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Banking Crises, Early Warning Models, and Efficiency

In: Advances in Efficiency and Productivity

Author

Listed:
  • Pavlos Almanidis

    (Ernst & Young LLP)

  • Robin C. Sickles

    (Rice University)

Abstract
This paper proposes a general model that combines the Mixture Hazard Model with the Stochastic Frontier Model for the purposes of investigating the main determinants of the failures and performances of a panel of U.S. commercial banks during the financial crisis that began in 2007. The combined model provides measures of the probability and time to failure conditional on a bank’s performance and vice versa. Both continuous-time and discrete-time specifications of the model are considered in the paper. The estimation is carried out via the expectation-maximization algorithm due to incomplete information regarding the identity of at-risk banks. In- and out-of-sample predictive accuracy of the proposed models is investigated in order to assess their potential to serve as early warning tools.

Suggested Citation

  • Pavlos Almanidis & Robin C. Sickles, 2016. "Banking Crises, Early Warning Models, and Efficiency," International Series in Operations Research & Management Science, in: Juan Aparicio & C. A. Knox Lovell & Jesus T. Pastor (ed.), Advances in Efficiency and Productivity, chapter 0, pages 331-364, Springer.
    • Handle: RePEc:spr:isochp:978-3-319-48461-7_14
    DOI: 10.1007/978-3-319-48461-7_14
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    Cited by:

    1. Sanchez González, Jim & Restrepo-Tobón, Diego & Ramírez Hassan, Andrés, 2021. "Inefficiency and bank failure: A joint Bayesian estimation method of stochastic frontier and hazards models," Economic Modelling, Elsevier, vol. 95(C), pages 344-360.
    2. Evžen Kočenda & Ichiro Iwasaki, 2022. "Bank survival around the world: A meta‐analytic review," Journal of Economic Surveys, Wiley Blackwell, vol. 36(1), pages 108-156, February.
    3. Zhiyong Li & Chen Feng & Ying Tang, 2022. "Bank efficiency and failure prediction: a nonparametric and dynamic model based on data envelopment analysis," Annals of Operations Research, Springer, vol. 315(1), pages 279-315, August.

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    More about this item

    Keywords

    Financial distress; Panel data; Bank failures; Semiparametric mixture hazard model; Discrete-time mixture hazard model; Bank efficiency;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • 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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