We use Markov chain Monte Carlo (MCMC) sampling to draw model coefficients to generate LGD distributions. We find that applying this Bayesian method on a sophisticated model that accounts for the bi-modal distribution of the LGDs, such as the zero-one-inflated beta (ZOIB) model, can generate LGD...

We examine the impact of the unobservable systematic risk factor on default prediction model performance. We find that including the unobservable systematic risk factor might help improve predictive accuracy, but it might not help improve rank ordering of firms by default risk. Rank ordering is...

We apply multiple machine learning (ML) methods to model loss given default (LGD) for corporate debt using a common dataset that is cross-sectional but collected over different time periods and shows much variation over time. We investigate the efficacy of three cross-validation (CV) schemes for...