Credit risk and incomplete information: a filtering framework for pricing and risk-management

We propose a reduced-form credit risk model where default intensities, interest rates and risk premia are determined by a not fully observable factor process with affine dynamics. The inclusion of latent factors enriches the model flexibility and induces an information-driven contagion effect among defaults of different issuers. The information on the unobserved factors is dynamically updated via stochastic filtering, on the basis of market data as well as rating scores. This allows for a continuous tuning of the model to the actual (latent) situation of the economy and provides a coherent and unified approach to pricing and risk management.