Chair in Quantitative Risk Analysis
We develop mathematical methods for understanding, measuring, and managing risk under uncertainty.
Our research lies at the interface of stochastics, financial and insurance mathematics, quantitative risk management, and machine learning. We are particularly interested in mathematical models that remain meaningful when data are incomplete, models are misspecified, or risks are complex and evolving.
A central focus of our work is the development of rigorous and practically relevant methods for nonlinear pricing, model risk, and risk measurement in finance and insurance. We combine tools from probability theory, stochastic analysis, optimization, statistics, and machine learning to study questions arising from financial markets, insurance portfolios, and emerging forms of systemic and non-standardized risk.
Main research directions
- Financial and insurance mathematics
- Nonlinear pricing and risk measures
- Model uncertainty and robust valuation
- Stochastic processes and stochastic analysis
- Machine learning methods for risk analysis
- Emerging risks, systemic risk, and non-standardized risk
For students
The chair offers supervision of Master’s theses in mathematical finance, insurance mathematics, and quantitative risk analysis. Students interested in a thesis project should have a solid background in probability theory and stochastic processes, and ideally have followed courses in mathematical finance (FIM 102 and FIM 202).
Students interested in the Minor in Financial Mathematics may find relevant information here.
PhD candidates work on research projects in financial and insurance mathematics and are associated with the Zurich Graduate School in Mathematics. Prospective students should first consult the homepage of the Zurich Graduate School in Mathematics for general information. The standard approach is to submit an application there, respecting the deadlines.