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Estimating bank default with generalised extreme value models

Author

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  • Raffaella Calabrese

    (Department of Quantitative Methods for Economics and Business Sciences, University of Milano-Bicocca)

  • Paolo Giudici

    (Department of Economics and Management, University of Pavia)

Abstract

This paper considers the joint role of macroeconomic and bankspecific factors in explaining the occurrence of bank failures. As bank failures are, fortunately, rare, we apply a regression model, based on extreme value theory, that turns out to be more effective than classical logistic regression models. The application of this model to the occurrence of bank defaults in Italy shows that, while capital ratios considered by the regulatory requirements of Basel III are extremely significant to explain proper failures, macroeconomic conditions are relevant only when failures are defined also in terms of merger and acquisition. We also apply the joint beta regression model, in order to estimate the factors that most contribute to the bank capital ratios monitored by Basel III. Our results show that the Tier 1 capital ratio and the Total capital ratio are affected by similar variables, at the micro and macroeconomic level. An important outcome of this part of the analysis is that capital ratio variables can be taken as reasonable proxies of distress, at least as far as the effect sign of the determinants of failure risk is being considered.

Suggested Citation

  • Raffaella Calabrese & Paolo Giudici, 2013. "Estimating bank default with generalised extreme value models," DEM Working Papers Series 035, University of Pavia, Department of Economics and Management.
    • Handle: RePEc:pav:demwpp:035
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    References listed on IDEAS

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    1. Adam B. Ashcraft, 2008. "Are Bank Holding Companies a Source of Strength to Their Banking Subsidiaries?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(2-3), pages 273-294, March.
    2. Raffaella Calabrese, 2014. "Predicting bank loan recovery rates with a mixed continuous‐discrete model," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 30(2), pages 99-114, March.
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    4. Paola Bongini & Stijn Claessens & Giovanni Ferri, 2001. "The Political Economy of Distress in East Asian Financial Institutions," Journal of Financial Services Research, Springer;Western Finance Association, vol. 19(1), pages 5-25, February.
    5. Arena, Marco, 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 & Finance, Elsevier, vol. 32(2), pages 299-310, February.
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    Cited by:

    1. D. Bidzhoyan S. & Д. Биджоян С., 2018. "Модель Оценки Вероятности Отзыва Лицензии У Российского Банка // Model For Assessing The Probability Of Revocation Of A License From The Russian Bank," Финансы: теория и практика/Finance: Theory and Practice // Finance: Theory and Practice, ФГОБУВО Финансовый университет при Правительстве Российской Федерации // Financial University under The Government of Russian Federation, vol. 22(2), pages 26-37.
    2. Raffaella Calabrese & Johan A. Elkink & Paolo S. Giudici, 2017. "Measuring bank contagion in Europe using binary spatial regression models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(12), pages 1503-1511, December.
    3. Prosper Senyo Koto, 2017. "Is Social Capital Important In Formal-Informal Sector Linkages?," Journal of Developmental Entrepreneurship (JDE), World Scientific Publishing Co. Pte. Ltd., vol. 22(02), pages 1-16, June.
    4. Raffaella Calabrese & Silvia Osmetti, 2014. "Modelling cross-border systemic risk in the European banking sector: a copula approach," Papers 1411.1348, arXiv.org.

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