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Enhanced credit default models for heterogeneous SME segments

Author

Listed:
  • Fantazzini, Dean

    () (Moscow School of Economics- Moscow State University (Russia))

  • DeGiuli, Maria Elena

    (Faculty of Economics, University of Pavia (Italy))

  • Figini, Silvia

    (Department of Statistics and Applied Economics, University of Pavia, (Italy))

  • Giudici, Paolo

    (Department of Statistics and Applied Economics, University of Pavia, (Italy))

Abstract

Considering the attention placed on SMEs in the new Basel Capital Accord, we propose a set of Bayesian and classical longitudinal models to predict SME default probability, taking unobservable firm and business sector heterogeneities as well as analysts’ recommendations into account. We compare this set of models in terms of forecasting performances, both in-sample and out-of-sample. Furthermore, we propose a novel financial loss function to measure the costs of an incorrect classification, including both the missed profits and the losses given defaults sustained by the bank. As for the in-sample results, we found evidence that our proposed longitudinal models outperformed a simple pooled logit model. Besides, Bayesian models performed even better than classical models. As for the out-of-sample performances, the models were much closer, both in terms of key performance indicators and financial loss functions, and the pooled logit model could not be outperformed

Suggested Citation

  • Fantazzini, Dean & DeGiuli, Maria Elena & Figini, Silvia & Giudici, Paolo, 2009. "Enhanced credit default models for heterogeneous SME segments," Journal of Financial Transformation, Capco Institute, vol. 25, pages 31-39.
  • Handle: RePEc:ris:jofitr:0027
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    References listed on IDEAS

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    Cited by:

    1. Andreeva, Galina & Calabrese, Raffaella & Osmetti, Silvia Angela, 2016. "A comparative analysis of the UK and Italian small businesses using Generalised Extreme Value models," European Journal of Operational Research, Elsevier, vol. 249(2), pages 506-516.
    2. Silvia Figini & Roberto Savona & Marika Vezzoli, 2016. "Corporate Default Prediction Model Averaging: A Normative Linear Pooling Approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(1-2), pages 6-20, January.
    3. Dean Fantazzini & Mario Maggi, 2014. "Proposed Coal Power Plants and Coal-To-Liquids Plants: Which Ones Survive and Why?," DEM Working Papers Series 082, University of Pavia, Department of Economics and Management.

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    More about this item

    Keywords

    Longitudinal models; Bayesian panel models; Credit risk; Default probability; Loss function;

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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