In this paper, a method is introduced for approximating the likelihood for the unknown
parameters of a state space model. The approximation converges to the true likelihood as …

This report reviews the structural approach for credit risk modelling, both considering the
case of a single firm and the case with default dependences between firms. In the single firm …

In state–space models, parameter learning is practically difficult and is still an open issue.
This paper proposes an efficient simulation-based parameter learning method. First, the …

This paper provides a review of the structural approach for modelling credit risk, both
considering the case of a single firm and the case with default dependences between firms …

The price disparity between the A-and H-share markets for dual-listed firms in China is one
of the most intriguing puzzles in the Mainland and Hong Kong financial markets. In this …

Discrete-time stochastic volatility (SV) models have generated a considerable literature in
financial econometrics. However, carrying out inference for these models is a difficult task …

Este artículo presenta los resultados del estudio sobre medición del riesgo de crédito en
firmas incluidas en el Índice General de la Bolsa de Valores de Colombia (IGBC) entre 2005 …

We develop a portfolio credit risk model that includes firm‐specific Markov‐switching
regimes as well as individual stochastic and endogenous recovery rates. Using weekly …

Abstract Sequential Monte Carlo (SMC) is a powerful technique originally developed for
particle filtering and Bayesian inference. As a generic optimizer for statistical and …

In this paper a Markov chain Monte Carlo (MCMC) technique is developed for the Bayesian
analysis of structural credit risk models with microstructure noises. The technique is based …