We work at the interface of

• mathematical finance
• risk management
• stochastics
  
• statistics
  
• machine learning / data science.
  

Many of the mathematical and stochastic problems that we are dealing with arise from questions in finance and economics with a focus on risk assessment. To exploit upside risks on the one hand and reduce downside risks on the other hand, mathematical models which are as close as possible to reality should be chosen. Nowadays this has become a feasible task as new machine learning approaches have opened the door to more data-driven and thus more robust model selection mechanisms, enabling realistic data driven risk inference.

Concrete topics that we are working on include

• universal structures in mathematical finance (START project);
  
• dynamic uncertainty modeling of financial markets (FWF project);
  
• affine and polynomial processes;
  
• high dimensional phenomena in finance and risk management (multivariate stochastic volatility, term structure modeling, stochastic portfolio theory, contagion and system risk modeling);
• theory and applications of machine learning, especially signature methods, deep neural networks and reservoir computing.

 RESEARCHERS

Faculty Members:

Christa Cuchiero

Irene Klein

Paul Hager

Project Staff:

Tomás Carrondo

Anna Paula Kwossek

Julian Pachschwöll