Stochastic Analysis
Module MA4405
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
Module version of SS 2021 (current)
There are historic module descriptions of this module. A module description is valid until replaced by a newer one.
Whether the module’s courses are offered during a specific semester is listed in the section Courses, Learning and Teaching Methods and Literature below.
| available module versions | |||
|---|---|---|---|
| SS 2021 | SS 2020 | SS 2014 | WS 2013/4 |
Basic Information
MA4405 is a semester module in English language at Master’s level which is offered in winter semester.
This Module is included in the following catalogues within the study programs in physics.
- Specialization Modules in Elite-Master Program Theoretical and Mathematical Physics (TMP)
| Total workload | Contact hours | Credits (ECTS) |
|---|---|---|
| 180 h | 60 h | 6 CP |
Content, Learning Outcome and Preconditions
Content
Brownian motion: construction and path properties, reflection principle. Stochastic integrals with respect to Brownian motion and Itô's formula. Stochastic integrals with respect to continuous martingales, cross-variation and Itô's product rule. Stochastic differential equations, weak and strong solutions. Lévy' s Theorem, Girsanov's Theorem and applications. Donsker's invariance principle.
Learning Outcome
After successful completion of the module, students are able to:
- define Brownian motion and apply basic calculations
involving Brownian motion
- understand fundamental results such as the reflection
principle for Brownian motion, Lévy's Theorem and
Donsker's invariance principle
- understand the basics of stochastic integration
- apply Itô's formula
- understand the basics of stochastic differential equations
- apply change-of-measure techniques.
- define Brownian motion and apply basic calculations
involving Brownian motion
- understand fundamental results such as the reflection
principle for Brownian motion, Lévy's Theorem and
Donsker's invariance principle
- understand the basics of stochastic integration
- apply Itô's formula
- understand the basics of stochastic differential equations
- apply change-of-measure techniques.
Preconditions
MA2409 - Probability Theory
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
| Type | SWS | Title | Lecturer(s) | Dates | Links |
|---|---|---|---|---|---|
| VO | 3 | Stochastic Analysis | Gantert, N. Tokushige, Y. |
Thu, 08:30–11:00, BC1 BC1 2.02.01 |
|
| UE | 1 | Stochastic Analysis (Exercise Session) | Gantert, N. Tokushige, Y. | dates in groups |
Learning and Teaching Methods
lecture, exercise module
The module is offered as lectures with accompanying practice sessions. In the lectures, the contents will be presented in a talk with demonstrative examples, as well as through discussion with the students. The lectures should motivate the students to carry out their own analysis of the themes presented and to independently study the relevant literature. Corresponding to each lecture, practice sessions will be offered, in which exercise sheets and solutions will be available. In this way, students can deepen their understanding of the methods and concepts taught in the lectures and independently check their progress.
The module is offered as lectures with accompanying practice sessions. In the lectures, the contents will be presented in a talk with demonstrative examples, as well as through discussion with the students. The lectures should motivate the students to carry out their own analysis of the themes presented and to independently study the relevant literature. Corresponding to each lecture, practice sessions will be offered, in which exercise sheets and solutions will be available. In this way, students can deepen their understanding of the methods and concepts taught in the lectures and independently check their progress.
Media
blackboard, assignments
Literature
F. den Hollander, M. Löwe, H. Maassen (1997): Stochastic Analysis, Lecture Notes, University of Nijmegen,
Netherlands.
P. Mörters, Y. Peres (2010): Brownian Motion, Cambridge University Press, New York / Melbourne / Madrid / Cape Town / Singapore / Sao Paulo / Delhi / Dubai / Tokyo
Netherlands.
P. Mörters, Y. Peres (2010): Brownian Motion, Cambridge University Press, New York / Melbourne / Madrid / Cape Town / Singapore / Sao Paulo / Delhi / Dubai / Tokyo
Module Exam
Description of exams and course work
The module examination is based on a written exam (60-90 minutes). Students have to know theoretical foundations of Brownian motion, Lévy's Theorem and Donsker's invariance principle. They are able to understand the basics of stochastic integration and stochastic differential equations and can apply Itô's formula.
Exam Repetition
The exam may be repeated at the end of the semester.
Current exam dates
Currently TUMonline lists the following exam dates. In addition to the general information above please refer to the current information given during the course.
| Title | |||
|---|---|---|---|
| Time | Location | Info | Registration |
| Stochastic Analysis | |||
| Thu, 2022-02-17, 11:30 till 13:00 | BC2: BC2 0.01.16 BC2: BC2 0.01.17 |
Bitte beachten Sie die Hinweise unter https://www.tum.de/die-tum/aktuelles/coronavirus/corona-lehre-pruefungen/. // Please read the information at https://www.tum.de/en/about-tum/news/coronavirus/corona-teaching-exams/ carefully. | till 2022-01-15 (cancelation of registration till 2022-02-10) |
| Mon, 2022-04-11, 14:15 till 15:45 | BC2: BC2 0.01.16 |
Bitte beachten Sie die Hinweise unter https://www.tum.de/die-tum/aktuelles/coronavirus/corona-lehre-pruefungen/. // Please read the information at https://www.tum.de/en/about-tum/news/coronavirus/corona-teaching-exams/ carefully. | till 2022-04-04 |