Markets have always changed in response to either exogenous or endogenous shocks. Many large events have occurred in financial and energy markets the last ten years. This dissertation examines market behavior and volatility in agricultural credit and fossil fuel markets under exogenous and endogenous changes in the last ten years. The efficiency of elements within the United States Farm Credit System, a major agricultural lender in the United States, and the dynamic correlation between coal, oil and natural gas prices, the three major fossil fuels, are examined. The Farm Credit system is a key lender in the U.S. agricultural sector, and its performance can influence the performance of the agricultural sector. However, its efficiency in providing credit to the agricultural sector has not been recently examined. The first essay of the dissertation provides assessments on the performance of elements within the Farm Credit System by measuring their relative efficiency using a stochastic frontier model. The second essay addresses the changes in relationship in coal, oil, and natural gas markets with respect to changes and turbulence in the last decade, which has also not been fully addressed in literature. The updated assessment on the relative performance of entities within the Farm Credit System provides information that the Farm Credit Administration and U.S. policy makers can use in their management of and policy toward the Farm Credit System. The measurement of the changes in fossil fuel markets’ relationships provides implications for energy investment, energy portfolio anagement, energy risk management, and energy security. It can also be used as a foundation for structuring forecasting models and other models related to energy markets. The dynamic correlations between coal, oil, and natural gas prices are examined using a dynamic conditional correlation multivariate autoregressive conditional heteroskedasticity (MGARCH DCC) model. The estimated results show that the FCS’s five banks and associations with large assets have more efficiently produced credit to the U.S. agricultural sector than smaller sized associations. Management compensation is found to be positively associated with the system’s efficiency. More capital investment and monitoring along with possible consolidation are implied for smaller sized associations to enhance efficiency. On average, the results show that the efficiency of the associations is increasing over time while the average efficiency of the five large banks is more stable. Overall, the associations exhibit a higher variation of efficiency than the five banks. In terms of energy markets the estimates from the MGARCH DCC model indicate significant and changing dynamic correlations and related volatility between the coal, oil, and natural gas prices. The coal price was found to experience more volatility and become more closely related to oil and natural gas prices in recent periods. The natural gas price was found to become more stable and drift away from its historical relationship with oil.

Master of Agribusiness / Department of Agricultural Economics / Allen M. Featherstone / Institutions of the Farm Credit System (FCS) focus on risk-based lending in accordance with regulatory direction. The rating of risk also assists retail staff in loan approval, risk-based pricing, and allowance decisions. FCS institutions have developed models to analyze financial and related customer information in determining qualitative and quantitative risk measures. The objective of this thesis is to examine empirical account data from 2006-2012 to review the probability of default (PD) rating methodology within the overall risk rating system implemented by a Farm Credit System association. This analysis provides insight into the effectiveness of this methodology in predicting the migration of accounts across the association’s currently-established PD ratings where negative migration may be an apparent precursor to actual loan default. The analysis indicates that average PD ratings hold relatively consistent over the years, though the distribution of the majority of PD ratings shifted to higher quality by two rating categories over the time period. Various regressions run in the analysis indicate that the debt to asset ratio is most consistently statistically significant in estimating future PD ratings. The current ratio appears to be superior to working capital to gross profit as a liquidity measure in predicting PD rating migration. Funded debt to EBITDA is more effective in predicting PD rating movement as a measure of earnings to debt than gross profit to total liabilities, although the change of these ratios over time appear to be weaker indicators of the change in PD rating potentially due to the variable nature of annual earnings of production agriculture operations due to commodity price volatility. The debt coverage ratio is important as it relates to future PD migration, though the same variability in commodity price volatility suggests the need implement multi-year averaging for calculation of earnings-based ratios. These ratios were important in predicting the PD rating of observations one year into the future for production agriculture operations. To further test the predictive ability of the PD ratings, similar regression analyses were completed comparing current year rating and ratios to future PD ratings beyond one year, specifically for three and five years. Results from these regression models indicate that current year PD rating and ratios are less effective in predicting future PD ratings beyond one year. Furthermore, because of the variation in regression results between the analyses completed for one, three and five years into the future, it is important to regularly capture ratio and rating information, at least annually.