Dependent default and recovery: MCMC study of downturn LGD credit risk model

TL;DR

This paper develops a Bayesian MCMC method to jointly estimate a dependent default and recovery LGD model, highlighting the importance of parameter uncertainty in credit risk capital estimation during downturns.

Contribution

It introduces a Bayesian inference approach with MCMC for estimating dependent default and recovery models, accounting for parameter and systematic risk uncertainties.

Findings

Parameter uncertainty significantly impacts economic capital estimates.

Dependent default and recovery models better capture downturn risk.

Bayesian MCMC provides comprehensive joint parameter estimation.

Abstract

There is empirical evidence that recovery rates tend to go down just when the number of defaults goes up in economic downturns. This has to be taken into account in estimation of the capital against credit risk required by Basel II to cover losses during the adverse economic downturns; the so-called "downturn LGD" requirement. This paper presents estimation of the LGD credit risk model with default and recovery dependent via the latent systematic risk factor using Bayesian inference approach and Markov chain Monte Carlo method. This approach allows joint estimation of all model parameters and latent systematic factor, and all relevant uncertainties. Results using Moody's annual default and recovery rates for corporate bonds for the period 1982-2010 show that the impact of parameter uncertainty on economic capital can be very significant and should be assessed by practitioners.