dictionary-learning-RMM
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 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. 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.
