390040 SE PhD-AW: Advanced Stochastic Modelling (2026S)
Continuous assessment of course work
Labels
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from Mo 09.02.2026 09:00 to Tu 17.02.2026 20:00
- Registration is open from Tu 24.02.2026 09:00 to We 25.02.2026 12:00
- Deregistration possible until Sa 14.03.2026 23:59
Details
max. 24 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
- Tuesday 03.03. 13:15 - 14:45 Seminarraum 15, Kolingasse 14-16, OG01
- Tuesday 10.03. 13:15 - 14:45 Seminarraum 15, Kolingasse 14-16, OG01
- Tuesday 17.03. 13:15 - 14:45 Seminarraum 15, Kolingasse 14-16, OG01
- Monday 23.03. 13:15 - 14:45 Seminarraum 16, Kolingasse 14-16, OG02
- Tuesday 14.04. 13:15 - 14:45 Seminarraum 15, Kolingasse 14-16, OG01
- Tuesday 21.04. 13:15 - 14:45 Seminarraum 12, Kolingasse 14-16, OG01
- Tuesday 21.04. 15:00 - 16:30 Seminarraum 12, Kolingasse 14-16, OG01
- Tuesday 28.04. 13:15 - 14:45 Seminarraum 15, Kolingasse 14-16, OG01
- Tuesday 05.05. 13:15 - 14:45 Seminarraum 15, Kolingasse 14-16, OG01
- Tuesday 12.05. 13:15 - 14:45 Seminarraum 15, Kolingasse 14-16, OG01
- N Tuesday 19.05. 13:15 - 14:45 Seminarraum 15, Kolingasse 14-16, OG01
- Tuesday 09.06. 13:15 - 14:45 Seminarraum 15, Kolingasse 14-16, OG01
- Tuesday 16.06. 13:15 - 14:45 Seminarraum 15, Kolingasse 14-16, OG01
- Tuesday 23.06. 13:15 - 14:45 Seminarraum 15, Kolingasse 14-16, OG01
Information
Aims, contents and method of the course
The focus of the seminar is on newest developments in the area of data-driven and machine learning approaches to financial problems combined with their mathematical and probabilistic foundations.This includes the analysis of training algorithms, financial time-series predictions, calibration, hedging, risk management, portfolio optimization, and market simulation.An emphasis will lie on machine learning tools tailored to learning of dynamic processes, e.g. neural SDEs and signature based methods. Mathematical tools from rough paths theory as well as (infinite) dimensional stochastic analysis shall be used.
Assessment and permitted materials
Talk on an article in the subject area of the seminar
Minimum requirements and assessment criteria
Assessment of the talk
Examination topics
Content of the paper chosen for the talk
Reading list
The articles will be announced on Moodle.
Association in the course directory
Last modified: Th 16.04.2026 10:47