The review of the open challenges in the IRB loan portfolio credit risk modeling

Abstract

The Basel Committee on Banking Supervision finalized the Basel III accord in the December 2017 and launched the set of its standards – the Basel Framework – in December 2019. Both documents allow bank to use mathematical models for the credit risk estimation. There are quantitative and qualitative requirements for models to be allowed for use in the prudential regulation of banks. The approach is called an Internal-Ratings-Based one (IRB). This paper aims at discussing a set of issues related to IRB credit risk modeling and such model estimates use. Those issues include data pooling in the credit registries, applying copula-discriminant analysis, validating the borrower concentration per grade, assigning the hybrid credit rating, use of model estimates when voting at the credit committee, estimate of the ultimate credit risk-taking by banks.

Keywords

trajectory cluster-analysis copula discriminant analysis basel committee IRB credit risk regulation credit committee voting data pooling hybrid credit rating credit registry

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References

Aivazian

, & Fantazzini

(2014). Econometrics-2. Advanced course with applications in finance [in Russian]. Moscow: Magistr.

Aivazian

Andrievskaya

Connolly

, & Penikas

(2011). Systemically important financial institutions identification: Review of methodologies. Russian Journal of Money and Credit, 8, 13-18.

Aleskerov

Karabekyan

Ivanov

, & Yakuba

(2019). On the Hare voting rule manipulabilitiy [in Russian]. XIIIth All-Russian Meeting of the Control Problems’ Proceedings (pp. 2143-2148). Moscow: Institute of Control Sciences RAS.

Altman

(2018). A fifty-year retrospective on credit risk models, the Altman Z-score family of models and their applications to financial markets and managerial strategies. The Journal of Credit Risk, IRMC 10th Anniversary Special Issue, 1-34.

Altman

, & Sabato

(2003ca). Modeling credit risk for SMEs: Evidence from the US market. Retrieved from http://people.stern.nyu.edu/ealtman/ModelingCreditRiskforSMEs%20.pdf.

Andrikopoulos

, & Khorasgani

(2018). Predicting unlisted SMEs’ Default: Incorporating market information on accounting-based models for improved. The British Accounting Review, 10.1016/j.bar.2018.02.003.

ARB. (2013). Validation. Retrieved August 19, 2019, from The Association of Russian Banks: https://arb.ru/upload/iblock/55d/.docx.

Arsova

Haralampieva

, & Tsvetanova

(2011). Comparison of regression models for LGD estimation. Edinburgh: Credit Scoring and Credit Control XII. Retrieved August 19, 2019, from https://www.experian.com.tr/assets/ebulten/Comparison-of-regression-models-for-LGD-estimation-Edinburgh.pdf.

Bank of Russia. (2020). On the definition of the systemically important credit institutions and approaches to their regulation [in Russian]. Retrieved from Public Consultation Report: http://www.cbr.ru/content/document/file/98619/consultation_paper_200123.pdf.

10.

Banque de France. (2006). La centralisation des risques bancaires. Retrieved from Note d’Information No. 115: http://www.banque-france.fr/fileadmin/user_upload/banque_de_france/Mission/Services_rendus/note115.pdf.

11.

BCBS. (2000a). Credit ratings and complementary sources of credit quality information. Retrieved from Basel Committee on Banking Supervision Website: http://www.bis.org/publ/bcbs_wp3.htm.

12.

BCBS. (2001). Working paper on the internal ratings-based approach to specialised lending exposures. Retrieved 08, 2018, from Basel Committee on Banking Supervision Website: https://www.bis.org/publ/bcbs_wp9.pdf.

13.

BCBS. (2005a). Studies on the validation on internal rating systems. Retrieved December 25, 2019, from Basel Committee on Banking Supervision: https://www.bis.org/publ/bcbs_wp14.htm.

14.

BCBS. (2005b). Validation of low-default portfolios in the Basel II Framework. Retrieved from Basel Committee on Banking Supervision: http://www.bis.org/publ/bcbs_nl6.htm.

15.

BCBS. (2006a). International convergence of capital measurement and capital standards. A revised framework. Comprehensive Version. Retrieved from http://bis.org/publ/bcbs128.htm.

16.

BCBS. (2006b). The IRB Use Test: Background and Implementation. Retrieved from The Basel Committee on Banking Supervision: http://www.bis.org/publ/bcbs_nl9.htm.

17.

BCBS. (2011). Range of methodologies for risk and performance alignment of Remuneration. Retrieved from https://www.bis.org/publ/bcbs194.pdf.

18.

BCBS. (2012a). Basel III regulatory consistency assessment (Level 2) – Japan. Retrieved from Basel Committee on Banking Supervision Website: https://www.bis.org/bcbs/implementation/l2_jp.pdf.

19.

BCBS. (2013c). Regulatory consistency assessment programme (RCAP) – Analysis of risk-weighted assets for credit risk in the banking book. Retrieved November 12, 2018, from Basel Committee on Banking Supervision Website: http://www.bis.org/publ/bcbs256.htm

20.

BCBS. (2013d). Regulatory consistency assessment programme (RCAP): Assessment of basel III regulations – China. Retrieved from Basel Committee on Banking Supervision Website: https://www.bis.org/bcbs/implementation/l2_cn.pdf.

21.

BCBS. (2013e). Capital requirements for banks’ equity investments in funds - final standard. Retrieved from Basel Committee on Banking Supervision Website: http://www.bis.org/publ/bcbs266.htm.

22.

BCBS. (2014c). Regulatory consistency assessment programme (RCAP): Assessment of basel III regulations – European Union. Retrieved from Basel Committee on Banking Supervision Website: https://www.bis.org/bcbs/publ/d300.pdf.

23.

BCBS. (2014d). Regulatory consistency assessment programme (RCAP): Assessment of basel III regulations – United States of America. Retrieved from Basel Committee on Banking Supervision Website: https://www.bis.org/bcbs/publ/d301.pdf.

24.

BCBS. (2016c). Regulatory consistency assessment programme (RCAP): Assessment of basel III risk-based capital regulations – Russia. Retrieved from Basel Committee on Banking Supervision: http://www.bis.org/bcbs/publ/d357.pdf.

25.

De Pooter

Ravazzolo

, & van Dijk

(2010). Term structure forecasting using macro factors and forecast combination. Retrieved from Board of Governors of the Federal Reserve System International Finance Discussion Papers No. 993: https://www.federalreserve.gov/pubs/ifdp/2010/993/ifdp993.pdf.

26.

Dwyer

Korebleva

, & Dzhao

(2010). RiskCalc model v3.1. for Russian companies. Retrieved from Modeling Methodology, Moody’s KMV.

27.

EBA. (2019). Instructions for reporting the validation results of internal models. IRB Pillar I models for credit risk. Retrieved December 15, 2019, from European Banking Authority Website: https://www.bankingsupervision.europa.eu/banking/tasks/internal_models/shared/pdf/instructions_validation_reporting_credit_risk.en.pdf.

28.

Ermolova

M. D.

, & Penikas

H. I.

(2017). PD-LGD correlation study: Evidence from the Russian corporate bond market. Model Assisted Statistics and Applications, 12(4), 335-358.

29.

Ermolova

, & Penikas

(2019). Research on the origin of the minimum PD grades number requirement for Basel II models. Workshop “Applied Econometrics” within a conference: XX April International Scientific Conference on the Economic and Social Development Issues. Moscow: HSE.

30.

Fantazzini

(2008). Credit risk management [in Russian]. Applied Econometrics, 12(4), 84-137.

31.

Fantazzini

(2009). Credit risk management (Continuation) [in Russian]. Applied Econometrics, 13(1), 105-138.

32.

Fantazzini

(2009). The effects of misspecified marginals and copulas on computing the value at risk: A monte carlo study. Computational Statistics & Data Analysis, 53(6), 2168-2188.

33.

Fantazzini

, & Figini

(2009). Random survival forests models for SME credit risk measurement. Methodology and Computing in Applied Probability, 17(1), 29-45.

34.

Ferri

, & Pesic

(2017). Bank regulatory arbitrage via risk weighted assets dispersion. Journal of Financial Stability, 33, 331-345.

35.

Frontczak

Jaeger

, & Schumacher

(2017). From power curves to discriminative power: Measuring model performance of LGD models. Journal of Mathematical Finance, 657-670. Retrieved August 16, 2019, from https://file.scirp.org/pdf/JMF_2017072016094724.pdf.

36.

Fungacova

, & Weill

(2013). Does competition influence bank failures? Evidence from Russia. Economics of Transition, 21(2), 301-322.

37.

Genest

, & Neslehova

(2007). A primer on copulas for count data. Astin Bulletin, 37(2), 475-515.

38.

Grammenos

Nomikos

, & Papapostolou

(2008). Estimating the probability of default for shipping high yield bond issues. Transportation Research Part E: Logistics and Transportation Review, 44(6), 1123-1138.

39.

Gupta

Barzotto

, & Khorasgani

(2018). Does size matter in predicting SMEs failure? International Journal of Finance and Economics, 23(4), 571-605.

40.

Han

Zhao

, & Liu

(2013). CODA: High dimensional copula discriminant analysis. Journal of Machine Learning Research, 14, 629-671.

41.

Hlawatsch

, & Reichling

(2009). A Framework for LGD validation of retail portfolios. Retrieved August 16, 2019, from https://www.fww.ovgu.de/fww_media/femm/femm_2009/2009_25.pdf.

42.

Hlawatsch

, & Reichling

(2010). A framework for loss given default validation of retail portfolios. The Journal of Risk Model Validation, 23-48. Retrieved August 16, 2019, from https://pdfs.semanticscholar.org/36fe/a6373d63360baf28dd6e5bc75e21db7b2946.pdf.

43.

Hurlin

Leymariez

, & Patin

(2017). Loss functions for LGD model comparison. Retrieved August 19, 2019, from https://www.unilim.fr/lape/IMG/pdf/LGD_25_mai_2017.pdf.

44.

Kaltofen

Paul

, & Stein

(2006). Retail loans and basel II: Using portfolio segmentation to reduce capital requirements. Retrieved from http://aei.pitt.edu/9427/.

45.

Kaluder

, & Augustin

(2013). Specialized lending rating model using analytical hierarchy process. Retrieved 08, 2018, from https://www.researchgate.net/publication/277774841.

46.

Karminsky

(2015). Credit ratings and their modeling [in Russian]. Moscow: HSE Publishing House.

47.

Karminsky

, & Kostrov

(2013). Modeling Russian banks default probabilities: Extended opportunities [in Russian]. Zhournal Novoy Economitcheskoi Associacii, 17(1), 64-86.

48.

Karminsky

Lozinskaya

, & Ozhegov

(2016). Approaches to the loss given default estimation in the domain of the retail mortgage lending [in Russian]. HSE Economic Journal, 20(1), 9-51.

49.

Kavussanos

M. T.

(2014). The determinants of credit spreads changes in global shipping bonds. Transportation Research Part E: Logistics and Transportation Review, 70, 55-75.

50.

Kavussanos

, & Tsouknidis

(2016). Default risk drivers in shipping bank loans. Transportation Research Part E: Logistics and Transportation Review, 94, 71-94.

51.

Khankov

, & Penikas

(2015). Modelling probability of default of Russian banks and companies using copula models. Retrieved from University of Pavia. Series DEM “Department of Economics and Management Working Paper Series” No. 113: http://dem-web.unipv.it/web/docs/dipeco/quad/ps/RePEc/pav/demwpp/DEMWP0113.pdf.

52.

Kumar

P. R.

, & Ravi

(2007). Bankruptcy prediction in banks and firms via statistical and intelligent techniques: A review. European journal of operational research, 180(1), 1-28.

53.

Lozinskaia

Merikas

Merika

, & Penikas

(2017). Determinants of the probability of default: The case of the internationally listed shipping corporations. Maritime Policy & Management, 4(7), 837-858.

54.

Luppi

Marzo

, & Scorcu

(2005ca). A credit risk model for Italian SMEs. Retrieved from https://core.ac.uk/download/pdf/6256309.pdf.

55.

Maarse

(2012). Backtesting framework for PD, EAD and LGD. Retrieved August 16, 2019, from https://essay.utwente.nl/61905/1/master_B._Maarse.pdf.

56.

Maskin

(2020). Arrow’s Theorem, May’s Axioms, and Borda’s Rule. Retrieved from https://scholar.harvard.edu/maskin/publications/arrow%E2%80%99s-iia-condition-may%E2%80%99s-axioms-and-borda-count

57.

Mogilat

A. N.

(2015). Bankruptcies of the real-sector Russian comapnies: Trends, structural characteristics, and major factors [in Russian]. Retrieved from http://www.forecast.ru/_ARCHIVE/Presentations/HSE/2015/bank042015.pdf.

58.

Mogilat

A. N.

(2019). Evaluation of the Russian industrial companies financial standing, or what do bankruptcies tell us? [in Russian]. Voprosy Economiki, 3, 101-118.

59.

Moody’s. (2007). Back-Testing Moody’s LGD Methodology. New York, London. Retrieved August 19, 2019, from https://www.moodys.com/sites/products/DefaultResearch/2006600000441444.pdf.

60.

Morgunov

(2017). Investment projects’ risk evaluation. Abstract for the Candidate of Sciences thesis [in Russian]. Moscow: HSE.

61.

Orgeldinger

(2006). Basel II and Project Finance - The development of a Basel II conforming rating model. The Journal of Structured Finance, 11(4), 84-95. doi: 10.3905/jsf.2006.614085.

62.

Oyama

, & Yoneyama

(2005). Advancing credit risk management through internal rating systems. Retrieved from Bank of Japan: https://www.boj.or.jp/en/research/brp/ron_2005/data/fsk0509a.pdf.

63.

Ozdemir

, & Miu

(2009). Basel II Implementation. A guide to developing and validating a compliant internal risk rating system. New York: McGrawHill.

64.

Pagano

, & Jappelli

(1993). Information sharing in credit markets. Journal of Finance, 48(5), 1693-1718.

65.

Penikas

(2013). Dead weight losses existence when using bank-specific internal data for the basel II IRB credit risk Models [In Russian]. “Econometric Methods for the Research of the Global Economic Processes” Conference Proceedings(pp. 149-155). Moscow: Ankil.

66.

Penikas

(2020a). History of the Basel internal-ratings-based (IRB) credit risk regulation. Model Assisted Statistics and Applications, 15, 81-98.

67.

Penikas

(2020b). Low default portfolios in basel II and basel III as a special case of significantly unbalanced classes in binary choice models. Russian Journal of Money and Finance, 79(2), 101-128.

68.

Penikas

(2020c). Why the conservative basel III portfolio credit risk model underestimates losses? MACSPro (p. in print). CEUR-WS.

69.

Penikas

Andrievskaya

Connolly

, & Aivazian

(2011). Modeling risk patterns of Russian systemically important financial Institutions. Review of Applied Socio-Economic Research, 1(1), 70-80.

70.

Peresetsky

(2012). Econometric methods in the distance analysis of the Russian banks’ activities [in Russian]. Moscow: HSE Publishing House.

71.

Peresetsky

A. A.

(2013). Modeling the causes of the Russian banks’ licence withdrawal: The impact of the unaccounted factors [in Russian]. Applied Econometrics, 30(2), 49-64.

72.

Peresetsky

Karminsky

, & Golovan

(2004). Probability of default models of Russian Banks. BOFIT. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1015451.

73.

Pompe

, & Bilderbeek

(2005). The prediction of bankruptcy of small- and medium-sized industrial firms. Journal of Business Venturing, 20(6), 847-868.

74.

Powell

Mylenko

Miller

, & Majnoni

(2004). Improving credit Information, Bank Regulation and Supervision: On the Role and design of Public Credit Registries. Retrieved from http://www-wds.worldbank.org/servlet/WDSContentServer/WDSP/IB/2004/12/17/000160016_20041217171024/Rendered/PDF/WPS3443.pdf.

75.

Quan

Lei

, & Shi

(2019). Review of bankruptcy prediction using machine learning and deep learning techniques. Procedia Computer Science, 162, 895-899.

76.

Repullo

, & Suarez

(2000). Entrepreneurial moral hazard and bank monitoring: A model of the credit channel. European Economic Review, 44, 1931-1950.

77.

Sabato

(2006). Managing credit risk for retail low default portfolios. Retrieved from SSRN: https://ssrn.com/abstract=908373.

78.

SAS. (2012). Appendix 7. statistical measures used in basel II reports. Retrieved from SAS Model Manager 12.1. User’s Guide: https://support.sas.com/documentation/cdl/en/mdsug/65072/PDF/default/mdsug.pdf.

79.

Sathe

(2006). A novel bayesian classifier using copula functions. Retrieved from http://arxiv.org/pdf/cs/0611150v1.pdf.

80.

Scheungrab

(2013). Copula based discriminant analysis with application. Retrieved from Technische University at Munchen, Zentrum Mathematik, Master Thesis: https://mediatum.ub.tum.de/doc/1166376/1166376.pdf.

81.

Shibitov

, & Mamedli

(2019). The finer points of model comparison in machine learning: Forecasting based on Russian banks’ data. Retrieved from Bank of Russia Working Paper Series No. 43: http://www.cbr.ru/content/document/file/87572/wp43_e.pdf.

82.

Sproates

(2017). Internal risk components validation: Indicative benchmarking of discriminatory power for LGD models (Public Version). Retrieved August 19, 2019, from https://essay.utwente.nl/71977/2/Sproates_MA_BMS.pdf.

83.

Surzhko

(2014). Approach for multi-period PD calibration for LDP portfolios. Retrieved 21, 2018, from http://vixra.org/pdf/1404.0001v1.pdf.

84.

Teruia

, & van Dijk

H. K.

(2002). Combined forecasts from linear and nonlinear time series models. International Journal of Forecasting, 18, 421-438.

85.

Totmyanina

(2014). Corporate borrowers default probability modeling accounting for the macroeconomic environment [in Russian]. Corporate Finance, 8(1), 18-30.

86.

Totmyanina

K. M.

(2011). Review of probability of default models [in Russian]. Risk Management Journal [in Russian](1), 12-24.

87.

van Dijk

, & Franses

P. H.

(2019). Combining expert-adjusted forecasts. Journal of Forecasting, 38, 415-421.

88.

Vasicek

(2002). The distribution of loan portfolio value. Risk, 160-162. Retrieved July 20, 2018, from Reprinted in Risk in December 2002: https://www.bankofgreece.gr/MediaAttachments/Vasicek.pdf.

89.

Vozzella

, & Gabbi

(2010). Default and asset correlation: An empirical study for italian SMEs. Retrieved from SSRN: https://ssrn.com/abstract=1532222.

90.

Vujnović

Nikolić

, & Vujnović

(2016). Validation of loss given default for corporate. Journal of Applied Engineering Science, 14(4), 465-476. doi: 10.5937/jaes14-11752.

91.

Yao

Crook

, & Andreeva

(2014). Modeling loss given default in SAS/STAT. Edinburgh. Retrieved August 19, 2019, from https://support.sas.com/resources/papers/proceedings14/1593-2014.pdf.

92.

Young

(1995). Optimal voting rules. Journal of Economic Perspectives, 9(1), 51-64.

93.

Zhivaikina

A. D.

, & Peresetsky

(2017). Russian banks’ credit ratings and banking licences’ withdrawal in 2012-2016 [in Russian]. Zhournal Novoi Economitcheskoi Associacii, 36(4), 49-80.