In this paper, we build a bridge between different reduced-form approaches to pricing defaultable claims. In particular, we showhow the well known formulas by Duffie et al. [12] and by Elliott et al.[14] are related. Moreover, in the spirit of Collin Dufresne et al. [8], wepropose a simple...

We consider the modelling of credit migration risk and the pricing of migration derivativesour approach enlarges the traditional setup where credit risk is based on default solely.We implement the Regime Shifting Markov Mixture model developed in Andersson (2007)and Andersson and Vanini (2008)...

We consider the modelling of credit migration risk and the pricing of migrationderivatives. To construct a Point-in-Time (PIT) rating migration matrix as the underlyingvalue for derivative pricing we show first that the Affine Markov Chain models isnot sufficient to generate PIT migration...

In classical contagion models, default systems are Markovian conditionally on the observation of their stochastic environment, with interacting intensities. This necessitates that the environment evolves autonomously and is not influenced by the history of the default events. We extend the...

In classical contagion models, default systems are Markovian conditionally on the observation of their stochastic environment, with interacting intensities. This necessitates that the environment evolves autonomously and is not influenced by the history of the default events. We extend the...

This paper develops a default-risky bond pricing model, which assumes that the default intensity is driven by a Markov chain and which accounts for default and liquidity risk. A representation of the bond price dynamics, which separates three different types of risk, was obtained. Introducing...

This paper studies in some examples the role of information in a default-risk framework. In a first-passage model, we assume that investors obtain two types of information about the firm’s unlevered asset value at a discrete sequence of dates. The effects of information on the distributional...