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Incorporating Risk Aversion and Model Misspecification into a Hybrid Model of Default

Sebastian Jaimungal
University of Toronto - Department of Statistics

Georg Sigloch
University of Toronto - Department of Mathematics


August 12, 2009


Abstract:     
It is well known that purely structural models of default cannot explain short term credit spreads, while purely intensity based models of default lead to completely unpredictable default events. Here we introduce a hybrid model of default in which a firm enters distress upon a non-tradable credit worthiness index (CWI) hitting a critical level. Upon distress, the firm defaults at the next arrival of a Poisson process. To value defaultable bonds and CDSs we introduce the concept of robust indifference pricing which differs from the usual indifference valuation paradigm by the inclusion of model uncertainty. To account for model uncertainty, the embedded optimization problems are modified to include a minimization over a set of candidate measures equivalent to the estimated reference measure. With this new model and pricing paradigm, we succeed in determining corporate bond spreads and CDS spreads and find that model uncertainty plays a similar, but distinct, role to risk aversion. In particular, model uncertainty allows for significant short term spreads.

Keywords: Strucutural Models, Intensity Models, Stochastic Optimal Control, Robust Optimization, Indifference Valuation, Defaultable Bonds, Credit Default Swap

Working Paper Series

Date posted: February 25, 2009 ; Last revised: August 14, 2009

Suggested Citation

Jaimungal, Sebastian and Sigloch, Georg, Incorporating Risk Aversion and Model Misspecification into a Hybrid Model of Default (August 12, 2009). Available at SSRN: http://ssrn.com/abstract=1348599


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Contact Information

Sebastian Jaimungal (Contact Author)
University of Toronto - Department of Statistics ( email )
Toronto, Ontario M5S 3G3 Canada
Georg Sigloch
University of Toronto - Department of Mathematics ( email )
Toronto, Ontario M5S 3G3 Canada
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