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 language | English |
|---|---|
| Article number | 334 |
| Pages (from-to) | 1-16 |
| Number of pages | 16 |
| Journal | Journal of Risk and Financial Management |
| Volume | 16 |
| Issue number | 7 |
| DOIs | |
| Publication status | Published - 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