The Robustness of Estimators in Structural Credit Loss Distributions

This paper provides Monte Carlo results for the performance of Method of Moments (MM), Maximum Likelihood (ML) and Ordinary Least Square (OLS) estimators of the credit loss distribution implied by the Merton (1974)-Vasicek (1987, 2002) framework, when the common or the idiosyncratic asset return factor is non-Gaussian and thus the true credit loss distribution deviates from the theoretical one. We find that OLS and ML outperform MM in small samples when the true data generating process comprises a non-gaussian common factor. This result is intensified as the sample size increases and holds in all cases. On the other hand, we find that all the three estimators present a large bias and variance when the true data generating process comprises a non-gaussian idiosyncratic factor. This last result holds independently of the sample size, across different asset correlation levels and intensifies for positive shape parameter values.