Credit risk mangement in finance : a review of various approaches

• Autorzy: Wójcicka-Wójtowicz A.

Identyfikatory

DOI

10.5277/ord180407

• Języki publikacji: EN

Abstrakty

EN

Classification of customers of banks and financial institutions is an important task in today’s business world. Reducing the number of loans granted to companies of questionable credibility can positively influence banks’ performance. The appropriate measurement of potential bankruptcy or probability of default is another step in credit risk management. Among the most commonly used methods, we can enumerate discriminant analysis models, scoring methods, decision trees, logit and probit regression, neural networks, probability of default models, standard models, reduced models, etc. This paper investigates the use of various methods used in the initial step of credit risk management and corresponding decision process. Their potential advantages and drawbacks from the point of view of the principles for the management of credit risk are presented. A comparison of their usability and accuracy is also made.

Słowa kluczowe

EN

credit risk; default; bankruptcy; credit risk management; credit risk models

PL

ryzyko kredytowe; bankructwo; zarządzanie ryzykiem kredytowym; modele ryzyka kredytowego

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Operations Research and Decisions
• Rocznik: 2018
• Tom: Vol. 28, No. 4
• Strony: 99--106
• Opis fizyczny: Bibliogr. 24 poz., rys.

Twórcy

autor

Wójcicka-Wójtowicz A.

• Department of Operations Research, Faculty of Informatics and Electronic Economy, University of Economics and Business, Aleja Niepodległości 10, 61-875 Poznań, Poland

Bibliografia

• [1] ALTMAN E., RESTI A., SIRONI A., Default Recovery Rates in Credit Risk Modelling: A Review of the Literature and Empirical Evidence, Economic Notes by Banca Monte dei Paschi di Siena SpA, 2004, 33 (2).
• [2] AMMANN M., Pricing derivative credit risk, Springer, 1999, 47–65.
• [3] BENOS A., PAPANASTASOPOULOS G., Extending the Merton model. A hybrid approach to assessing credit quality, Math. Comp. Model., 2007, 46 (1, 2), 47–68.
• [4] BROWN K., MOLES P., Credit risk management, CR-A3 I/2014(1044).
• [5] BYSTRÖM H., A flexible way of modelling default risk, 2004 http://www.business.uts.edu.au/qfrc/research/research_papers/rp112.pdf (access 1.10.2018).
• [6] BYSTRÖM H., KWON K., A simple continuous measure of credit risk, working paper, 2003, http://ideas.repec.org/a/eee/finana/v16y2007i5p508-523.html (access 10.09.2018).
• [7] ELIZALDE A., Credit risk models I. Default correlations in intensity models, working paper, 2005,http://www.abelelizalde.com
• [8] FRYE J., Depressing recoveries, Risk, 2000, 8, 108–111.
• [9] GRABCZAN W., Managing banking risk, Fundacja Rozwoju Rachunkowości w Polsce, Warsaw 1996 (in Polish).
• [10] HULL J.C., NELKEN I., WHITE A., Merton’s Model, Credit Risk, and Volatility Skews, J. Credit Risk,2004, 1 (1), 3–28.
• [11] JAJUGA K., On systemizing credit risk models, [In:] Business bankruptcy in Poland in the years 1990–2003. Theory and practice, D. Appenzeller (Ed.), Zeszyty Naukowe AE, No. 49, Poznań 2004.
• [12] KACZMAREK T., Management of trading and financial risk for practitioners, Ośrodek Doradztwa i Doskonalenia Kadr, Gdańsk 1999.
• [13] KRASKA M., Credit scoring i credit rating. Applications in commercial banking, Biz. Fin., Warsaw 2004.
• [14] MERTON R., On the pricing of corporate debt. The risk structure of interest rates, J. Fin., 1974, 29, 449–470.
• [15] PRITCHARD C.L., Managing project risk. Theory and practice, WIG-Press, Warsaw 2002 (in Polish).
• [16] SAUNDERS A., Credit Risk Measurement. KMV, VAR, CreditMetrics, LAS, RAROC, CreditRisk+, Oficyna Ekonomiczna, Krakow 2001 (in Polish).
• [17] WÓJCIAK M., Methods of assessing credit risk, PWE, Warsaw 2007 (in Polish).
• [18] WÓJCICKA A., Applying a modification of Moody’s KMV model to the assessment of credit risk, [In:] From the work of the Department of Operational Research, W. Sikora (Ed.), Poznań University of Economics and Business, 2008, No. 104, 58–173 (in Polish).
• [19] WÓJCICKA A., Estimating the premium for market risk using models for assessing credit risk under the conditions of the Polish economy, Investment in the capital markets, Studies and Work of the Faculty of Economic Sciences and Management, Szczecin 2008, No. 10, 674– 684.
• [20] WÓJCICKA A., Sensitivity of the MKMV model to the method of estimating the growth rate of the value of assets, [In:] Taxonomy 15. Classification and data analysis – theory and applications, K. Jajuga, M. Walesiak (Eds.), Wrocław University of Economics, Wrocław 2008, No. 7 (1207), 182–189.
• [21] WÓJCIK-MAZUR A., Managing credit risk in commercial banks, Czestochowa University of Technology, ser. Monograpphies, No. 143, Częstochowa 2008 (in Polish).
• [22] TARASHEV N., Structural models of default: lessons from firm-level data, BIS Quart. Rev., 2005, 99–108.
• [23] TRUECK S., RACHEV S.T., Rating Based Modeling of Credit Risk: Theory and Application of Migration Matrices, Academic Press Advanced Finance Series, Elsevier Inc., Burlington 2009.
• [24] Principles for the management of credit risk, Basel Committee on Banking Supervision, Basel 2000, https://www.bis.org/publ/bcbs75.pdf (access 17.01.2018).

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).