Markov chain Monte Carlo estimation of default and recovery: dependent via the latent systematic factor

Xiaolin Luo and Pavel V. Shevchenko

Abstract

ABSTRACT

It is a well-known fact that recovery rates tend to decrease when the number of defaults increases during economic downturns. We demonstrate how the loss given default model with the default and recovery dependent via the latent systematic risk factor can be estimated using Bayesian inference methodology and the Markov chain Monte Carlo method. This approach is very convenient for joint estimation of all model parameters and latent systematic factors. Moreover, all relevant uncertainties are easily quantified. Typically available data is the annual averages of defaults and recoveries and thus the data sets are small and parameter uncertainty is significant. In this case, the Bayesian approach is superior to the maximum likelihood method, which relies on a large-sample limit Gaussian approximation for the parameter uncertainty. As an example, we consider a homogeneous portfolio with one latent factor. However, the approach can be easily extended to deal with nonhomogenous portfolios and several latent factors.