IDEAS home Printed from https://ideas.repec.org/p/cab/wpaefr/39.html
   My bibliography  Save this paper

Early Warning Models for Banking Supervision in Romania

Author

Listed:
  • Radu Muntean

Abstract

In this paper we propose an early warning system for the Romanian banking sector, as an addition to the standardized CAAMPL rating system used by the National Bank of Romania for assessing the local credit institutions. We aim to find the determinants for downgrades as well as for a bank to have a weak overall position, to estimate the respective probabilities and to be able to perform rating predictions. Having this purpose, we build two models with binary dependent variables and one ordered logistic model that accounts for all possible future ratings. One result is that indicators for current position, market share, profitability and assets quality determine rating downgrades, whereas capital adequacy, liquidity and macroeconomic environment are not represented in the model. Banks that will have a weak overall position in one year can be predicted using also indicators for current position, market share, profitability and assets quality, as well as, in this case, capital adequacy and macroeconomic environment, the latter only for the binary dependent variable model, leaving liquidity indicators out again. Based on the ordered logistic model’s capacity for rating prediction, we estimated one year horizon scores and ratings for each bank and we aggregated these results for predicting a measure of assessing the local banking sector as a whole.

Suggested Citation

  • Radu Muntean, 2009. "Early Warning Models for Banking Supervision in Romania," Advances in Economic and Financial Research - DOFIN Working Paper Series 39, Bucharest University of Economics, Center for Advanced Research in Finance and Banking - CARFIB.
    • Handle: RePEc:cab:wpaefr:39
    as

    Download full text from publisher

    File URL: http://www.dofin.ase.ro/Working%20papers/Muntean%20Radu/muntean.radu.dissertation.pdf
    File Function: First version, 2009
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rupa Duttagupta & Mr. Paul Cashin, 2008. "The Anatomy of Banking Crises," IMF Working Papers 2008/093, International Monetary Fund.
    2. 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.
    3. Mr. Tigran Poghosyan & Mr. Martin Cihak, 2009. "Distress in European Banks: An Analysis Basedon a New Dataset," IMF Working Papers 2009/009, International Monetary Fund.
    4. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    5. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    6. Kolari, James & Glennon, Dennis & Shin, Hwan & Caputo, Michele, 2002. "Predicting large US commercial bank failures," Journal of Economics and Business, Elsevier, vol. 54(4), pages 361-387.
    7. Allen N. Berger & Margaret K. Kyle & Joseph M. Scalise, 2001. "Did US Bank Supervisors Get Tougher during the Credit Crunch? Did They Get Easier during the Banking Boom? Did It Matter to Bank Lending?," NBER Chapters, in: Prudential Supervision: What Works and What Doesn't, pages 301-356, National Bureau of Economic Research, Inc.
    8. R. Alton Gilbert & Andrew P. Meyer & Mark D. Vaughan, 2000. "The role of a CAMEL downgrade model in bank surveillance," Working Papers 2000-021, Federal Reserve Bank of St. Louis.
    9. Jitka Rychtarikova, 2004. "The case of the Czech Republic," Demographic Research Special Collections, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 2(5), pages 105-138.
    10. Engelmann, Bernd & Hayden, Evelyn & Tasche, Dirk, 2003. "Measuring the Discriminative Power of Rating Systems," Discussion Paper Series 2: Banking and Financial Studies 2003,01, Deutsche Bundesbank.
    11. Mr. Martin Cihak & Mr. Klaus Schaeck, 2007. "How Well Do Aggregate Bank Ratios Identify Banking Problems?," IMF Working Papers 2007/275, International Monetary Fund.
    12. David C. Wheelock & Paul W. Wilson, 2000. "Why do Banks Disappear? The Determinants of U.S. Bank Failures and Acquisitions," The Review of Economics and Statistics, MIT Press, vol. 82(1), pages 127-138, February.
    13. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    14. Alexis Derviz & JiÅí Podpiera, 2008. "Predicting Bank CAMELS and S&P Ratings: The Case of the Czech Republic," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 44(1), pages 117-130, January.
    15. Mr. Rupert D Worrell, 2004. "Quantitative Assessment of the Financial Sector: An Integrated Approach," IMF Working Papers 2004/153, International Monetary Fund.
    16. Julapa Jagtiani & James Kolari & Catharine Lemieux & G. Hwan Shin, 2003. "Early warning models for bank supervision: Simpler could be better," Economic Perspectives, Federal Reserve Bank of Chicago, vol. 27(Q III), pages 49-60.
    Full references (including those not matched with items on IDEAS)

    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. Peresetsky, A. A., 2011. "What factors drive the Russian banks license withdrawal," MPRA Paper 41507, University Library of Munich, Germany.
    2. Zeineb Affes & Rania Hentati-Kaffel, 2016. "Predicting US banks bankruptcy: logit versus Canonical Discriminant analysis," Post-Print halshs-01281948, HAL.
    3. Zeineb Affes & Rania Hentati-Kaffel, 2016. "Predicting US banks bankruptcy: logit versus Canonical Discriminant analysis," Documents de travail du Centre d'Economie de la Sorbonne 16016, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    4. Li Xian Liu & Shuangzhe Liu & Milind Sathye, 2021. "Predicting Bank Failures: A Synthesis of Literature and Directions for Future Research," JRFM, MDPI, vol. 14(10), pages 1-24, October.
    5. Zeineb Affes & Rania Hentati-Kaffel, 2016. "Forecast bankruptcy using a blend of clustering and MARS model - Case of US banks," Post-Print halshs-01314553, HAL.
    6. Duffie, Darrell & Saita, Leandro & Wang, Ke, 2007. "Multi-period corporate default prediction with stochastic covariates," Journal of Financial Economics, Elsevier, vol. 83(3), pages 635-665, March.
    7. repec:zbw:bofrdp:2009_035 is not listed on IDEAS
    8. Anatoly Peresetsky & Alexandr Karminsky & Sergei Golovan, 2011. "Probability of default models of Russian banks," Economic Change and Restructuring, Springer, vol. 44(4), pages 297-334, November.
    9. Peresetsky, Anatoly, 2013. "Modeling reasons for Russian bank license withdrawal: Unaccounted factors," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 30(2), pages 49-64.
    10. Ke Wang & Darrell Duffie, 2004. "Multi-Period Corporate Failure Prediction With Stochastic Covariates," Econometric Society 2004 Far Eastern Meetings 747, Econometric Society.
    11. fernández, María t. Tascón & gutiérrez, Francisco J. Castaño, 2012. "Variables y Modelos Para La Identificación y Predicción Del Fracaso Empresarial: Revisión de La Investigación Empírica Reciente," Revista de Contabilidad - Spanish Accounting Review, Elsevier, vol. 15(1), pages 7-58.
    12. Alexandros Benos & George Papanastasopoulos, 2005. "Extending the Merton Model: A Hybrid Approach to Assessing Credit Quality," Finance 0505020, University Library of Munich, Germany, revised 18 Nov 2005.
    13. Costeiu, Adrian & Neagu, Florian, 2013. "Bridging the banking sector with the real economy: a financial stability perspective," Working Paper Series 1592, European Central Bank.
    14. Jaizah Othman & Mehmet Asutay, 2018. "Integrated early warning prediction model for Islamic banks: the Malaysian case," Journal of Banking Regulation, Palgrave Macmillan, vol. 19(2), pages 118-130, April.
    15. repec:zbw:bofitp:2004_021 is not listed on IDEAS
    16. Zeineb Affes & Rania Hentati-Kaffel, 2019. "Forecast bankruptcy using a blend of clustering and MARS model: case of US banks," Annals of Operations Research, Springer, vol. 281(1), pages 27-64, October.
    17. Anatoly Peresetsky & Alexandr Karminsky & Sergei Golovan, 2011. "Probability of default models of Russian banks," Economic Change and Restructuring, Springer, vol. 44(4), pages 297-334, November.
    18. Demyanyk, Yuliya & Hasan, Iftekhar, 2010. "Financial crises and bank failures: A review of prediction methods," Omega, Elsevier, vol. 38(5), pages 315-324, October.
    19. Psillaki, Maria & Tsolas, Ioannis E. & Margaritis, Dimitris, 2010. "Evaluation of credit risk based on firm performance," European Journal of Operational Research, Elsevier, vol. 201(3), pages 873-881, March.
    20. Fiordelisi, Franco & Mare, Davide Salvatore, 2013. "Probability of default and efficiency in cooperative banking," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 26(C), pages 30-45.
    21. Petropoulos, Anastasios & Siakoulis, Vasilis & Stavroulakis, Evangelos & Vlachogiannakis, Nikolaos E., 2020. "Predicting bank insolvencies using machine learning techniques," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1092-1113.
    22. Zeineb Affes & Rania Hentati-Kaffel, 2016. "Forecast bankruptcy using a blend of clustering and MARS model - Case of US banks," Documents de travail du Centre d'Economie de la Sorbonne 16026, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.

    More about this item

    Keywords

    early warning system; CAAMPL rating system;

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:cab:wpaefr:39. 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: Ciprian Necula (email available below). General contact details of provider: https://edirc.repec.org/data/caasero.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.