In this talk we consider a reduced-form credit risk model where default intensities, interest rates and risk premia are determined by a not fully observable stochastic factor process with affine dynamics. The inclusion of latent factors enriches the model flexibility and, besides capturing omitted variables and truly unmeasurable effects, induces an information-driven default contagion effect. We show how, by relying on stochastic filtering techniques, the information on the unobserved factors can be dinamically updated, on the basis of financial data as well as rating scores. This allows for a continuous tuning of the model to the actual (latent) situation of the economy. By combining market-based with rating-based data, the model can capture both forward-looking and backward-looking sources of information and provides a coherent unified approach to pricing and risk management. Finally, we will also show how the model can be extended in order to jointly model credit and equity risk.
Fontana, C. (2010), Credit Risk and Incomplete Information: a Filtering Framework for Pricing and Risk Management, to appear in: Mathematical and Statistical Methods for Actuarial Sciences and Finance, Springer
Fontana, C. and Runggaldier W.J. (2010), Credit Risk and Incomplete Information: Filtering and EM Parameter Estimation, International Journal of Theoretical and Applied Finance, 13(5): 683-715.
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