IDEAS home Printed from https://ideas.repec.org/a/prg/jnlpep/v2013y2013i2id446p163-181.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://pep.vse.cz/doi/10.18267/j.pep.446.html
    Download Restriction: free of charge
    File URL: http://pep.vse.cz/doi/10.18267/j.pep.446.pdf
    Download Restriction: free of charge
    File URL: https://libkey.io/10.18267/j.pep.446?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    2. Bernd Engelmann & Robert Rauhmeier (ed.), 2006. "The Basel II Risk Parameters," Springer Books, Springer, number 978-3-540-33087-5, June.
    3. Daniel McFadden, 1976. "A Comment on Discriminant Analysis "Versus" Logit Analysis," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 511-523, National Bureau of Economic Research, Inc.
    4. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    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.), The use of big data analytics and artificial intelligence in central banking, volume 50, Bank for International Settlements.
    5. Sergio Edwin Torrico Salamanca, 2014. "Macro credit scoring as a proposal for quantifying credit risk," Investigación & Desarrollo, Universidad Privada Boliviana, vol. 2(1), pages 42-64.
    6. 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.
    7. 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.
    8. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mousavi, Mohammad M. & Ouenniche, Jamal & Xu, Bing, 2015. "Performance evaluation of bankruptcy prediction models: An orientation-free super-efficiency DEA-based framework," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 64-75.
    2. Nawaf Almaskati & Ron Bird & Yue Lu & Danny Leung, 2019. "The Role of Corporate Governance and Estimation Methods in Predicting Bankruptcy," Working Papers in Economics 19/16, University of Waikato.
    3. Ittonen, Kim & Tronnes, Per C. & Wong, Leon, 2017. "Substantial doubt and the entropy of auditors’ going concern modifications," Journal of Contemporary Accounting and Economics, Elsevier, vol. 13(2), pages 134-147.
    4. Almaskati, Nawaf & Bird, Ron & Yeung, Danny & Lu, Yue, 2021. "A horse race of models and estimation methods for predicting bankruptcy," Advances in accounting, Elsevier, vol. 52(C).
    5. Ruey-Ching Hwang, 2013. "Forecasting credit ratings with the varying-coefficient model," Quantitative Finance, Taylor & Francis Journals, vol. 13(12), pages 1947-1965, December.
    6. Chen, Peimin & Wu, Chunchi, 2014. "Default prediction with dynamic sectoral and macroeconomic frailties," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 211-226.
    7. Giordani, Paolo & Jacobson, Tor & Schedvin, Erik von & Villani, Mattias, 2014. "Taking the Twists into Account: Predicting Firm Bankruptcy Risk with Splines of Financial Ratios," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 49(4), pages 1071-1099, August.
    8. Li, Chunyu & Lou, Chenxin & Luo, Dan & Xing, Kai, 2021. "Chinese corporate distress prediction using LASSO: The role of earnings management," International Review of Financial Analysis, Elsevier, vol. 76(C).
    9. Pesaran, M. Hashem & Schuermann, Til & Treutler, Bjorn-Jakob & Weiner, Scott M., 2006. "Macroeconomic Dynamics and Credit Risk: A Global Perspective," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(5), pages 1211-1261, August.
    10. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
    11. Zhang, Wei, 2015. "R&D investment and distress risk," Journal of Empirical Finance, Elsevier, vol. 32(C), pages 94-114.
    12. Fougère, D. & Golfier, C. & Horny, G. & Kremp, E., 2013. "What has been the impact of the 2008 crisis on firms’ default? (in French)," Working papers 463, Banque de France.
    13. Adriana Csikosova & Maria Janoskova & Katarina Culkova, 2020. "Application of Discriminant Analysis for Avoiding the Risk of Quarry Operation Failure," JRFM, MDPI, vol. 13(10), pages 1-14, September.
    14. Maria H. Kim & Graham Partington, 2015. "Dynamic forecasts of financial distress of Australian firms," Australian Journal of Management, Australian School of Business, vol. 40(1), pages 135-160, February.
    15. Koke, Jens, 2002. "Determinants of acquisition and failure: evidence from corporate Germany," Structural Change and Economic Dynamics, Elsevier, vol. 13(4), pages 457-484, December.
    16. John Y. Campbell & Jens Hilscher & Jan Szilagyi, 2008. "In Search of Distress Risk," Journal of Finance, American Finance Association, vol. 63(6), pages 2899-2939, December.
    17. Michael Halling & Evelyn Hayden, 2008. "Bank failure prediction: a two-step survival time approach," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The IFC's contribution to the 56th ISI Session, Lisbon, August 2007, volume 28, pages 48-73, Bank for International Settlements.
    18. Elizabeth Demers & Philip Joos, 2007. "IPO Failure Risk," Journal of Accounting Research, Wiley Blackwell, vol. 45(2), pages 333-371, May.
    19. Dennis M. López & Marshall K. Pitman, 2013. "Auditor workload compression and busy season portfolio changes – U.S. evidence," Working Papers 0216acc, College of Business, University of Texas at San Antonio.
    20. Koresh Galil & Neta Gilat, 2019. "Predicting Default More Accurately: To Proxy or Not to Proxy for Default?," International Review of Finance, International Review of Finance Ltd., vol. 19(4), pages 731-758, December.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:prg:jnlpep:v:2013:y:2013:i:2:id:446:p:163-181. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Stanislav Vojir (email available below). General contact details of provider: https://edirc.repec.org/data/uevsecz.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.