Andres Trujillo-Barrera

Price and volatility transmissions between natural gas, fertilizer, and corn markets

We investigate the price and volatility transmission between natural gas, fertilizer (ammonia), and corn markets, an issue that has been traditionally ignored in the literature despite its significant importance. Using a Vector Error Correction Model for the conditional mean equation and a Multivariate Generalized Autoregressive Heteroskedasity model for the conditional volatility equation, we find significant interplay between fertilizer and corn markets, while only a mild linkage in prices and volatility exist between those markets and natural gas during the period 1994-2014. There is not only a positive relationship between corn and ammonia prices in the short-run, but both prices react to deviation from the long-run parity. Furthermore, the lagged conditional volatility of ammonia prices positively affects conditional volatility in the corn market and vice versa. This result is robust to a specification using crude oil price as an alternative to natural gas price to account for the large transportation cost built into ammonia prices. Results for the period of 2006-2014 indicate virtually no linkage between natural gas prices and those of fertilizer and corn during that period, while linkages in price level and volatility between the latter remain strong. This article is the first in the literature to comprehensively examine the role of fertilizer on corn prices and volatility, and its relation to natural gas prices.

Impact of US Biofuel Policy in the Presence of Uncertain Climate Conditions

We analyze the impact of total and partial waivers of the US Renewable Fuel Standard (RFS) under uncertain changes in climate conditions that affects crop yield distributions. Results show that reducing RFS would make world agricultural consumers better off, and increase the US corn share in the world market while slightly decreasing agricultural commodity prices. However, the higher the RFS reduction the higher the uncertainty on the price changes. On the other hand, price changes would make ethanol and agricultural producers face losses as well as increase gasoline consumption and, therefore, bring larger environmental damages. Overall, RFS reduction generates negative changes in total economic surplus, specifically, small and medium RFS reductions generate changes in the socio-economic variables; however, large reductions do not bring further changes, although uncertainty in welfare results is higher under an increased reduction.

Volatility Spillover and Time-Varying Conditional Correlation Between DDGS, Corn, and Soybean Meal Markets

In this paper, we seek to identify the price and volatility transmission mechanisms between DDGS, corn, and soybean meal markets to better inform market participants who wish to manage price risks in these three markets. Using weekly data from January 2000 to May 2016, we find important interplays between the three markets in both price and volatility dynamics. We uncover a stable long-run equilibrium between the three prices, and find that in the long-run, DDGS prices are positively correlated with both corn and soybean meal prices. However, corn and soybean meals are weakly exogenous — neither markets respond to deviations from this long-run relationship. We also find strong time-varying dynamic conditional correlations between the three markets, with the correlation between DDGS and corn strengthening between 2006 and 2012, the period when ethanol production in the US underwent massive expansion. Additionally, we identify significant volatility spillovers from both the corn and soybean meal markets to the DDGS market, with the impact from corn shocks much larger compared to soybean meal shocks. Corn appears to be the primary channel through which exogenous shocks are transmitted to the DDGS market. Our results also suggest that neither DDGS prices nor volatility significantly impact the corn and soybean meal markets.

Business Management Models of Microfinance Institutions (MFIs) in Africa: A Study into Their Enabling Environments

In this study, the authors analyse the socioeconomic, political and geographic conditions that are conducive of cooperative microfinance initiatives in comparison with other organizational forms in Africa. They distinguish three types of institutions (MFIs) and business models: cooperatives/credit unions, non-profit or non-governmental (NGOs and commercial banks). To analyse the enabling environment for the three business models three types of factors are distinguished: macroeconomic policy, institutional, and geographical. Multinomial logistic regression is applied to investigate the impact of these external conditions. The authors use data on 1790 MFIs in selected African countries (MIX Market) and global socioeconomic data of these countries. Their findings reveal that irrespective geographic location, cooperatives feature in countries with civil law systems, low inflation rates and high levels of economic growth. Commercial MFIs (banks) feature particularly in the countries with common law legal systems. NGO type MFIs are associated with high inflation rates and low levels of economic growth.

Price Density Forecasts in the U.S. Hog Markets: Composite Procedures

We develop and evaluate quarterly out-of-sample individual and composite density forecasts for U.S. hog prices. Individual density forecasts are generated using time series models and the implied distributions of USDA and Iowa State University outlook forecasts. Composite density forecasts are constructed using linear and logarithmic combinations of the individual forecasts and several weighting schemes. Density forecasts are evaluated on predictive accuracy (sharpness), goodness of fit (calibration), and their economic value in a hedging simulation. Logarithmic combinations using equal and mean square error weights outperform all individual density forecasts and are modestly better than linear composites. Comparison of the outlook forecasts to the best composite demonstrates the usefulness of the composite procedure, and identifies the economic value that more accurate expected price probability distributions can provide to producers.