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Structured dictionary learning of rating migration matrices for credit risk modeling

Author

Listed:
  • Michaël Allouche

    (CNRS, Ecole Polytechnique, Institut Polytechnique de Paris)

  • Emmanuel Gobet

    (CNRS, Ecole Polytechnique, Institut Polytechnique de Paris)

  • Clara Lage

    (CNRS, Ecole Polytechnique, Institut Polytechnique de Paris)

  • Edwin Mangin

    (BNP Paribas)

Abstract

Rating migration matrix is a crux to assess credit risks. Modeling and predicting these matrices are then an issue of great importance for risk managers in any financial institution. As a challenger to usual parametric modeling approaches, we propose a new structured dictionary learning model with auto-regressive regularization that is able to meet key expectations and constraints: small amount of data, fast evolution in time of these matrices, economic interpretability of the calibrated model. To show the model applicability, we present a numerical test with both synthetic and real data and a comparison study with the widely used parametric Gaussian Copula model: it turns out that our new approach based on dictionary learning significantly outperforms the Gaussian Copula model.

Suggested Citation

  • Michaël Allouche & Emmanuel Gobet & Clara Lage & Edwin Mangin, 2024. "Structured dictionary learning of rating migration matrices for credit risk modeling," Computational Statistics, Springer, vol. 39(6), pages 3431-3456, September.
    • Handle: RePEc:spr:compst:v:39:y:2024:i:6:d:10.1007_s00180-023-01449-y
    DOI: 10.1007/s00180-023-01449-y
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    References listed on IDEAS

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    1. Gondzio, Jacek, 2012. "Interior point methods 25 years later," European Journal of Operational Research, Elsevier, vol. 218(3), pages 587-601.
    2. Florian Bourgey & Emmanuel Gobet & Clément Rey, 2020. "Meta-model of a large credit risk portfolio in the Gaussian copula model," Post-Print hal-02291548, HAL.
    3. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
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