Universität Wien
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390040 SE PhD-AW: Advanced Stochastic Modelling (2024S)

3.00 ECTS (2.00 SWS), SPL 39 - Doktoratsstudium Wirtschaftswissenschaften
Continuous assessment of course work
MIXED
Moodle

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).

Details

max. 24 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

Upcoming talks:
Tuesday 23.07.2024 11:30-13:00 Seminarroom 8, Kolingasse 14-16, OG01 01.18
Tuesday 23.07.2024 14:00-15:30 Seminarroom 8, Kolingasse 14-16, OG01 01.18
Tuesday 23.07.2024 16:30-18:00 Seminarroom 8, Kolingasse 14-16, OG01 01.18
Wednesday 24.07.2024 18:00-19:30 Seminarroom 8, Kolingasse 14-16, OG01 01.18

  • Wednesday 20.03. 11:30 - 13:00 Digital
  • Wednesday 24.04. 13:15 - 14:45 Digital
  • Wednesday 22.05. 13:15 - 14:45 Digital
  • Monday 17.06. 11:30 - 13:00 Seminarraum 9, Kolingasse 14-16, OG01
  • Tuesday 18.06. 11:30 - 13:00 Seminarraum 9, Kolingasse 14-16, OG01
  • Tuesday 18.06. 15:00 - 16:30 Seminarraum 19, Kolingasse 14-16, OG02
  • Wednesday 19.06. 11:30 - 13:00 Seminarraum 9, Kolingasse 14-16, OG01
  • Thursday 20.06. 11:30 - 13:00 Seminarraum 9, Kolingasse 14-16, OG01
  • Monday 24.06. 13:15 - 14:45 Seminarraum 9, Kolingasse 14-16, OG01
  • Tuesday 25.06. 11:30 - 13:00 Seminarraum 9, Kolingasse 14-16, OG01
  • Wednesday 26.06. 11:30 - 13:00 Seminarraum 9, Kolingasse 14-16, OG01
  • Wednesday 26.06. 16:45 - 18:15 Seminarraum 19, Kolingasse 14-16, OG02
  • Tuesday 23.07. 11:30 - 13:00 Seminarraum 8, Kolingasse 14-16, OG01
  • Tuesday 23.07. 13:15 - 14:45 Seminarraum 8, Kolingasse 14-16, OG01
  • Tuesday 23.07. 15:00 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01
  • Tuesday 23.07. 16:45 - 18:15 Seminarraum 8, Kolingasse 14-16, OG01
  • Wednesday 24.07. 16:45 - 18:15 Seminarraum 8, Kolingasse 14-16, OG01
  • Wednesday 24.07. 18:30 - 20:00 Seminarraum 8, 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 11.07.2024 10:46