New Insights on Loss Given Default for Shipping Finance: Parametric and Non-parametric Estimations

Aida Salko () and Rita D’Ecclesia ()
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Aida Salko: Sapienza University of Rome, Department of Economics and Social Sciences
Rita D’Ecclesia: Sapienza University of Rome, Department of Statistics

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2022, pp 410-416 from Springer

Abstract: Abstract This study analyzes different parametric and non-parametric modeling methods for estimating the Loss Given Default (LGD) of bank loans for shipping companies. The shipping industry is subject to several risks which create the need to accurately measure the possible losses in order to estimate the LGD for the banking industry. We use a unique database of defaulted loans in European banks involved in shipping finance. The aim of this study is twofold: to compare the performance of alternative LGD modeling methodologies in shipping finance and to provide some insights into what drives LGD in the shipping industry. We find that non-parametric methods, especially random forest, lead to a remarkable increase in the prediction accuracy and outperform the traditional statistical models in terms of both in-sample and out-of-sample results. To investigate the risk drivers in the shipping business, we use a variable importance measure built on the idea of the permutation importance and find the energy index to be of paramount importance the most important risk factor. We find that crude oil prices play a big role and may affect the financial health of shipping firms and then the LGDs of shipping loans.

Keywords: Loss Given Default; Shipping finance; Global Credit Data (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-99638-3_66

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DOI: 10.1007/978-3-030-99638-3_66

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