A credit risk model suitable for agricultural lenders is identified. The model incorporates sector correlations and is applied to the loan portfolio of an agricultural credit association to create a distribution of loan losses. The distribution is used to derive the lender’s expected and...

This study investigates important factors that should be used by lenders in risk‐rating their farm customers. These factors predict actual farm performance and debt repayment ability. Linear and logistic regression models are used to identify the debt‐to‐asset ratio as a major predictor of...

A framework is identified for modeling credit risk in agriculture. A CreditRisk+ type model is deemed most suitable for agricultural lending. The CreditRisk+ model is modified to overcome its drawbacks by incorporating recent research that accounts for sector correlations and uses a more stable...

"We show that agricultural lenders can implement a credit risk model that uses their loan portfolio data and complies with the new Basel Capital Accord without requiring Merton-type model assumptions about underlying asset price volatility. A credit risk model is described and calibrated to the...