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 / Christine Wilson / Credit scoring is a tool used to make lending decisions. AgChoice Farm Credit has a dealer financing program called Farm Credit Express that makes lending decisions based on a scoring model. Farm Credit Express is a dealer financing option for farm equipment purchases. AgChoice has generated significant loan volume with this program but has also experienced challenges with loan delinquencies as field staff must service loans that they did not originate. This thesis evaluates loan delinquencies within AgChoice Farm Credit’s Farm Credit Express (“FCE”) program. The thesis develops a regression model that includes delinquencies as the dependent variable and Total AgChoice Borrowing, Original Loan Amount, Farming Segment, CBI Score, AgScore, and FCE Only as the independent variables. The model provides an examination of AgChoice’s Farm Credit Express delinquencies and evaluates the variables mentioned above and their ability to predict delinquencies. The results showed that Total AgChoice Borrowing, Original Loan Amount, CBI Score and FCE only were statistically significant independent variables. Based on results of the model, recommendations were made to potentially reduce future delinquencies in the Farm Credit Express loan portfolio.

Master of Agribusiness / Department of Agricultural Economics / Allen M. Featherstone / Farm Credit Mid-America is experiencing strong growth due to the success of the farming sector in our four state territory of Tennessee, Kentucky, Indiana, and Ohio. The company is well positioned to meet the financial demands of its customers and they have an aggressive growth plan to increase total assets from $18 billion to $25 billion in five years. They also plan to add 600 new employees in that time period. Determining the appropriate level of capital to sustain growth and meet the demands of its customers will be a primary objective of the organization over the next five years. Permanent capital is viewed as a percentage of total assets at Farm Credit Mid-America with the ideal amount between 14% and 16%. A detailed analysis of the current capital level, regulatory requirements, and the projected future financial position of the company was completed to: · Define and understand capital as it applies to Farm Credit Mid-America; · Research the current capital levels for Farm Credit Mid-America; · Compare capital levels of Farm Credit Mid-America to capital levels of other Farm Credit Associations and other banks; · Understand Basel III Accords and how it applies to Farm Credit Mid-America’s capital requirements; · Complete sensitivity analysis with multiple scenarios applied to the current Farm Credit Mid-America loan portfolio to determine the effect certain events may have on capital levels; · Determine if Farm Credit Mid-America is appropriately capitalized based on the other objectives. When looking at the results, it is determined that current capital levels are in line with other Farm Credit associations and competitors. Also, Farm Credit Mid-America has met the Basel III guidelines for minimum capital requirements. The sensitivity analysis included a wide range of scenarios from normal growth rates to extreme loan portfolio distress and the effects those scenarios would have on permanent capital. The permanent capital ratio exceeded the minimum standard of 12% on all sensitivity analysis scenarios. Therefore, based on the objectives of this thesis Farm Credit Mid-America appears to be adequately capitalized.

Master of Agribusiness / Department of Agricultural Economics / Allen M. Featherstone / Land is typically the highest value category of assets that farmers and ranchers have on their balance sheets. The value of land is affected by inflation. Understanding the effect of inflation on the land market helps farmers make better land pricing decisions and better asset management decisions. Using Treasury Bills and Farm Credit Bonds, future inflation expectations and agricultural risk premiums can be estimated. With the recent government stimulation of the economy and the resulting large amount of money infused into the economy, inflation is becoming an increasing concern with investors. Economic theory suggests that this infusion of money will affect future interest rates and ultimately the value of land given the inverse relationship between interest rates and the value of land. These lingering affects occur with the rise and fall of yield rates for Treasury Bills and Farm Credit bonds. Farm Credit bonds are sold at a premium over Treasury Bills. This premium indicates the market-assessed additional risk that farmers have to pay for their operating loans and other mortgages. Even though land values are affected by inflation, other things affect land values such as recreational use, development, and natural resource exploration. A combination of inflation and these other affects can greatly affect land prices.

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.

Master of Agribusiness / Department of Agricultural Economics / Allen M. Featherstone / Crop insurance, in recent years, has displaced U.S. federal farm program payments as the most important safety net for net farm income. The business climate that crop insurance purchasers and providers face in the future is one of increasing premiums for producers and decreasing commissions for crop insurance companies and agents. The primary objective of this thesis is to assess the desirability of crop insurance agency acquisitions to increase market share for Farm Credit Services of America, considering the significant uncertainties in the future subsidy levels and commission levels for these products. Financial analysis and modeling crop insurance agency acquisitions is completed under a wide range of future economic and political scenarios. The wide range of assumptions, however, does contribute to a wide range of potential purchase prices and rates of return on crop insurance agency acquisitions. The crop insurance industry faces uncertainty in the future and general industry profitability will likely decline. However, an expansion strategy in a period of reduced commissions can be profitable if acquisitions are priced appropriately and can be made in locations where existing support services can be leveraged to support the acquisition.

Dpartment of Agricultural Economics and Agribusiness / MSc.AEC