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Bank loans recovery rate in commercial banks: A case study of non-financial corporations

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  • Natalia Nehrebecka

    (Warsaw University - Faculty of Economic Sciences, D³uga 44/50, 00-241 Warsaw, Poland)

Abstract

The empirical literature on credit risk is mainly based on modelling the probability of default, omitting the modelling of the loss given default. This paper is aimed to predict recovery rates on the rarely applied nonparametric method of Bayesian Model Averaging and Quantile Regression, developed on the basis of individual prudential monthly panel data in the 2007–2018. The models were created on financial and behavioural data that present the history of the credit relationship of the enterprise with financial institutions. Two approaches are presented in the paper: Point in Time (PIT) and Through-the-Cycle (TTC). A comparison of the Quantile Regression which get a comprehensive view on the entire probability distribution of losses with alternatives reveals advantages when evaluating downturn and expected credit losses. A correct estimation of LGD parameter affects the appropriate amounts of held reserves, which is crucial for the proper functioning of the bank and not exposing itself to the risk of insolvency if such losses occur.

Suggested Citation

  • Natalia Nehrebecka, 2019. "Bank loans recovery rate in commercial banks: A case study of non-financial corporations," Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, University of Rijeka, Faculty of Economics and Business, vol. 37(1), pages 139-172.
    • Handle: RePEc:rfe:zbefri:v:37:y:2019:i:1:p:139-172
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    References listed on IDEAS

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    Cited by:

    1. Natalia Nehrebecka, 2019. "Credit risk measurement: Evidence of concentration risk in Polish banks’ credit exposures," Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, University of Rijeka, Faculty of Economics and Business, vol. 37(2), pages 681-712.

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    More about this item

    Keywords

    recovery rate; regulatory requirements; reserves; quantile regression; Bayesian model averaging;
    All these keywords.

    JEL classification:

    • G20 - Financial Economics - - Financial Institutions and Services - - - General
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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