Does Non-linearity Matter in Retail Credit Risk Modeling?

Timotej Jagric (), Vita Jagric and Davorin Kracun
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Timotej Jagric: University of Maribor, Slovenia, http://www.uni-mb.si/
Vita Jagric: University of Maribor, Slovenia, http://www.uni-mb.si/
Davorin Kracun: University of Maribor, Slovenia, http://www.uni-mb.si/

Czech Journal of Economics and Finance (Finance a uver), 2011, vol. 61, issue 4, 384-402

Abstract: In this research we propose a new method for retail credit risk modeling. In order to capture possible non-linear relationships between credit risk and explanatory variables, we use a learning vector quantization (LVQ) neural network. The model was estimated on a dataset from Slovenian banking sector. The proposed model outperformed the benchmarking (LOGIT) models, which represent the standard approach in banks. The results also demonstrate that the LVQ model is better able to handle the properties of categorical variables.

Keywords: retail banking; credit risk; logistic regression; learning vector quantization (search for similar items in EconPapers)
JEL-codes: C25 C45 D81 G21 (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:fau:fauart:v:61:y:2011:i:4:p:384-402

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