Migration Analysis of Credit Risk in Tunisian Banking Sector

  • Amel Ben Youssef Faculty of Economic sciencesand Management of Tunisia, FSEGT, El Manar, Tunis, Tunisia
Keywords: Transition Matrices, Credit Risk,Banking Sector

Abstract

In this paper, credit migration matrices are built to measuretransition probabilitiesat Tunisian credit institutions, allowing a comparison of credit risk quality shiftsfor public banks, private banks and leasing companies. We proposeto apply estimating Markov transition matrices using proportions data in order to be adapted to the scarcity of individual dataonloan quality transitions. We employ annual classification of assets issued in theregistration documents and annual financial reports during 2003-2014 period.It’s found from the analysis that the risk grade 2 has the greater tendancy to be downgraded than to be upgraded in public banks and in leasing companies.For the other risk grade 3, the upgradation in the category is higher than the downgradation in all cases. The resultsindicate that the public banks are the riskiest credit institution in Tunisia and there is a lack of rigor in loan classification inpublic and private banks. The findings are useful and critical for supervisory purposes and foroptimizing bank credit risk management.

References

Tunisian Central Bank (2014). Rapport sur la Supervision Bancaire. Banque Centrale de Tunisie.
IMF (2012). Tunisie: Évaluation de la stabilité du système financier. FMI report, 12/241.
J.P. Morgan & Co (1997). CreditMetrics™ - Technical Document. J.P. Morgan & Co. Incorporated.
Basel committee on Banking Supervision (2003). The New Basel Capital Accord: consultative document. Bank for International Settlements.
Basel committee on Banking Supervision (2004). International convergence of capital measurement and capital standards. Bank for International Settlements.
Basel committee on Banking Supervision (2010). Basel III: A global regulatory framework for more resilient banks and banking systems. Bank for International Settlements.
Bajaj, R.V.(2010). Migration analysis of Indian corporate: Rating-Based approach. The IUP Journal of Financial Risk Management, 7, 24-34.
BCT (2014). Rapport sur la Supervision Bancaire. Tunisian Central Bank.
Ivičić, L.,&Cerovac, S.(2009). Credit risk assessment of corporate sector in Croatia.Financial Theory and Practice, 33, 373-399. http://hrcak.srce.hr/48606
Jafry, Y., &Schuermann, T. (2004). Measurement, estimation and comparison of credit migration matrices. Journal of Banking & Finance, 28, 2603-2639.doi:10.1016/j.jbankfin.2004.06.004
Grzybowska, U., Karwańskia, M. &Orłowski, A. (2012). Examples of migration matrices models and their performance in Credit Risk Analysis.doi:10.12693/APhysPolA.121.B-40
Jones, M. (2005). Estimating Markov transition matrices using proportions data : an application to credit risk. IMF working paper 05/219
Nicula, I. (2013). Some aspects concerning the measurement of credit risk.Procedia Economics and Finance, 6, 668 – 674. doi: 10.1016/S2212-5671(13)00187-1
Lando,D.&Skødeberg, T. (2002) .Analyzing rating transitions and rating drift with continuous observations. Journal of Banking & Finance, 26, 423–444. doi: 10.1016/S0378-4266(01)00228-X
Perilioglu, A.&Tuysuz, S.(2015). Conditional Sovereign Transition Probability Matrices. Procedia Economics and Finance, 30, 643-655. https://doi.org/10.1016/S2212-5671(15)01283-6
Schechtman, R. (2013).Default matrices : A complete measurement of bank’s consumer credit delinquency.Journal of Financial Stability, 9, 460-474. doi:10.1016/j.jfs.2013.07.001
Published
2018-03-09
How to Cite
Youssef, A. B. (2018). Migration Analysis of Credit Risk in Tunisian Banking Sector. Indian Journal of Finance and Banking, 2(1), 34-43. https://doi.org/10.46281/ijfb.v2i1.91
Section
Research Paper/Theoretical Paper/Review Paper/Short Communication Paper