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Credit risk of non -financial companies in the context of financial stability

This study examines credit risk of non-financial companies and its impact on financial stability. It covers models, methodology, stress-testing, and conclusions from data analysis and validation. Results present probabilities of default at economy and sector levels for different time frames.

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Credit risk of non -financial companies in the context of financial stability

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  1. Academy of Economic Studies Doctoral School of Finance and Banking Credit risk of non-financial companies in the context of financial stability Credit risk of non-financial companies in the context of financial stability MSc Student: Romulus Mircea Supervisor: Professor Moisă Altăr Bucharest, July 2007

  2. Topics • Preliminary aspects • Credit risk models in practice • Methodology and input data • Results • Stress-testing • Conclusions

  3. 1. Preliminary aspects 1.1. Importance of credit risk assessment models: - Entities who buy and sell credit risk - Central authorities 1.2. Objectives: - Determinants of default for non-financial companies - Estimate probabilities of default - Evaluate risks to financial stability - Stress-testing Stakeholders Conclusions

  4. 2. Credit risk models in practice • Logit models are among the best alternatives to model credit risk of non-publicly traded companies • Currently used by central banks from euro-zone area, in order to determine eligible collateral for refinancing operations (OeNB, BUBA, BDE) • Ohlson (1980), Lennox (1999), Bernhardsen (2001), Bunn (2003) • Multivariate discriminant analysis: Beaver(1966), Altman(1968), Bardos(1998), BdF current methodology as an ECAI • Does not produce a probability of default directly • Rather restrictive assumptions on underlying explanatory variables

  5. 3. Methodology and input data • Methodology (1) • Default = 90 days past due bank loans obligations (Basel II) • Explanatory variables are financial ratios derived from firms’ financial statements

  6. 3. Methodology and input data • Methodology (2) • Variable selection filters • Ratios hypothesis tests using KS test • Monotony and linearity tests • Univariate accuracy tests • Multicolinearity • Model estimation • Bootstrapping Logit using a backward selection methodology on a 50:50 sample of defaulting to non-defaulting firms • Calibrate probabilities of default on the real portfolio • Model Validation • Economic performance measures: • ROC/AR, Hit rates, False alarm rates • Statistical performance measures: Hosmer Lemeshow test, Spiegelhalter test Skip

  7. Monotony and linearity tests - Logit models imply a linear and monotonous relationship between the log odd of default and explanatory variables • Steps: • Order observations relative to each variable • Divide dataset in several subgroups • Compute for each subgroup the mean of the considered variable and the log odd of default • Run OLS: log odd against explanatory variable • Check OLS assumptions and exclude variables Back

  8. Calibrating probabilities of default to the real portfolio - King (2001) - Adjustment to intercept only, MLE of β need not be changed: Back

  9. Economic performance Receiver operator characteristic Cumulative accuracy profile Back

  10. Statistical performance measures - Hosmer Lemeshow test: - Spiegelhalter test: Back

  11. 3. Methodology and input data • Methodology (3) - Measures for risk to financial stability via the direct channel:

  12. 3. Methodology and input data • Input data (1) • Explanatory variables from financial statements reported by the non-financial companies to MPF • Default information from credit register

  13. Balance-sheet ratios • Leverage • Liquidity • Investment behavior • Size • Growth • Income statement: • Profitability • Expense Structure • Size • Growth • Mixed sources: • Cashflows • Debt coverage • ratios 3. Methodology and input data • Input data (2) • Assumption: accounting data provides an accurate picture of firms’ financial position • 40+ explanatory variables covering different financial features • Accounting issues that may impair a financial ratio’s explanatory power: • Different cost flow methods (LIFO/FIFO) • Capitalizing vs. expensing costs decisions

  14. 4. Results (1) • Model 1: 1 year probability of default at economy level

  15. 4. Results (2) • Model 1: Validation • ROC: 74.2% (in sample), 75% (out of sample), 75.3% (out of time) • Neutral cost policy function: 2.3% (cutoff), 71.7% (Hit rate), 32.7% (False alarm rate)

  16. 4. Results (3) • Model 1: 1 year probability of default dynamics

  17. 4. Results (4) • Model 1: 1 year probability of default at sector level (2006)

  18. 4. Results (5) • Model 1

  19. 4. Results (6) • Model 2: 3 years probability of default at economy level

  20. 4. Results (7) • Model 2: Validation • ROC: 74.1% (in sample), 73.12% (out of sample) • Neutral cost policy function: 5.5% (cutoff), 73.8% (Hit rate), 37.7% (False alarm rate

  21. 4. Results (8) • Model 2: 3 years (2006-2008) vs 1 year (2006) probability of default

  22. 4. Results (9) • Model 3: 1 year probability of default for large firms

  23. 4. Results (10) • Model 3: Validation • ROC: 80.57% (in sample) • HL-test: 15.88 (critical value 21) • Neutral cost policy function: 2.3% (optimal cutoff), 89.5% (hit rate), 42% (false alarm rate)

  24. 4. Results (11) • Model 3: 1 year probability of default dynamics for large firms

  25. 4. Results (12) • Model 4: 1 year probability of default for foreign trade firms

  26. 4. Results (13) • Model 4: Validation • ROC: 78.8% (in sample), 79.1% (out of sample) • Neutral cost policy function:2.3% (optimal cutoff), 68.2% (hit rate), 23.4% (false alarm rate)

  27. 4. Results (14) • Model 4: 1 year probability of default for foreign trade firms

  28. 5. Stress-testing (1) • Aspects to consider when building stress-testing scenarios: • Consistency – taking into considerations all the implications of a shock on the financial position of a firm • Methods of incorporating shocks into explanatory variables: identity relationships or estimations • Assumptions – for situations when information is not available Impact of interest rate adjustments on 1 year and 3 years probabilities of default

  29. 6. Conclusions • Determinants of default: • at economy level trade arrears, interest burden and receivables cash conversion cycle are the most frequent determinants of default • Productivity - specific determinant of default for large firms • Share of labor costs to total operating costs – specific determinant of default for foreign trade firms • Risks to financial stability: • Bank loans are concentrated into above average risk firms… • …but debt at risk is well provisioned by banks • Manufacturing and trade sectors have the lowest probability of default • Large firms are more likely to default when compared to all non-financial companies, but their effective defaulted debt is lower  benign risks to financial stability • Foreign trade firms are less riskier, with importers having the lowest probability of default while exporters present the highest risk of default

  30. 6. Conclusions • Stress-testing: • We have come up with a solution to measure the impact of interest rate changes on the probability of default • Modest impact on probabilities of default even for large interest rate adjustments • Further research on this area would include: • Refining the dataset used • Improving model calibration • Accounting for correlations across firms Return

  31. Bibliography (1) • Alexander, C., Elizabeth, S., 2004,“The Professional Risk Manager’s Handbook: A comprehensive guide to current theory and best practices”, PRMIA Institute • Altman, E., 1968, “Financial ratios, discriminant analysis and the prediction of corporate bankruptcy”, The Journal of Finance, 23, 589-609 • Beaver, W., 1966, “Financial ratios as predictors of failure”, Journal of Accounting Research, 4, 71-111 • Bardos, M., 1998., “ Detecting the risk of company failure at the Banque de France”, Journal of Banking and Finance, 22, 1405-1419 • Bardos, M., Zhu, W., 1997, “Comparaison de l’analyse discriminante lineaire et de reseaux de neurones. Application a la detection de defaillance d’entreprise”, Revue de statistique appliqué, 4, 65-92 • Balthazar, L., 2006, “From Basel 1 to Basel 3: The integration of State-of-the-Art risk modeling in banking regulation”, Palgrave Macmillan • BIS, 2005, “Studies on the validation of internal rating system”, Working Paper, 14 • Bernhardsen, E., 2001, “A model of bankruptcy prediction”, Norges Bank, Working Paper 10.

  32. Bibliography (2) • Bunn, P., Redwood, V., (2003), “Company accounts based modeling of business failures and the implications for financial stability”, Bank of England, Working paper no.210 • Dimitras, A., Zanakis, S., Zopounidis, C., 1996, “A survey of business failures with an emphasis on prediction methods and industrial applications”, European Journal of Operational Research, 90, 487-513 • Doumpos, M., Kosmidou, K., Baourakis, G., Zopounidis, C., 2002, “Credit risk assessment using hierarchical discrimination approach: A comparative analysis”, European Journal of Operational Research, 138, 392-412 • Engelmann, B., Hayden, E., Tasche, D., 2003, “Measuring the discriminatory power of rating systems”, Deutsche Bundesbank, Discussion paper 1 • Hammerle, A., Rauhmeier, R., Rosch, D., 2003, “Uses and misuses of measures for credit rating accuracy”, Regensburg University • Hammerle, A., Liebig, T., Scheule, H., 2004, “Forecasting Credit Portfolio Risk”, Deutsche Bundesbank, Discussion Paper, 1 • Hillegeist, S., Keating, E., Cram, D., Lundstedt, K., 2004, “Assessing the probability of bankruptcy”, Review of Accounting studies, 9, 5-34 • King, G., Zeng, L., 2001, “Logistic Regression in Rare Events Data”, Harvard University, Center for Basic Research in the Social Sciences.

  33. Bibliography (3) • Laitinen, E., Laitinen, T., 2001, “Bankruptcy prediction: Application of Taylor’s expansion in logistic regression”, University of Vaasa, Department of Accounting and Finance, Finland • Lennox, C., 1999, “Identifying failing companies: A reevaluation of the logit, probit and DA approaches”, Journal of Economics and Business, 51, 347-364 • Morris, R., 1997, „Early Warning Indicators of Corporate Failure”, Ashgate Publishing • Ohlson, J., 1980, „Financial ratios and the probabilistic prediction of bankruptcy”, Journal of Accounting Research, 18, 109-131 • Ooghe H., Claus, H., Sierens N., Camerlynck J., 1999, „International Comparison of Failure Prediction models from different countries: An empirical analysis”, Ghent University, Department of Corporate Finance, No.99/79

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