Wissen, was gut ist. Studieren in Chemnitz.

Projekte

  • Filtering techniques in the modeling, pricing and hedging of interest rate and credit risk
    In this project stochastic filtering methodology will be employed for solving pricing, hedging and calibration problems in interest rate and credit risk models. Stochastic filtering is concerned with the detection of signals from noisy observations. In interest rate and credit risk modeling, filtering problems arise naturally since important state variables such as firm values cannot be observed directly by investors. Existing filtering results are not yet sufficient for the application to complicated problems in model calibration and derivative pricing. The mathematical contribution of this project will therefore be the generalization of filtering results from the literature and the development of new numerical methods. On the financial side the project will contribute to a better understanding of dynamic credit risk models, including counterparty credit risk and credit contagion. Moreover, risk management techniques for derivatives such as dynamic hedging will be analyzed with the help of filtering. The practical relevance of these issues has been highlighted during the current financial crisis.
    Funded by DFG.
  • Portfolio Managemant on Gas Markets
    This project studies prices of contracts on gas traded at the European Energy Exchange. We aim at determining a statistical model which describes current and historic evolution. From this model we derive results regarding risk-management and optimal trading strategies.
  • Market Models for CDOs
    Collateralized debt obligations have proven to be a dangerous tool in the credit crisis. The most important reason for this is lack of data on the underlying. This project aims at a deeper understanding of the relationship of underlying credits and what can be learnt from data, if available. If no data is available CDOs are a very risky tool. On the other side, if the underlying credits are well studied and one has a good statistical model, a proper risk management is a difficult, but achievable taks.
  • General Models of Credit Risk
    Influenced by recent studies with shot-noise models and their generalizations we propose a general framework for pools of credit. This setting is studied in mathematical detail and consequences for applications are given.
  • Ratings
    We consider defaultable models under the additional information given by ratings. The proposed framework generalizes existing models available in the literature and studies the implications which can be drawn purely from the assumption about absence of arbitrage. A given example is based on a filtering approach with incomplete information.
    This is joint work with Jacek Jakubowski and Mariusz Nieweglowski.
  • Statistics of Financial Markets
    In particular the recent crisis shows the difficulty of current market practice: calibration. In this project we aim at finding a bridge between calibration of models to market data and historical data.