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Advances in Credit Risk Modelling and Corporate Bankruptcy Prediction

Editor

Listed:
  • Jones,Stewart
  • Hensher,David A.

Abstract

The field of credit risk and corporate bankruptcy prediction has gained considerable momentum following the collapse of many large corporations around the world, and more recently through the sub-prime scandal in the United States. This book provides a thorough compendium of the different modelling approaches available in the field, including several new techniques that extend the horizons of future research and practice. Topics covered include probit models (in particular bivariate probit modelling), advanced logistic regression models (in particular mixed logit, nested logit and latent class models), survival analysis models, non-parametric techniques (particularly neural networks and recursive partitioning models), structural models and reduced form (intensity) modelling. Models and techniques are illustrated with empirical examples and are accompanied by a careful explanation of model derivation issues. This practical and empirically-based approach makes the book an ideal resource for all those concerned with credit risk and corporate bankruptcy, including academics, practitioners and regulators.

Suggested Citation

  • Jones,Stewart & Hensher,David A. (ed.), 2008. "Advances in Credit Risk Modelling and Corporate Bankruptcy Prediction," Cambridge Books, Cambridge University Press, number 9780521689540.
  • Handle: RePEc:cup:cbooks:9780521689540
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    Cited by:

    1. Paweł Zając & Piotr Gurgul, 2012. "Forecasting of migration matrices in business demography," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 13(2), pages 387-404, June.
    2. repec:eee:spacre:v:15:y:2012:i:1:p:7-58 is not listed on IDEAS
    3. Jones, Stewart & Johnstone, David & Wilson, Roy, 2015. "An empirical evaluation of the performance of binary classifiers in the prediction of credit ratings changes," Journal of Banking & Finance, Elsevier, vol. 56(C), pages 72-85.
    4. repec:spr:reaccs:v:22:y:2017:i:3:d:10.1007_s11142-017-9407-1 is not listed on IDEAS
    5. Mehmet Karan & Aydın Ulucan & Mustafa Kaya, 2013. "Credit risk estimation using payment history data: a comparative study of Turkish retail stores," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 21(2), pages 479-494, March.

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