Default Probability Estimation in Small Samples - With an Application to Sovereign Bonds

In small samples and especially in the case of small true default probabilities, standard approaches to credit default probability estimation have certain drawbacks. Most importantly, standard estimators tend to underestimate the true default probability which is of course an undesirable property from the perspective of prudent risk management. As an alternative, we present an empirical Bayes approach to default probability estimation and apply the estimator to a comprehensive sample of Standard & Poor's rated sovereign bonds. We further investigate the properties of a standard estimator and the empirical Bayes estimator by means of a simulation study. We show that the empirical Bayes estimator is more conservative and more precise under realistic data generating processes