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Credit Risk under Incomplete Information and Nonlinear Filtering

Subject Area Mathematics
Term from 2007 to 2011
Project identifier Deutsche Forschungsgemeinschaft (DFG) - Project number 59144157
 
The project is concerned with the development of realistic and tractable dynamic credit risk models for the valuation and the hedging of credit derivatives such as corporate bonds or related portfolio products. A new information-based approach mimicking the informational advantages of institutional investors is proposed. Under this approach, this informational advantage is reflected in prices of traded credit derivatives; secondary-market investors will therefore try to back out this information from observed prices. Our project will study the optimal solution of this problem, using advanced methods from stochastic calculus and in particular from nonlinear filtering theory. Moreover, we plan to analyze the ensuing dynamics of prices of traded securities and credit spreads. Further research goals include calibration and testing of the model using market data; the development of suitable hedging strategies under incomplete information; the extension of our approach to equity-derivatives markets; and finally the analysis of portfolio-optimization problems in our context, with particular focus on incomplete information.
DFG Programme Research Grants
International Connection Italy, USA
 
 

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