Inference of the structural credit risk model using MLE

Credit risk analysis is not only an important research topic in finance, but also of interest in everyday life. Unfortunately, the non-linear nature of the widely accepted Black-Scholes option price model, which sits at the very heart of the structural credit risk model, causes great difficulty when inferring the latent asset value sequence from observed data. The main contribution of this paper is to address this problem by pursuing maximum likelihood state estimation (MLE) instead of the usual particle filtering approach. Experiments demonstrate the competitiveness of the proposed MLE approach: it achieves a much lower inference error and a much lower running time than particle filtering methods. This work has merit for the general problem of inferring latent values for probabilistic time-series.