Universität Wien

390005 SE PhD-AW: Advanced Stochastic Modelling (2022W)

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

Wednesday 05.10.2022 10:30 -12:00 Seminarraum 9, Kolingasse 14-16, OG01 01.23
Wednesday 12.10.2022 10:30 -12:00 Seminarraum 18 Kolingasse 14-16, OG02 02.20
Wednesday 19.10.2022 10:30 -12:00 Seminarraum 18 Kolingasse 14-16, OG02 02.20
Wednesday 09.11.2022 10:30 -12:00 Seminarraum 18 Kolingasse 14-16, OG02 02.20
Wednesday 16.11.2022 10:30 -12:00 Seminarraum 18 Kolingasse 14-16, OG02 02.20
Monday 21.11.2022 15.00-16.30 Ort: Seminarraum 10, Kolingasse 14-16, OG01
Tuesday 22.11.2022 16:45 -18:15 Seminarraum 7, Kolingasse 14-16, OG01 01.13
Wednesday 23.11.2022 10:30 -12:00 Seminarraum 18 Kolingasse 14-16, OG02 02.20
Thursday 01.12.2022 09:45 - 11:15 Seminarraum 18 Kolingasse 14-16, OG02 02.2
Monday 05.12.2022 15:00 -16:30 Seminarraum 10, Kolingasse 14-16, OG01 01.24
Wednesday 07.12.2022 10:30 -12:00 PC-Seminarraum 3, Kolingasse 14-16, OG02 02.08
Wednesday 14.12.2022 10:30 -12:00 PC-Seminarraum 3, Kolingasse 14-16, OG02 02.08
Wednesday 11.01.2023 10:30 -12:00 PC-Seminarraum 3, Kolingasse 14-16, OG02 02.08
Wednesday 18.01.2023 10:30 -12:00 Seminarraum 18 Kolingasse 14-16, OG02 02.20
Wednesday 25.01.2023 10:30 -12:00 Seminarraum 18 Kolingasse 14-16, OG02 02.20

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, detection of statistical arbitrages, calibration, hedging, 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: Mo 28.11.2022 10:31