Quantitative Risk Management
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 2018
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|
|WS 2021/2||SS 2020||SS 2018||SS 2012|
MA5415 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.
- Catalogue of non-physics elective courses
|Total workload||Contact hours||Credits (ECTS)|
|150 h||45 h||5 CP|
Content, Learning Outcome and Preconditions
a. Quantile functions
b. Empirical distribution and quantile function
II) Risk measures
a. Axiomatic approach
III) Standard methods for market risk
a. Variance-covariance method
b. Historical simulation
c. Monte Carlo method
IV) Statistical methods in extreme value theory
a. Extreme value distributions
b. Domains of attraction
c. Statistical estimation of extremes
V) Copulas and dependence structures
b. Dependence measures
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
|VO||2||Quantitative Risk Management||Horvath, B.||
Tue, 12:15–13:45, BC2 BC2 3.5.06
and singular or moved dates
|UE||1||Quantitative Risk Management (Exercise Session)||de Witte, D. Horvath, B.||dates in groups|
Learning and Teaching Methods
The module consists of the lecture supplemented by an exercise session. The lecture material is presented with slide presentations and mathematical proofs are presented on the blackboard. The students are encouraged to study course references and course subjects. The exercise session consists of theoretical and computer-oriented exercises. In the theoretical exercises students will work under instructor assistance on assignments, sometimes in teamwork. The exercises contribute to a better understanding of the lecture materials.
Carmona, R. (2004): Statistical Analysis of Financial Data in S-Plus, Springer, New York.
Glasserman, P. (2004): Monte Carlo Methods in Financial Engineering, Springer, New York.
Description of exams and course work
The exam may be repeated at the end of the semester.