250188 VO Selected topics in probability theory (2019S)
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Details
Language: English
Examination dates
Lecturers
Classes (iCal) - next class is marked with N
Wednesday
06.03.
15:00 - 16:30
Seminarraum 11 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
13.03.
15:00 - 16:30
Seminarraum 11 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
20.03.
15:00 - 16:30
Seminarraum 11 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
27.03.
15:00 - 16:30
Seminarraum 11 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
03.04.
15:00 - 16:30
Seminarraum 11 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
10.04.
15:00 - 16:30
Seminarraum 11 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
08.05.
15:00 - 16:30
Seminarraum 11 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
15.05.
15:00 - 16:30
Seminarraum 11 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
22.05.
15:00 - 16:30
Seminarraum 11 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
29.05.
15:00 - 16:30
Seminarraum 11 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
05.06.
15:00 - 16:30
Seminarraum 11 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
12.06.
15:00 - 16:30
Seminarraum 11 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
19.06.
15:00 - 16:30
Seminarraum 11 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
26.06.
15:00 - 16:30
Seminarraum 11 Oskar-Morgenstern-Platz 1 2.Stock
Information
Aims, contents and method of the course
Assessment and permitted materials
Minimum requirements and assessment criteria
* measure theory
* basic knowledge in martingales and stochastic processes
* basic ideas of math finance will be useful but not necessary
* basic knowledge in martingales and stochastic processes
* basic ideas of math finance will be useful but not necessary
Examination topics
Reading list
Association in the course directory
MSTV
Last modified: We 23.09.2020 00:28
In the second part of the lecture we will complement the worst case point of view of MOT on robust finance by a ``local'' approach. This will naturally lead us to adapted versions of the OT problem, the COT, which we will explore in detail. Our discussion will be guided by examples from finance and stochastic analysis.