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A zero-inflated non default rate regression model for credit scoring data

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  • Francisco Louzada
  • Fernando F. Moreira
  • Mauro Ribeiro de Oliveira

Abstract

The aim of this paper is to propose a survival credit risk model that jointly accommodates three types of time-to-default found in bank loan portfolios. It leads to a new framework that extends the standard cure rate model introduced by Berkson and Gage (1952) regarding the accommodation of zero-inflations. In other words, we propose a new survival model that takes into account three different types of individuals which have so far not been jointly accounted for: (i) an individual with an event at the starting time (zero time); (ii) non susceptible for the event, or (iii) susceptible for the event. Considering this, the zero-inflated Weibull non default rate regression models, which include a multinomial logistic link for the three classes, are presented using an application for credit scoring data. The parameter estimation is reached by the maximum-likelihood estimation procedure and Monte Carlo simulations are carried out to assess its finite sample performance.

Suggested Citation

  • Francisco Louzada & Fernando F. Moreira & Mauro Ribeiro de Oliveira, 2018. "A zero-inflated non default rate regression model for credit scoring data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(12), pages 3002-3021, June.
    • Handle: RePEc:taf:lstaxx:v:47:y:2018:i:12:p:3002-3021
    DOI: 10.1080/03610926.2017.1346803
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    Cited by:

    1. Yang, Qi & He, Haijin & Lu, Bin & Song, Xinyuan, 2022. "Mixture additive hazards cure model with latent variables: Application to corporate default data," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).

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