ACADEMY OF ECONOMIC STUDIES DOCTORAL SCHOOL OF FINANCE AND BANKING

1. ACADEMY OF ECONOMIC STUDIES DOCTORAL SCHOOL OF FINANCE AND BANKING DISSERTATION PAPER Testing the internal rating system of a commercial bank Discrete choice models Student: Hurmuz Manuela Supervisor: Professor Moisa Altar BUCHAREST, JULY 2006

2. Contents • Introduction • 2. Literature review • Model description • Empirical results • Conclusions

3. 1. Introduction Standing for the core business of a bank………………. LENDING ACTIVITY “Credit risk* is the risk that a counterparty that owes, or potentially owes a bank money fails to meet its obligations.” *(Jorion, Philippe, 2003, „Financial Risk Manager Handbook, second edition”, John Wiley & Sons)

4. 2. Literature review • Charitou, Neophythou and Charalambous (2004) - The main purpose of the study was • to examine the incremental information content of operating cash flows in predicting • financial distress and thus develop reliable failure prediction models for UK public • industrial firms. The results yielded an overall correct classification accuracy of 83% 1 • year prior to the failure; • Ralf Ewert/ Andrea Szczesny (2001) use it in testing internal rating systems compliance • with the Basel II Capital Accord. They find that German banks are not ready for the • internal ratings-based approach. Furthermore, rating systems not comparable over • banks and they reveal differences between credit rating determining and default • probability determining factors respectively. • While some authors - Lee C. Spector and Michael Mazzaeo (1980) argue that the LPM, which is based on the OLS, has too many shortcomings and propose as alternative the probit analysis, Noreen (1988) demonstrates that the OLS regression performs at least as well as probit in what accounting classificatory studies are concerned

5. Rating classes Risk factors Probability of default 3. Model description • Ralf Ewert/ Andrea Szczesny -“Countdown for the New Basle Capital Accord: • Are German Banks ready for the Internal Ratings-Based Approach” • Goal: Test whether the same risk factors determine the probability of default as well as the probability of falling into a specific rating category. In addition we verifiy the ratings’ capacity to predict PD

6. Model’s variables • Interest cover ratio • Ordinary income margin • EBTDA margin • Equity ratio • Return on assets • Debt amortization period • Dummy variables for industry effects

7. Methodology • Linear probability model • Yi = β1 + β2Xi + Ui • P(y = 1) = 1 + 2X2 + … + kXk • Main problems: • error term is non-normally distributed • the error term is heteroskedastic • limited usefulness of R2 • marginal effects remain constant • Non-fulfilment of 0  E(Yi)  1.

8. B) Probit model Y*t = α + βXt + εt where Yt* is an unobservable variable Yt = 1 if α + βXt + εt > 0 Yt = 0 if α + βXt + εt < 0 εt ~ N(0, σ2) C) Ordered probit

9. 4. Empirical results • Data source: rating system of a commercial bank • Testing the series: • the series are not normally distributed • all series are stationary as per ADF test Null hypothesis: the series is non stationary • For example: Debt amortization period

10. Estimated equation - OLS dummy_default debt_am ebtda_margin er interest_cover ordinary_income roa commodity_trade manufacturing construction services c

11. White test of hetroskedasticity

12. Estimation Output - OLS

13. Estimating the equation with binary Probit

14. Expectation prediction table

15. Goodness of fit

16. Testing the relation between rating categories and risk factors - ordered probit

17. Expectation prediction table Dependent variable frequencies

18. Testing the relation between probability of default and the rating categories Estimated equation: dummy_default c rating2 rating3 rating4 rating5 rating6 rating7 rating8 rating10

19. Expectation prediction table

20. Goodness of fit

21. 5. Conclusions • testing the proposed modelsled to the conclusion that none of them is significant thus we were not able to conduct the comparison proposed at the beginning • low database for the probability of default • high correlations between the financial ratios standing at the base of the rating system • no data was introduced to capture macroeconomic effects • more qualitative data needed (e.g. size, age) • financial ratios as well as ratings were restricted to only one year

22. 6. Bibliography • Bessis, Joel (2002), “Risk Management in Banking”, second edition, John Wiley & Sons Ltd, England • Jorion, Philippe (2003), „Financial Risk Manager Handbook”, second edition, John Wiley & Sons • Damodaran, Aswath (1996) „Investment valuation. Tools and techniques for determining the value of any asset, John Wiley & Sons, Inc., Canada • Shimko, David (2004), “Credit risk models and Management”, second edition, Incisive Financial Publishing Ltd., London • Cramer, J.S. (2001), “An introduction to the logit model for economists”, second edition, Timberlake Consultants Ltd., London • Gujarati, Damodar N. (1995), “Basic Econometrics”, third edition, McGraw-Hill, Inc., USA • Green, William H. (2000), Econometric Analysis, New York University • Noreen, Eric (1988), “An empirical comparison of probit and OLS regression hypothesis tests”, Journal of Accounting Research, Vol. 26, No. 1, pp. 119-133

23. 9. Kaiser, Ulrich and Szczesny, Andrea (2000), “Einfache ökonometrische Verfahren für die Kreditrisikomessung: Logit- und Probit-Modelle“, Johann Wolfang Goethe Universitaet Frankfurt am Main 10.Mazzaeo, Michael and Spector, Lee C. (1980), “Probit Analysis and Economic Education”, The journal of economic education, Vol. 11, No.2, pp. 37-44 11. Abbot, Robert D, Bailey, Kent T., Caroll, Raymond J., Spiegelmann, Clifford H. and LAn, Gordon (1984), “On errors in variables for binary regression models”, Biometrika, Vol. 71, No. 1, pp. 19-25 12. Boes, Stefan and Winkelmann, Rainer, “Ordered response models”, Allgemeines Statistisches Archiv, Zuerich University 13. Galil, Koresh (2005), „Rtaings as predictors of default in the long term: and empirical investigation“, Monaster Center for Economical Research, Israel 14. Gascoigne, Jamie and Turner, Paul (2003), “Asymmetries in the Bank of England monetary policy”, Sheffield University, UK 15. Villalobos, Pablo and Wolff, Hendrik (2002), “Willingness to pay for an environmental good in Chile – Concept and application” American Meeting of the Econometric Society on the campus of Escola de Administração de Empresas de São Paulo da Fundação Getulio Vargas 16. Machauer, Achim and Weber, Martin (1998), “Bank behavior based on internal credit ratings of borrowers”, Center for financial studies, Frankfurt am Main