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Comparison of Credit Scoring Models on Probability of Default Estimation for Us Banks

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  • Petr Gurný
  • Martin Gurný

Abstract

This paper is devoted to the estimation of the probability of default (PD) as a crucial parameter in risk management, requests for loans, rating estimation, pricing of credit derivatives and many others key financial fields. Particularly, in this paper we will estimate the PD of US banks by means of the statistical models, generally known as credit scoring models. First, in theoretical part, we will briefly introduce the two main categories of credit scoring models, which will be afterwards used in application part - linear discriminant analysis and regression models (logit and probit), including testing the statistical significance of estimated parameters. In the main part of the paper we will work with the sample of almost three hundred US commercial banks which will be separated into two groups (non-default and default) on the basis of historical information. Subsequently, we will stepwise apply the mentioned above scoring models on this sample to derive several models for estimation of PD. Further we will apply these models to the control sample to determine the most appropriate model.

Suggested Citation

  • Petr Gurný & Martin Gurný, 2013. "Comparison of Credit Scoring Models on Probability of Default Estimation for Us Banks," Prague Economic Papers, Prague University of Economics and Business, vol. 2013(2), pages 163-181.
    • Handle: RePEc:prg:jnlpep:v:2013:y:2013:i:2:id:446:p:163-181
    DOI: 10.18267/j.pep.446
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    1. Anatoly Peresetsky, Alexander Karminsky, 2011. "Models for Moody’s Bank Ratings," Frontiers in Finance and Economics, SKEMA Business School, vol. 8(1), pages 88-110, April.
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    Cited by:

    1. D. Bidzhoyan S. & Д. Биджоян С., 2018. "Модель Оценки Вероятности Отзыва Лицензии У Российского Банка // Model For Assessing The Probability Of Revocation Of A License From The Russian Bank," Финансы: теория и практика/Finance: Theory and Practice // Finance: Theory and Practice, ФГОБУВО Финансовый университет при Правительстве Российской Федерации // Financial University under The Government of Russian Federation, vol. 22(2), pages 26-37.
    2. Caplescu Raluca Dana & Cojocea Manuela-Simona & Pele Daniel Traian & Strat Vasile Alecsandru, 2021. "Improvements in PD models. A case-study approach," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 15(1), pages 13-32, December.
    3. Anita Nandi & Partha Pratim Sengupta & Abhijit Dutta, 2019. "Diagnosing the Financial Distress in Oil Drilling and Exploration Sector of India through Discriminant Analysis," Vision, , vol. 23(4), pages 364-373, December.
    4. Anastasios Petropoulos & Vasilis Siakoulis & Evaggelos Stavroulakis & Aristotelis Klamargias, 2019. "A robust machine learning approach for credit risk analysis of large loan level datasets using deep learning and extreme gradient boosting," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Are post-crisis statistical initiatives completed?, volume 49, Bank for International Settlements.
    5. Caplescu Raluca Dana & Panaite Ana-Maria & Pele Daniel Traian & Strat Vasile Alecsandru, 2020. "Will they repay their debt? Identification of borrowers likely to be charged off," Management & Marketing, Sciendo, vol. 15(3), pages 393-409, September.
    6. A. R. Provenzano & D. Trifir`o & A. Datteo & L. Giada & N. Jean & A. Riciputi & G. Le Pera & M. Spadaccino & L. Massaron & C. Nordio, 2020. "Machine Learning approach for Credit Scoring," Papers 2008.01687, arXiv.org.
    7. Anastasios Petropoulos & Vasilis Siakoulis & Evaggelos Stavroulakis & Aristotelis Klamargias, 2019. "A robust machine learning approach for credit risk analysis of large loan-level datasets using deep learning and extreme gradient boosting," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The use of big data analytics and artificial intelligence in central banking, volume 50, Bank for International Settlements.
    8. Sergio Edwin Torrico Salamanca, 2014. "Macro credit scoring as a proposal for quantifying credit risk," Investigación & Desarrollo 0814, Universidad Privada Boliviana, revised Nov 2014.
    9. Juan Rafael Ruiz & Patricia Stupariu & Ángel Vilariño, 2024. "The weakest links in the crisis of the Spanish Savings Banks," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(1), pages 654-664, January.
    10. Haris Doukas & Panos Xidonas & Nikos Mastromichalakis, 2022. "How Successful are Energy Efficiency Investments? A Comparative Analysis for Classification & Performance Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 579-598, February.
    11. Irving Fisher Committee, 2019. "The use of big data analytics and artificial intelligence in central banking," IFC Bulletins, Bank for International Settlements, number 50, July.

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    More about this item

    Keywords

    logistic regression; probability of default (PD); credit scoring models; linear discriminant analysis; probit regression;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G01 - Financial Economics - - General - - - Financial Crises
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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