Exposición al Default: Estimación para un Portafolio de Tarjeta de Crédito

Carlos Bambino-Contreras, Víctor Morales-Oñate

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

Resumen

This work estimates the exposure at default without using the credit conversion factor, a common mechanism used in the expected loss estimation literature and suggested by the Basel Committee. To achieve this objective, the probability distribution of this variable (exposure at default) has been identified, which is subsequently estimated in parts (EAD = 0 and EAD > 0) using generalized linear models (logit and GLM-Gamma). The results obtained are competitive with those found in the literature. This shows that the simultaneous estimation of parameters, as well as the separate estimation, give promising results. Additionally, the EAD > 0 case is contrasted with a MARS model whose performance is superior to GLM-Gamma. These models were applied to a data set of a credit card portfolio of a financial institution in Ecuador.

Título traducido de la contribuciónExposure to Default: Estimation for a Credit Card Portfolio
Idioma originalEspañol
Páginas (desde-hasta)71-82
Número de páginas12
PublicaciónRevista Politecnica
Volumen50
N.º2
DOI
EstadoPublicada - nov. 2022
Publicado de forma externa

Palabras clave

  • Credit risk
  • Expected loss
  • Exposure at default
  • Gamma Distribution
  • Generalized linear models
  • Machine Learning

Huella

Profundice en los temas de investigación de 'Exposición al Default: Estimación para un Portafolio de Tarjeta de Crédito'. En conjunto forman una huella única.

Citar esto