The aim of this study is to evaluate the risk-return efficiency of credit policies for managing portfolio credit risk of banking institutions. The focus of the empirical analysis is on the impact of risk pricing and problem loan restructuring on bank risk and returns using a simulation model that represents an operating environment of lenders servicing the Australian farm sector. Insurance theory principles and agency relationships between a borrower and a lender are integrated into the portfolio theory framework. The portfolio theory framework is then couched in terms of the capital budgeting approach to generate a portfolio return distribution function for a particular credit policy regime. Borrowers are segmented by region, industry, loan maturity and credit risk class. Each credit risk class defines risk constraints on which a stochastic simulation model may be developed for credit scoring an average borrower in a portfolio segment. The stochastic simulation method is then used to generate loan security returns for a particular credit policy regime through time with loan return outcomes weighted by the number of borrowers in a segment to give measures of portfolio performance. Stochastic dominance efficiency criteria are used to choose between distributions of NPV of bank returns measured for a number of credit policy alternatives. The findings suggest that banks servicing the Australian farm sector will earn more profit without additional portfolio risk if the maximum limit to which pricing accounts for default risk in loan reviews is positively linked to volatility of gross incomes of farm business borrowers. Importantly, credit-underwriting standards must also be formulated so as to procure farm business borrowers of above average productivity with loans that are fully secured using fixed assets. The results of simulations also suggest that restructuring loans in event of borrower default provide for large benefits compared to a �no restructuring� option.

The aim of this study is to evaluate the risk-return efficiency of credit policies for managing portfolio credit risk of banking institutions. The focus of the empirical analysis is on the impact of risk pricing and problem loan restructuring on bank risk and returns using a simulation model that represents an operating environment of lenders servicing the Australian farm sector. Insurance theory principles and agency relationships between a borrower and a lender are integrated into the portfolio theory framework. The portfolio theory framework is then couched in terms of the capital budgeting approach to generate a portfolio return distribution function for a particular credit policy regime. Borrowers are segmented by region, industry, loan maturity and credit risk class. Each credit risk class defines risk constraints on which a stochastic simulation model may be developed for credit scoring an average borrower in a portfolio segment. The stochastic simulation method is then used to generate loan security returns for a particular credit policy regime through time with loan return outcomes weighted by the number of borrowers in a segment to give measures of portfolio performance. Stochastic dominance efficiency criteria are used to choose between distributions of NPV of bank returns measured for a number of credit policy alternatives. The findings suggest that banks servicing the Australian farm sector will earn more profit without additional portfolio risk if the maximum limit to which pricing accounts for default risk in loan reviews is positively linked to volatility of gross incomes of farm business borrowers. Importantly, credit-underwriting standards must also be formulated so as to procure farm business borrowers of above average productivity with loans that are fully secured using fixed assets. The results of simulations also suggest that restructuring loans in event of borrower default provide for large benefits compared to a �no restructuring� option.

<p dir="ltr">The Farm Service Agency's Guaranteed Loan Program supports eligible lender's ability to provide credit to farms who would otherwise not qualify for loans by guaranteeing up to 95% of principal and interest if the farmer defaults. The first chapter examines the degree to which bank characteristics influence FSA guaranteed loan rates paid by farmers. We leverage the unique characteristics of a panel of FSA guaranteed loans that include both borrower and lender information. Relative to pooled OLS, our preferred fixed-effects regression specification suggests that both time-varying and invariant lender effects are a significant determinant of FSA guaranteed loan rates. Further, when controlling for lender-effects, the significance of borrower characteristics largely diminish. These findings are consistent with prior studies of broader lending market interaction. This is the first study to examine FSA guaranteed loans which accounts for bank-level variation in lending terms. The findings may be of interest to policymakers, program administrators, lenders, and farmers.</p><p dir="ltr">Bankers’ expectations have been shown to provide reasonable forecasts of land value. In the second chapter, we test the informativeness of bankers’ expectations in predicting FSA guaranteed loan application volumes. Once again, we leverage proprietary administrative data from the FSA and, this time, pair it with survey data from the Federal Reserve Bank of Chicago to evaluate bankers’ forecasts. Results show that bankers’ forecasts are outperformed by naïve models, and including bankers’ expectations does not improve predictive models. Once again, these results will be of interest to FSA program administrators, lenders, and potential borrowers.</p><p dir="ltr">The study of risk is an important thread of farm management research as agriculture is an industry with many sources of risk. In the third chapter, we link broad measures of policy risk in the form of Equity Market Volatility trackers to farmer’s perceptions of risk and uncertainty. We consider disagreement in ex ante sentiment questions to measure farmer risk. Through a series of pairwise VARs, we show which sources of risk matriculate as concerns for farmers measured by uncertainty in the Purdue University-CME Group Ag Economy Barometer. Increases in tax policy, trade policy and infectious disease uncertainty are found to Granger-cause movement in farmer sentiment uncertainty.</p>