Scholar
Christian Bayer
Google Scholar ID: 9Pt2PbsAAAAJ
Research fellow, Weierstrass Institute, Berlin
Citations & Impact
Citations
1,645
H-index
19
i10-index
35
Publications
20
Co-authors
48
list available
Publications
Browse publications on Google Scholar (top-right) ↗
Resume (English only)
Academic Achievements
- 2025 preprint: 'Local regression on path spaces with signature metrics' (with Luca Pelizzari, Davit Gogolashvili).
- 2025 preprint: 'Pricing American options under rough volatility using deep-signatures and signature-kernels' (with Luca Pelizzari, Jia-Jie Zhu).
- 2024 preprint: 'State spaces of multifactor approximations of nonnegative Volterra processes' (with Eduardo Abi Jaber, Simon Breneis).
- 2024 preprint: 'Dimension reduction for path signatures' (with Martin Redmann).
- 2024 preprint: 'Continuous time Stochastic optimal control under discrete time partial observations' (with Boualem Djehiche, Eliza Rezvanova, Raul Tempone).
- 2024 preprint: 'Quasi-Monte Carlo with Domain Transformation for Efficient Fourier Pricing of Multi-Asset Options' (with Chiheb Ben Hammouda, Antonis Papapantoleon, Michael Samet, Raul Tempone).
Research Experience
- Affiliated with the research group 'Stochastic Algorithms and Nonparametric Statistics' at Weierstraß Institute for Applied Analysis and Stochastics (WIAS Berlin).
- Collaborates on DFG-funded MATH+ project AA4-2 on numerical methods for stochastic optimal control.
- Member of DFG International Research Training Group IRTG 2544 'Stochastic Analysis in Interaction'.
- Participant in DFG CRC/TRR 388 'Rough Analysis, Stochastic Dynamics and Related Fields'.
- Member of Math+, the Berlin Cluster of Excellence.
- Teaches 'Actuarial Mathematics' at TU Berlin.
Background
- Main research interests are financial mathematics and stochastic numerics.
- Working on modeling stock indices (e.g., S&P 500) consistently with the implied volatility surface and the VIX.
- Focuses on 'rough volatility models' using fractional Brownian motion-type stochastic volatility processes to capture the power-law explosion of implied volatility for very short maturities.
- Research includes numerical approximation of stochastic optimal control problems, particularly optimal stopping.
- Applies rough path theory and path signatures to develop efficient numerical methods for non-Markovian stochastic control problems.
- Explores theoretical connections between rough path analysis and deep neural networks.
