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Understanding and predicting bank rating transitions using optimal survival analysis models

Author

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  • Louis, Philippe
  • Van Laere, Elisabeth
  • Baesens, Bart

Abstract

In the aftermath of the financial crisis, this study investigates which underlying determinants cause bank rating transitions. We develop survival analysis models to explain credit transition hazards using macroeconomic factors and the rating history. We find that there exists a significant dependence of rating upgrade or rating downgrade transition hazards on rating-specific covariates and macro-economic covariates. Our results confirm the momentum effect, meaning that a financial institution that has been recently upgraded/downgraded has a higher chance of being upgraded/downgraded again. The predictive performance of the developed models turns out to be satisfactory.

Suggested Citation

  • Louis, Philippe & Van Laere, Elisabeth & Baesens, Bart, 2013. "Understanding and predicting bank rating transitions using optimal survival analysis models," Economics Letters, Elsevier, vol. 119(3), pages 280-283.
    • Handle: RePEc:eee:ecolet:v:119:y:2013:i:3:p:280-283
    DOI: 10.1016/j.econlet.2013.02.033
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    References listed on IDEAS

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    1. Frydman, Halina & Schuermann, Til, 2008. "Credit rating dynamics and Markov mixture models," Journal of Banking & Finance, Elsevier, vol. 32(6), pages 1062-1075, June.
    2. Figlewski, Stephen & Frydman, Halina & Liang, Weijian, 2012. "Modeling the effect of macroeconomic factors on corporate default and credit rating transitions," International Review of Economics & Finance, Elsevier, vol. 21(1), pages 87-105.
    3. Lando, David & Skodeberg, Torben M., 2002. "Analyzing rating transitions and rating drift with continuous observations," Journal of Banking & Finance, Elsevier, vol. 26(2-3), pages 423-444, March.
    4. Curry, Timothy J. & Fissel, Gary S. & Hanweck, Gerald A., 2008. "Is there cyclical bias in bank holding company risk ratings?," Journal of Banking & Finance, Elsevier, vol. 32(7), pages 1297-1309, July.
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    Cited by:

    1. Fenech, Jean Pierre & Yap, Ying Kai & Shafik, Salwa, 2016. "Modelling the recovery outcomes for defaulted loans: A survival analysis approach," Economics Letters, Elsevier, vol. 145(C), pages 79-82.
    2. GABAN Lucian & RUS IonuÈ› - Marius & FETITA Alin, 2017. "A Model Of Rating Of Eastern European Banks," Revista Economica, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 69(3), pages 42-56, August.

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    More about this item

    Keywords

    Rating transitions; Survival analysis; Rating-specific and macro-economic covariates; Prediction accuracy;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • 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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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