MATS280 Risk Theory (5 cr)
Study level:
Advanced studies
Grading scale:
0-5
Language:
English
Responsible organisation:
Department of Mathematics and Statistics
Curriculum periods:
2017-2018, 2018-2019, 2019-2020
Description
Content
Stochastic modeling of non-live insurance: Poisson process, risk process, ruin probabilities, Cramer-Lundberg bounds,
heavy and light tails for claim size distributions.
Completion methods
Course exam and exercises. Part of the exercises may be obligatory.
Final exam is an other option.
Assessment details
The grade is based on
a) the number of points in the course exam and possibly additional points from exercises
OR
b) the number of points in the final exam.
At least half of the points are needed to pass the course.
Learning outcomes
After completing the course, the participant knows
* how to model the basic risk of an insurance company,
* how to compute and estimate ruin probabilities,
* the difference in between modelling small and frequent risks (light tails) and big and rare risks (heavy tails).
* how to model the basic risk of an insurance company,
* how to compute and estimate ruin probabilities,
* the difference in between modelling small and frequent risks (light tails) and big and rare risks (heavy tails).
Description of prerequisites
MATA280 Foundations of stochastics or TILA121 Probability or TILA1200 Probability 1.
Study materials
Lecture Notes: C. Geiss and S. Geiss. Non-life insurance mathematics.
T. Mikosch. Non-Life Insurance Mathematics. Springer 2006.
T. Mikosch. Non-Life Insurance Mathematics. Springer 2006.
Completion methods
Method 1
Select all marked parts
Method 2
Select all marked parts
Parts of the completion methods
x
Teaching (5 cr)
Type:
Participation in teaching
Grading scale:
0-5
Language:
English
Teaching
9/2–10/27/2019 Lectures
8/31–10/25/2020 Lectures
10/21–10/21/2020 Exam
11/4–11/4/2020 Exam
8/30–10/24/2021 Lectures
10/20–10/20/2021 Exam, remote exam
11/3–11/3/2021 Exam, remote exam
8/29–10/23/2022 Lectures
10/19–10/19/2022 Course exam
11/2–11/2/2022 Course exam
9/4–10/29/2023 Lectures
10/25–10/25/2023 Course Exam
11/8–11/8/2023 Course Exam
x
Exam (5 cr)
Type:
Exam
Grading scale:
0-5
Language:
English