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To Be Bailed Out or To Be Left to Fail? A Dynamic Competing Risks Hazard Analysis

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  • Nikolaos I. Papanikolaou

    (Bournemouth University)

Abstract

During the late 2000s financial crisis, a large number of banks either failed or received financial aid thus inflicting substantial losses on the system. We contribute to the early warning literature by developing a dynamic competing risks hazard model that explores the joint determination of the probability of a distressed bank to face a licence withdrawal or to be bailed out. The underlying patterns of distress are analysed based on a broad range of bank-level and environmental factors. We find that institutions with inadequate capital, illiquid and risky assets, poor management, low levels of earnings and high sensitivity to market conditions have a higher probability to go bankrupt. Bailed out banks, on the other hand, face both capital and liquidity shortages, experience low earnings, and are highly exposed to market products; however, neither managerial expertise, nor the quality of assets are relevant to the odds of bailout. We further document that large and complex banks are less likely to fail and more likely to be bailed out and that authorities are more prone to provide support to a distressed bank, which is well-connected with politicians and political parties and less prone to let it go bankrupt. Importantly, our model outperforms the commonly used logit model in terms of forecasting accuracy in all the in- and out-of-sample tests we conduct.

Suggested Citation

  • Nikolaos I. Papanikolaou, 2017. "To Be Bailed Out or To Be Left to Fail? A Dynamic Competing Risks Hazard Analysis," BAFES Working Papers BAFES12, Department of Accounting, Finance & Economic, Bournemouth University.
  • Handle: RePEc:bam:wpaper:bafes12
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    2. Ellis, Scott & Sharma, Satish & Brzeszczyński, Janusz, 2022. "Systemic risk measures and regulatory challenges," Journal of Financial Stability, Elsevier, vol. 61(C).
    3. Manthoulis, Georgios & Doumpos, Michalis & Zopounidis, Constantin & Galariotis, Emilios, 2020. "An ordinal classification framework for bank failure prediction: Methodology and empirical evidence for US banks," European Journal of Operational Research, Elsevier, vol. 282(2), pages 786-801.
    4. Ioannis Malandrakis & Konstantinos Drakos, 2025. "Green banks versus non‐green banks: A financial stability comparative analysis in terms of CAMEL ratios," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 30(3), pages 2536-2573, July.
    5. Guo, Lin & Prezas, Alexandros P., 2019. "Market monitoring and influence: evidence from deposit pricing and liability composition from 1986 to 2013," Journal of Financial Stability, Elsevier, vol. 43(C), pages 146-166.
    6. Cutura, Jannic Alexander, 2018. "Debt holder monitoring and implicit guarantees: Did the BRRD improve market discipline?," SAFE Working Paper Series 232, Leibniz Institute for Financial Research SAFE.
    7. Carbó-Valverde, Santiago & Cuadros-Solas, Pedro J. & Rodríguez-Fernández, Francisco, 2020. "Do bank bailouts have an impact on the underwriting business?," Journal of Financial Stability, Elsevier, vol. 49(C).

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    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D02 - Microeconomics - - General - - - Institutions: Design, Formation, Operations, and Impact
    • G01 - Financial Economics - - General - - - Financial Crises
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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