Abstract
The paper proposes a novel model for the prediction of bank failures, on the basis of both macroeconomic and bank-specific microeconomic factors. As bank failures are rare, in the paper we apply a regression method for binary data based on extreme value theory, which turns out to be more effective than classical logistic regression models, as it better leverages the information in the tail of the default distribution. The application of this model to the occurrence of bank defaults in a highly bank dependent economy (Italy) shows that, while microeconomic factors as well as regulatory capital are significant to explain proper failures, macroeconomic factors are relevant only when failures are defined not only in terms of actual defaults but also in terms of mergers and acquisitions. In terms of predictive accuracy, the model based on extreme value theory outperforms classical logistic regression models.
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References
Acharya VV, Pedersen LH, Philippon T and Richardson M (2010). Measuring systemic risk. Working Paper, Federal Reserve of Cleveland.
Adrian T and Brunnermeier MK (2010). Covar. Technical Report, Princeton University.
Agresti A (2002). Generalised Linear Models. Wiley: New York.
Altman E (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance 23 (4): 589–609.
Arena M (2008). Bank failures and bank fundamentals: A comparative analysis of Latin America and East Asia during the nineties using bank-level data. Journal of Banking and Finance 32 (2): 299–310.
Ashcraft AB (2008). Are bank holding companies a source of strength to their banking subsidiaries? Journal of Money, Credit and Banking 40 (3–4): 273–294.
Berger A, De Young R, Flannery MJ, Lee D and Oztekin O (2008). How do large banking organisations manage their capital ratios? Journal of Financial Services Research 34 (2): 123–149.
Billio M, Getmansky M, Lo A and Pelizzon L (2012). Econometric measures of connectedness and systemic risk in the finance and insurance sector. Journal of Financial Economics 104 (3): 535–559.
Bongini P, Claessens S and Ferri G (2001). The political economy of distress in east asian financial institutions. Journal of Financial Services Research 19 (1): 5–25.
Bongini P, Laeven L and Majnoni G (2002). How good is the market at assessing bank fragility? A horse race between different indicators. Journal of Banking and Finance 26 (5): 1011–1028.
Boyd JH, De Nicolo G and Jalal AM (2009). Bank competition, risk, and asset allocations. International Monetary Fund, Working Paper.
Brown C and Dinc IS (2009). Too many to fail? Evidence of regulatory forbearance when the banking sector is weak. Review of Financial Studies 24 (4): 1378–1405.
Brownlees CT and Engle RF (2011). Volatility. correlation and tails for systemic risk measurement. Technical Report, New York University.
Brunnermeier M and Oehmke M (2012). Bubbles, financial crises, and systemic risk. NBER Working Papers 18398, National Bureau of Economic Research.
Buehler K, Samandari H and Mazingo C (2009). Capital ratios and financial distress: Lessons from the crisis. McKinsey Working Papers on Risk.
Calabrese R and Elkink JA (2012). Estimators of binary spatial autoregressive models: A Monte Carlo study. Working Papers, Geary Institute, University College Dublin.
Calabrese R and Osmetti S (2013). Modelling SME loan defaults as rare events: An application to credit defaults. Journal of Applied Statistics 40 (6): 1172–1188.
Canbas S, Cabuk A and Kilic SB (2005). Prediction of commercial bank failure via multivariate statistical analysis of financial structures: The Turkish case. European Journal of Operational research 166 (2): 528–546.
Carapeto M, Moeller S, Faelten A, Vitkova V and Bortolotto L (2010). Distress classification measures in the banking sector. Technical Report, Cass Business School, City University of London.
Cole L and Gunther N (1998). Predicting bank failures: A comparison of on and off-site monitoring systems. Journal of Financial Services Research 13 (2): 103–117.
Davis EP and Karim D (2008). Comparing early warning systems for banking crisis. Journal of Financial Stability 4 (2): 89–120.
De Lisa R, Zedda S, Vallascas F, Campolongo F and Marchesi M (2011). Modelling deposit insurance scheme losses in a basel 2 framework. Journal of Financial Services Research 40 (3): 123–141.
Dowd K (2002). Measuring Market Risk. John Wiley and Sons: Chichester.
Embrechts P, Klupelberg C and Mikosch T (1997). Modelling Extremal Events for Insurance and Finance. Springer Verlag: Berlin.
Figini S and Giudici P (2013). Credit risk predictions with Bayesian model averaging. DEM Working Papers Series 034, University of Pavia, Department of Economics and Management.
Giudici P and Figini S (2009). Applied Data Mining for Business and Industry. Wiley: London.
Gomez-Gonzalez J and Kiefer NM (2009). Bank failure: Evidence from the Colombian financial crisis. The International Journal of Business and Finance Research 3 (3): 15–31.
Gonzalez-Hermosillo B (1999). Determinants of ex-ante banking system distress: A macro-micro empirical exploration of some recent episodes. IMF Working Paper no. 33.
Gup BE (1998). Bank Failures in the Major Trading Countries of the World: Causes and Estimation. Quorum Books: Westport, Connecticut.
Halaj G (2013). Optimal asset structure of a bank. Bank reactions to stressful market conditions. ECB Working Papers n. 1533.
Hand DJ (2009). Measuring classifier performance: A coherent alternative to the area under the ROC curve. Machine Learning 77: 103–123.
Hand DJ (2010). Evaluating diagnostic tests: The area under the ROC curve and the balance of errors. Statistics in Medicine 29 (14): 1502–1510.
Hand DJ, Mannila H and Smyth P (2001). Principles of Data Mining. MIT Press: Cambridge, MA.
Hanley JA and McNeil BJ (1983). A method of comparing the areas under receiver operating characteristic curves from the same cases. Radiology 148 (3): 839–843.
Huang X, Zhou H and Zhu H (2011). Systemic risk contribution. Technical Report, Board of Governors of the Federal reserve System.
Idier J, Lame’ G and Mesonnier JS (2013). How useful is the marginal expected shortfall for the measurement of systemic exposure? A practical assessment. Working Paper Series no. 1546, European Central Bank.
Kanno M (2013). Credit migration forecasting and correlation between business and credit cycles. Technical Report, Kanagawa University.
Kenny G, Kostka T and Masera F (2013). Can macroeconomist forecast risk? Event-based evidence from the Euro area. ECB Working Paper n. 1540.
King G and Zeng L (2001). Logistic regression in rare events data. Political Analysis 9 (2): 137–163.
Klomp J and de Haan J (2012). Banking risk and regulations: Does one size fit all? Journal of Banking and Finance 36 (12): 3197–3212.
Koopman SJ, Lucas A and Schwaab B (2012). Dynamic factor models with macro, frailty and industry effects for U.S. default counts: The credit crisis of 2008. Journal of Business and Economic Statistics 30: 521–532.
Mannasoo K and Mayes DG (2009). Explaining bank distress in Eastern European transition economies. Journal of Banking and Finance 33 (2): 244–253.
Mare DS (2012). Contribution of macroeconomic factors to the prediction of small bank failures. Technical Report, the University of Edinburgh.
Maurin L and Toivanen M (2012). Risk, capital buffer and bank lending a granular approach to the adjustment of euro area banks. European Central Bank, Working Paper Series no. 1499.
McCullagh P and Nelder J (1989). Generalised Linear Models. Chapman and Hall: New York.
Memmel C and Raupach P (2010). How do banks adjust their capital ratios? Journal of Financial Intermediation 19 (4): 509–528.
Merton RC (1974). On the pricing of corporate debt: The risk structure of interest rates. Journal of Finance 2 (2): 449–471.
Nocedal J and Wright SJ (2006). Numerical Optimization. Springer-Verlag: New York.
Resti A and Sironi A (2007). Risk Management and Shareholders’ Value in Banking. Wiley: New York.
Robin X et al (2011). pROC: An open-source package for R and S+to analyze and compare ROC curves. BMC Bioninformatics 12: 77–85.
Rose PS and Kolari JW (1985). Early warning systems as a monitoring device for bank condition. Quarterly Journal of Business and Economics 24 (1): 43–60.
Roth M (1994). Too big to fail and the instability of the banking system: Some insights from foreign countries. Business Economics 4 (4): 43–49.
Ruppert D, Wand MP and Carroll RJ (2003). Semiparametric Regression. Cambridge University Press: London.
Segoviano MA and Goodhart C (2009). Banking stability measures. IMF Working Paper.
Sinkey JF (1975). A multivariate statistical analysis for the characteristics of problem banks. Journal of Finance 1 (1): 21–36.
Tam KY and Kiang MY (1992). Managerial applications of neural networks: The case of bank failure predictions. Management Science 38 (7): 926–947.
Vasicek OA (1984). Credit valuation. KMV corporation, March: San Francisco.
Vazquez F and Federico P (2012). Bank funding structures and risk: Evidence from the global crisis. International Monetary Fund Working Papers no. 29.
Wagner W (2006). The liquidity of bank assets and banking stability. Journal of Banking & Finance 31 (1): 121–139.
Wang X and Dey DK (2010). Generalised extreme value regression for binary response data: An application to b2b electronic payments system adoption. The Annals of Applied Statistics 4 (4): 2000–2023.
Acknowledgements
The authors acknowledge useful comments and discussion at the Paris conference FEBS/LabEx ReFi 2013 and, subsequently, by the referees. The authors also acknowledge financial support from the MIUR PRIN project MISURA: Multivariate statistical models for risk assessment.
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Calabrese, R., Giudici, P. Estimating bank default with generalised extreme value regression models. J Oper Res Soc 66, 1783–1792 (2015). https://doi.org/10.1057/jors.2014.106
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DOI: https://doi.org/10.1057/jors.2014.106