The multi-state latent factor intensity model for credit rating transitions

Abstract

A new empirical reduced-form model for credit rating transitions is introduced. It is a parametric intensity-based duration model with multiple states and driven by exogenous covariates and latent dynamic factors. The model has a generalized semi-Markov structure designed to accommodate many of the stylized facts of credit rating migrations. Parameter estimation is based on Monte Carlo maximum likelihood methods for which the details are discussed in this paper. A simulation experiment is carried out to show the effectiveness of the estimation procedure. An empirical application is presented for transitions in a 7 grade rating system. The model includes a common dynamic component that can be interpreted as the credit cycle. Asymmetric effects of this cycle across rating grades and additional semi-Markov dynamics are found to be statistically significant. Finally, we investigate whether the common factor model suffices to capture systematic risk in rating transition data by introducing multiple factors in the model. r

Observation Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk

“…Previous research on credit risk has focused on a standard multinomial specification; see, for example, Koopman, Lucas, and Monteiro (2008) and Koopman, Kraeussl, Lucas, and Monteiro (2009). The multinomial density does not take into account the fact that ratings are ordered.…”

“…The model can therefore be used to stress test current credit portfolios and determine adequate capital buffers using the high percentiles of the simulated portfolio loss distributions. Our modeling framework provides a relatively simple observation driven alternative to the (parameter driven) frailty models of McNeil and Wendin (2007), Koopman, Lucas, and Monteiro (2008), and Duffie, Eckner, Horel, and Saita (2009).…”

Credit Default and Business Cycles: An Empirical Investigation of Brazilian Retail Loans

“…These two papers use GDP growth to classify the different phases of the business cycle and compute separate default and rating transition probabilities for each of these regimes. Papers that use time series techniques include Koopman and Lucas (2005) and Koopman et al (2005b). They use a multivariate unobserved components framework to study cyclical co-movements between GDP and business failures.…”

Joint Bayesian Analysis of Parameters and States in Nonlinear, Non-Gaussian State Space Models

“…Koopman et al (2008) and Duffie et al (2009) consider the stochastic intensity model for studying the systematic dynamics of U.S. corporate defaults and credit rating migrations.…”

“…Firm k only contributes to the likelihood function when it is at risk of defaulting, that is when R ki = R k (t i ) = 1. The state equation remains as in Section 3.4, for further details, see Koopman et al (2008).…”