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.


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    Title :

    Default risk drivers in shipping bank loans




    Publication date :

    2016




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English



    Classification :

    BKL:    85.00 / 55.82 Güterverkehr