Structured dictionary learning of rating migration matrices for credit risk modeling

Michaël Allouche (), Emmanuel Gobet (), Clara Lage () and Edwin Mangin ()
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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

Computational Statistics, 2024, vol. 39, issue 6, No 22, 3456 pages

Abstract: 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.

Keywords: Rating migration matrix; Dictionary learning; Auto-regressive modeling; Interpretability; Gaussian Copula model (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00180-023-01449-y

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