Particle MCMC in forecasting frailty-correlated default models with expert opinion

Ha Nguyen*

*Corresponding author for this work

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Abstract

Predicting corporate default risk has long been a crucial topic in the finance field, as bankruptcies impose enormous costs on market participants as well as the economy as a whole. This paper aims to forecast frailty-correlated default models with subjective judgements on a sample of U.S. public non-financial firms spanning January 1980–June 2019. We consider a reduced-form model and adopt a Bayesian approach coupled with the Particle Markov Chain Monte Carlo (Particle MCMC) algorithm to scrutinize this problem. The findings show that the 1-year prediction for frailty-correlated default models with different prior distributions is relatively good, whereas the prediction accuracy ratios for frailty-correlated default models with non-informative and subjective prior distributions over various prediction horizons are not significantly different.

Original languageEnglish
Article number334
Pages (from-to)1-16
Number of pages16
JournalJournal of Risk and Financial Management
Volume16
Issue number7
DOIs
Publication statusPublished - Jul 2023

Bibliographical note

Copyright © 2023 by the author. Licensee MDPI, Basel, Switzerland. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • default risk
  • doubly stochastic
  • expert opinion
  • frailty
  • hidden factors
  • particle independent metropolis–hastings
  • Particle Markov Chain Monte Carlo

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