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Corporate Credit Risk Modeling: Quantitative Rating System And Probability Of Default Estimation

Author

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  • João Fernandes

    (Banco BPI)

Abstract

The literature on corporate credit risk modeling for privately-held firms is scarce. Although firms with unlisted equity or debt represent a significant fraction of the corporate sector worldwide, research in this area has been hampered by the unavailability of public data. This study is an empirical application of credit scoring and rating techniques applied to the corporate historical database of one of the major Portuguese banks. Several alternative scoring methodologies are presented, thoroughly validated and statistically compared. In addition, two distinct strategies for grouping the individual scores into rating classes are developed. Finally, the regulatory capital requirements under the New Basel Capital Accord are calculated for a simulated portfolio, and compared to the capital requirements under the current capital accord.

Suggested Citation

  • João Fernandes, 2005. "Corporate Credit Risk Modeling: Quantitative Rating System And Probability Of Default Estimation," Finance 0505013, University Library of Munich, Germany.
    • Handle: RePEc:wpa:wuwpfi:0505013
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    References listed on IDEAS

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    Cited by:

    1. Moro Russ A. & Härdle Wolfgang K. & Schäfer Dorothea, 2017. "Company rating with support vector machines," Statistics & Risk Modeling, De Gruyter, vol. 34(1-2), pages 55-67, June.
    2. Marinela BARBULESCU & Alina HAGIU, 2019. "Typology Of Credit Risk In Economy," Scientific Bulletin - Economic Sciences, University of Pitesti, vol. 18(3), pages 101-106.
    3. Jakubik, Petr & Moinescu, Bogdan, 2015. "Assessing optimal credit growth for an emerging banking system," Economic Systems, Elsevier, vol. 39(4), pages 577-591.
    4. Alexey Litvinenko, 2023. "A Comparative Analysis of Altman's Z-Score and T. Jury's Cash-Based Credit Risk Models with The Application to The Production Company and The Data for The Years 2016-2022," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 22(3), pages 518-553, September.
    5. Mariusz Górajski & Dobromił Serwa & Zuzanna Wośko, 2019. "Measuring expected time to default under stress conditions for corporate loans," Empirical Economics, Springer, vol. 57(1), pages 31-52, July.
    6. Van Laere, Elisabeth & Baesens, Bart, 2010. "The development of a simple and intuitive rating system under Solvency II," Insurance: Mathematics and Economics, Elsevier, vol. 46(3), pages 500-510, June.
    7. Marinela BARBULESCU & Alina HAGIU, 2020. "Financial Markets And Hedging Approaches," Scientific Bulletin - Economic Sciences, University of Pitesti, vol. 19(1), pages 30-37.
    8. Dagmar Čámská, 2016. "Development tendencies of prediction models with an emphasis on Central Europe," Ekonomika a Management, Prague University of Economics and Business, vol. 2016(4).

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    More about this item

    Keywords

    Credit Scoring; Credit Rating; Private Firms; Discriminatory Power; Basel Capital Accord; Capital Requirements;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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