Default risk drivers in shipping bank loans

• A simple credit scoring model reveals the default risk drivers of shipping bank loans.
• A unique sample of data is used consisting of the credit portfolio of a ship-lending bank.
• Unbiased inferences are enabled through two-way clustered adjusted standard errors.
• New variables are shown to be important in explaining default probabilities.

This paper proposes a credit scoring model for the empirical assessment of default risk drivers of shipping bank loans. A unique dataset, consisting of the credit portfolio of a ship-lending bank is used to estimate a logit model with two-way clustered adjusted standard errors, ensuring robust inferences. Industry specific variables, captured through current and expected conditions in the extremely volatile global shipping freight markets, the risk appetite of borrowers–the shipowners – expressed through the chartering policy they follow – and a pricing variable, are shown for the first time to be the important factors explaining default probabilities of bank loans.