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

Francisco Louzada, Fernando Moreira, Mauro Oliveira Jr

Research output: Contribution to journalArticlepeer-review

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 & Gage [8] 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.
Original languageEnglish
JournalCommunications in Statistics - Theory and Methods
Early online date30 Jun 2017
DOIs
Publication statusPublished - 12 Oct 2017

Keywords

  • non-default rate models
  • portfolios
  • survival
  • zero-inflated
  • Weibull

Fingerprint Dive into the research topics of 'A zero-inflated non-default rate regression model for credit scoring data'. Together they form a unique fingerprint.

Cite this