  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.
