Quantitative Risk Management (QRM)
Risk measures; extreme value theory; multivariate distributions and dependence; copulas; credit modeling and operational risk modeling.
MSc Programme in Actuarial Mathematics
MSc Programme in Mathematics-Economics
Knowledge: By the end of the course, the student should
develop an understanding of risk measures, including VaR and
expected shortfall, and of stastistical methods from extreme value
theory (including the Hill estimator and the POT method).
Also, the student should develop a thorough understanding of the
various means for analyzing dependence, including elliptical
distributions and copulas. Moreover, the student should
develop a thorough knowledge of some of the standard models used
for credit risk modeling and operational risk modeling.
Skills: The student should develop analytical and computational skills for computing VaR, expected shortfall, and for analyzing dependence and credit risk losses.
Competencies: The student should be able to analyze risk in a variety financial settings and to compute VaR, expected shortfall, or other related risk measures in these contexts. The student should also be able to apply basic methods from extreme value theory to analyze these risks. Moreover, the student should develop proficiency in analyzing dependent risks using, in particular, elliptical distributions or copulas. Finally, the student should develop a competence in analyzing credit risk losses.
5 hours of lectures per week for 7 weeks.
VidSand2 concurrently, or equivalent.
Academic qualifications equivalent to a BSc degree is recommended.
- 7,5 ECTS
- Type of assessment
Oral examination, 30 minutes
- Type of assessment details
- No preparation time.
- Without aids
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners.
Criteria for exam assessment
The student must, in a satisfactory way, demonstrate that he/she has mastered the learning outcome.
Single subject courses (day)
- Course number
- 7,5 ECTS
- Programme level
- Full Degree Master
- Block 2
- No limit
The number of seats may be reduced in the late registration period
- Study Board of Mathematics and Computer Science
- Department of Mathematical Sciences
- Faculty of Science
- Jeffrey F. Collamore (9-65716e6e636f717467426f63766a306d7730666d)
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