Three essays on agricultural and catastrophic risk management

In this dissertation, I examine three issues related to agricultural and catastrophic risk management. In my first essay, "Modeling Yield Distribution in High Risk Counties: Application to Texas Upland Cotton", I develop models of yield distributions for crops and regions whose yields exhibit irregular behavior. Specifically, I undertake a statistical case study of Texas upland cotton, which in recent years has exhibited poor actuarial performance under the U.S. crop insurance program, and propose an alternative mixture distribution model in which the conditional distribution of yield depends upon an observable drought index. The results indicate that the mixture distribution model provides a better fit to the data than conventional parametric distributions and, for more than two-thirds of the Texas counties examined, implies Group Risk Plan insurance premium rates that are greater than those currently published by the U.S. Department of Agricultural Risk Management Agency.; In my second essay, "Effects of Crop Insurance on Farmer Crop Abandonment", I attempt to uncover evidence of moral hazard in the U.S. crop insurance program by examining the effects of crop insurance on post-planting abandonment decisions. In particular, I construct a theoretical intra-seasonal dynamic model that can explicitly explain farmers' crop abandonment decisions. I then use the theoretical model to motivate a Logit regression model, which I estimate using a pooled cross-sectional, time series of corn and upland cotton county-level yields during the years 1989-2004. My empirical analysis expands the scope of existing empirical studies by including regions and crops that have historically experienced high loss ratios under the Federal crop insurance program. The results provide strong evidence that insurance participation encourages farmers to abandon their crops during the growing season for corn in Central Plains and Southern Plains regions and for upland cotton in all South regions.; In my third essay, "Time Series Models of El Nino Events," I examine alternative time-series model of El Nino dynamics that can be used to support the actuarial analysis of a proposed weather derivative contract for Peru. In particular, I use a class of multiple structural change tests proposed by Bai and Perron (1998, 2003) to formally test whether the sea-surface temperature anomalies that characterize El Nino events have exhibited structural change during the 20th century. The tests identify several structural breaks over the past century and indicate that El Ninos have become more frequent and severe since 1976. Based on these results, I then examine the ability of three alternative time-series models, including an autoregressive (AR) error model, a generalized autoregressive conditional heteroskedasticity (GARCH) model and an exponential GARCH model, to predict El Nino severity with a 6-month lead time, a task that would be essential to the design and rating of insurance contracts. A linear AR(2) error model provides more accurate forecasts than the other two models.