Particle MCMC Forecasting of Frailty-Driven Corporate Defaults

Summary

This study forecasts corporate defaults using a reduced-form model in which firms’ default intensities share dependence on an unobserved frailty factor. It combines Bayesian estimation with Particle Markov Chain Monte Carlo, and incorporates expert judgments through subjective prior distributions. The analysis uses U.S. publicly listed non-financial firms observed from January 1980 through June 2019.

The reported results associate greater volatility and mean reversion in the hidden factor with higher firm default intensities. One-year forecasts are described as relatively good under different prior choices. Across several forecast horizons, however, prediction accuracy ratios for models using non-informative and subjective priors are not significantly different. The summary provides no specific accuracy measures or details about the expert elicitation process, so it does not establish how the model compares with alternative forecasting approaches or how well it transfers beyond the sampled firms and period.

Key ideas

• The model represents dependence among firms’ default intensities through a hidden frailty factor.
• Bayesian estimation and Particle MCMC are used to forecast correlated defaults.
• Expert judgments enter through subjective prior distributions.
• The study links hidden-factor volatility and mean reversion positively to default intensities.
• Reported one-year forecasts are relatively good, while prior choice does not significantly separate accuracy ratios across the tested horizons.

Tags

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


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









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 volatility and the mean reversion of the hidden factor, which determine the dependence of the unobserved default intensities on the latent variable, have a highly economically and statistically significant positive impact on the default intensities of the firms. The results also indicate 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.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.