Non-linear filtering applied to a new model with jumps in credit risk.

Lending money has been one of the basic activities of banks for centuries. However, credit evaluation and pricing of loans are still not well understood, since the assessment of the impact of credit risk on prices in bond markets, which is one of the most challenging types of financial risk, is in general difficult and subject to the complex interplay of factors as e.g. recovery risk and market risk. Roughly speaking, credit risk describes the exposure of losses due to changes of the vanity of borrowers as e.g. the issuer of a corporate bond. The severe global crisis of 2008, which was significantly caused by the sudden occurrence of illiquidity of credit markets, has shown the urgent need for a better understanding this sort of risk. In this thesis we will present a new quantitative model which is developed to control these kind of risk. We will estimate the parameters of the new model by using non-linear filtering techniques. Based on these estimations future stock prices will be computed