Rating transitions forecasting: a filtering approach

Areski Cousin (1) , Jérôme Lelong (2) , Ragnar Norberg (3) , Tom Picard (2)

1 IRMA - Institut de Recherche Mathématique Avancée
2 DAO - Données, Apprentissage et Optimisation
3 ISFA - Institut de Science Financière et d'Assurances

Analyzing the effect of business cycle on rating transitions has been a subject of great interest these last fifteen years, particularly due to the increasing pressure coming from regulators for stress testing. In this paper, we consider that the dynamics of rating migrations is governed by an unobserved latent factor. Under a point process filtering framework, we explain how the current state of the hidden factor can be efficiently inferred from observations of rating histories. We then adapt the classical Baum-Welsh algorithm to our setting and show how to estimate the latent factor parameters. Once calibrated, we may reveal and detect economic changes affecting the dynamics of rating migration, in real-time. To this end we adapt a filtering formula which can then be used for predicting future transition probabilities according to economic regimes without using any external covariates. We propose two filtering frameworks: a discrete and a continuous version. We demonstrate and compare the efficiency of both approaches on fictive data and on a corporate credit rating database. The methods could also be applied to retail credit loans.

Domaines: Machine Learning [stat.ML]; Gestion des risques [q-fin.RM]; Probabilités [math.PR]

Areski Cousin, Jérôme Lelong, Ragnar Norberg, Tom Picard. Rating transitions forecasting: a filtering approach. 2021. ⟨hal-03347521v1⟩