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 between investment grade, subinvestment grade, and default ratings for U.S. corporates. The model strongly suggests the presence of a common dynamic component that can be interpreted as the credit cycle. We also show that the impact of this credit cycle is asymmetric with respect to downgrade and upgrade probabilities.

Multiperiod corporate default prediction—A forward intensity approach

“…One natural speculation is that our model has missed out some variables that are capable of reflecting long-term credit risk. A potential quick fix is to introduce the frailty effect as suggested in the previous literature such as Koopman et al (2008) and Duffie et al (2009) or to employ the regime-switching approach as in Chuang and Kuan (2010). Koopman et al (2009a,b) studied the relation between macroeconomic fundamentals and cycles in defaults and rating activities.…”

Inference for a Nonstationary Self-Exciting Point Process with an Application in Ultra-High Frequency Financial Data Modeling

“…Since being proposed by Hawkes (1971), the SEPP and its extensions have found applications in a wide ranges of areas, such as earthquake occurrence modeling (Ogata 1988) and prediction (Vere-Jones 1995), neuron firing process modeling (Chornoboy et al 1988), triggered optical emission modeling (Teich and Saleh 2000), financial securities trading and quote arrival time modeling (Engle and Lunde 2003), credit rating transition modeling (Koopman et al 2008), general ultra-high frequency financial data modeling (Monteiro 2009), and social network interaction modeling (Crane and Sornette 2008).…”

A likelihood ratio test for stationarity of rating transitions

“…Systematic economic activity constitutes a documented covariate for rating transitions (Koopman et al, 2008). Systematic risk could lead to higher downgrade intensities during a recession, compared to upswings.…”