Issues Regarding Price Risk in Agricultural Commodity Markets

This study analyzes issues regarding the risk that crop and livestock production face with respect to price variation. Producers generally face an inelastic demand for their products, while their supply may encounter different unexpected production shocks---generating an increase in price variability. This price risk is compounded when it involves varying prices of production inputs, as in the case of livestock feed. The first study addresses crop production price risk mitigation via crop insurance---specifically crop revenue insurance contracts, by investigating the efficiency benefits of incorporating a model which captures the natural inverse relationship between crop prices and yields. The study begins by considering a parametric distribution which takes into account asymmetry and fat tails to model crop prices, in lieu of empirical evidence of prices having positively-skewed distributions and fatter tails than the conventional characterization obtained from a Log-normal distribution. Subsequently, a copula method is proposed to account for the natural inverse relation between crop prices and yields. Crops considered are corn, soybean, and wheat which form part of livestock feed. Copula methods have been used for managing risk in the financial sector, and results from this study show operational efficiency gains over present crop revenue insurance contracts. The next chapter begins by extending a multivariate time-series dynamic correlations model containing different correlation regimes with regime switching being governed by a Markov chain, via constant transition probabilities. Extension of the Regime Switching Dynamic Correlations (RSDC) model (Pelletier, 2006), permits identification of underlying economic fundamentals affecting the dynamic interrelationships between markets, by incorporating state dependent transition probabilities in the regime switching process. The state dependent transition probabilities contain fundamental economic variables related to the process. The study then applies the developed model to empirically analyze the impact of increased corn demand from ethanol production---on the dynamic interrelationship between corn, soybean and cattle prices, including spillover effects from market frictions. Results identify specific periods in which the correlation levels among the markets are impacted by the surge in corn consumption conditions. Risk spillovers from one market to another are determined, and their impacts on market interrelationships are discussed. The final chapter makes use of a co-integrated vector autoregressive model to further gauge the dynamic relationships between grain and cattle markets, in lieu of the boost in corn demand from ethanol mandated production. In addition to corn and soybean prices, grain sorghum (milo) and wheat---which also serve as feed---are taken into account along with feeder and live-cattle markets. Results corroborate some of the previous findings regarding the dynamic interaction between grain and cattle markets, especially considering the impact on these market relationships during the period of increased corn demand from ethanol consumption.