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Pré-Publication, Document De Travail Année : 2022

Structured Dictionary Learning of Rating Migration Matrices for Credit Risk Modeling

Résumé

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 real data. The source code and the data are available at https://github.com/michael-allouche/ dictionary-learning-RMM.git for the sake of reproducibility of our research.
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Dates et versions

hal-03715954 , version 1 (07-07-2022)
hal-03715954 , version 2 (09-12-2023)

Identifiants

  • HAL Id : hal-03715954 , version 1

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Michaël Allouche, Emmanuel Gobet, Clara Lage, Edwin Mangin. Structured Dictionary Learning of Rating Migration Matrices for Credit Risk Modeling. 2022. ⟨hal-03715954v1⟩
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