Kellner, Ralf and Nagl, Maximilian and Rösch, Daniel (2022) Opening the black box - Quantile neural networks for loss given default prediction. JOURNAL OF BANKING & FINANCE, 134: 106334. ISSN 0378-4266, 1872-6372
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We extend the linear quantile regression with a neural network structure to enable more flexibility in every quantile of the bank loan loss given default distribution. This allows us to model interactions and non-linear impacts of any kind without the need of specifying the exact form beforehand. The precision of the quantile forecasts increases up to 30% compared to the benchmark, especially for higher quantiles which are most important in credit risk. By using a novel feature importance measure, we calculate the strength, direction, interactions and other non-linear impacts for every conditional quantile and every variable. This enables us to explain why our extension exhibits superior performance over the benchmark. Moreover, we find that the macroeconomy is up to two times more important in USA than in Europe and has large joint impacts in both regions. The macroeconomy is most important in the US, whereas in Europe collateralization is essential. (c) 2021 Elsevier B.V. All rights reserved.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | RECOVERY RATES; NONPARAMETRIC-ESTIMATION; MODELS; MACHINE; APPROXIMATION; SELECTION; LOANS; Quantile regression; Black box; Neural networks; Explainable machine learning; Global credit data |
| Subjects: | 300 Social sciences > 330 Economics |
| Divisions: | Business, Economics and Information Systems > Institut für Betriebswirtschaftslehre > Lehrstuhl für Statistik und Risikomanagement (Prof. Dr. Rösch) |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 10 Aug 2022 09:29 |
| Last Modified: | 10 Aug 2022 09:29 |
| URI: | https://pred.uni-regensburg.de/id/eprint/46311 |
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