLA

The understanding of agricultural commodity financial markets has become of significant interest, given the increasing attention both the private and public sector have been giving to them. Financial investment in these goods has increased exponentially over the past 15 years. Food prices have reached significantly high levels. Therefore, my dissertation focuses on what is a relevant not only for the literature but also for both public and private institutions. I start by working within a competitive storage framework, as is usual for the literature. I then make assumptions and change the timing and information structure to match realistic aspects, therefore obtaining partially different theoretical results. I then intend to test these results by contrasting them with publicly available data regarding prices, production, consumption and information. Since the model is designed to match real-life aspects of the market, it can be applied to identify and measure fraction of price changes due to each different fundamental. For example, one recurring and important aspect of the data is that, in general, storage models predict excessively stable prices. That is, standard deviations are higher in the data than those compared to simulations in the models. Volatility is important since it can have potential welfare effects on both consumers and producers. Therefore, it is an issue that deserves attention. Moreover, related to this, in the past 15 years financial investment in these markets has increased severely, bringing concern to policy makers since these may have some effects on price levels and/or volatility. In the first chapter, I propose an innovative structure in the model to study this. More specifically, I subdivide time periods in four quarters, and each quarter with its own specific parameters. That is, only in the first quarter there is production, and demand presents seasonal effects for each of the four quarters. My intention is to improve the accuracy of the model by introducing once more a more realistic framework. Once these adjustments are made, I will be able to decompose and quantify through simulations the different causes of prices changes.

In the second part, I incorporate an innovation into the standard theoretical sotrage model. The cornerstone of seasonally produced goods literature is the competitive storage model. Since production occurs only during one part of the year but consumption takes place all year along, inevitably storage appears as the main solution. Therefore, storage models have been widely used within the literature, with an important deal of success. However, not all aspects of the data have yet been explained. For instance, when it comes to agricultural goods, the model predicts that future contracts that deliver goods before the next harvest should not be strongly correlated with futures that deliver goods after the harvest takes place. The argument for this is that the first contracts deliver goods "from last year", whereas the latter ones deliver "this year's harvest". Since sources of supply are different, when new news regarding supply appear (for example, a harvest forecast) they should only affect the latter contracts, but not the first ones. The data shows however otherwise. Indeed, correlation between "new harvest" and "old harvest" futures contracts is positive and close to 1. This is the issue I address in the second chapter. The key element in my paper is that I assume that harvest comes in "continuously" within a relevant time interval instead of "all in one moment". This allows me to split the harvest between early and non-early parts. I show that the market equilibrium results in the early part end up being arbitraged with "old" future contracts, whereas the non-early section arbitrages with "new" ones. Therefore, the same source of supply gets sold on both type of contracts, allowing for supply induced positive correlation. I simulate the model and show this result is robust to changing parameter specifications, obtaining correlations between 0.7 to 1, as in the data. I also provide proof of the assumptions made to get this result, showing that they are highly realistic. These results are not incompatible with the main findings that have already been made, therefore it contributes to the literature by additionally explaining an unsolved puzzle.

In my third chapter, I analyze inflationary processes in major LATAM economies. More specifically, with other two coauthors we study inflation in Peru, Colombia, Brazil, Mexico and Chile for the past 18 years. We find that domestic factors such as intertia and expectations still play the biggest role. Foreign inflation however gains importance in some countries. With regarding to Phillips curve slopes, we find that these have been flattening in the last decade for most countries, that is, the cycl has a smaller effect than it used to have in previous decades when determining inflation.