"I apply methods from complexity economics and post-Keynesian economics to better understand the effect of climate risk on the economy and the financial system in particular. I am further interested in the emergence of systemic risk and liquidity risk in the financial system through network dynamics. I have worked on the development of the European Central Bank’s ISA model for system-wide stress-testing, as well as the stock-flow consistent EIRIN model."

"In my PhD research, I explore tools for statistical inference that go beyond 'classical' methods. On the one hand, I am interested in the construction of powerful tests and confidence sets in irregular and non-parametric models. On the other hand, the main part of my research is concerned with new approaches to inference itself, such as anytime valid inference, e-values, post-hoc inference and game-theoretic approaches to testing."

"I do research in the computation of systemic risk measures, which quantifies how interconnected financial institutions contribute to overall system instability. My work focuses on developing and analyzing numerical algorithms—such as sample average approximation and optimization-based methods—to efficiently approximate these complex risk measures and establish their convergence properties. This research helps improve the understanding and management of contagion effects and joint distress within financial networks."

"My research focuses on stochastic optimal control and mean field game theory, with applications in quantitative finance - particularly in the context of portfolio insurance and systemic risk."

“My research develops methods for optimal policy learning under privacy and fairness constraints. My work focuses on designing econometric frameworks for selecting treatment allocation rules that are independent of sensitive individual characteristics, jointly optimizing the treatment rule and the privatization mechanism to maximize the decision-maker’s value.”

Former students

• Julian Amon - Scientometric Applications in Statistical Learning and Text Mining: Three Essays

• Robert Bajons (WU) - Statistical Learning for Sports Analytics: Advanced Methods for Player Evaluation in American and European Football

• Giacomo Bressan

• Andreas Celary (WU) - Linearized term structure models: Markov modulation and kernel techniques

• Camilla Damian (Vrije Universiteit Amsterdam) - Statistical Inference for Partial Information Models in Finance: Three Essays

• Eva Flonner - Neural SDEs for Model Calibration and Stochastic Filtering: A focus on Bayesian Methods and a Momentum and Mean Reversion Model

• Natalie Frantsits

• Jan Greve (University of Oslo) - Probability Distributions on Partitions of Data: Theory and Applications

• Niklas Hey (WU) - Solution concepts in convex vector optimization and the computation of Nash equilibria

• Rainer Hirk - Multivariate ordinal models in credit risk: Three essays

• Jana Hlavinová (WU) - Elicitability and identifiabity of set-valued functionals

• Darjus Hosszejni - Bayesian covariance matrix estimation via latent state space models

• Peter Knaus (WU) - Effective (dynamic) Shrinkage in Time-Varying Parameter Models

• Gabriela Kováĉova (UCLA) - The set-valued Bellman principle: Methodology, applications and computation

• Verena Köck - Deep neural network methods for partial integro-differential equations with applications in finance, insurance and economics

• Kevin Kurt - Markov-Modulated Affine Processes: Theory and Applications

• Lukas Sablica (WU) - Topics in Statistical Modeling