COURSE UNIT TITLE

: MATHEMATICAL RISK THEORY

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
STA 6052 MATHEMATICAL RISK THEORY ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR DOCTOR GÜÇKAN YAPAR

Offered to

Statistics
Statistics
STATISTICS

Course Objective

The objective of this course is to explain the probability theoretical Fundamentals of risk. Topics covered include: Utility theory and insurance, the individual risk model, collective risk models, ruin theory, Premium principles, bonus malus system, credibility theory, IBNR techniques.

Learning Outcomes of the Course Unit

1   At the end of this course students will be able to describe the assumptions of IRM and CRM.
2   At the end of this course students will be able to learn utility theory, insurance and premium principles.
3   At the end of this course students will be able to learn ruin theory
4   At the end of this course students will be able to learn bonus malus system.
5   At the end of this course students will be able to learn credibility theory and IBNR technigues

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Utiliy Theory and Insurance
2 Time expected utility model, Classes of utility functions
3 Optimality of stop loss reinsurance
4 The individual risk models
5 Mixed distributions and risks, Convolution, Transformations, Reinsurance.
6 Collective risk models
7 Compuond Distributions, Distributions for the number of claims.
8 Midterm exam
9 Compound Poisson distributions
10 Compuond Negative Binomial Distributions, some Parametric Claim Size Distributions.
11 Ruin Theory
12 Bonus-malus systems
13 Credibility Theory, Homework
14 IBNR technigues

Recomended or Required Reading

Textbook(s):
Hans Bühlmann, Mathematical Methods in Risk Theory, 2nd Edition, Springer Verlag, 1996.
Supplementary Book(s):

Planned Learning Activities and Teaching Methods

The course consists of lecture, class discussion and problem solving.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 ASG ASSIGNMENT
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.30 +ASG * 0.20 +FIN * 0.50
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + ASG * 0.20 + RST * 0.50


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

Exams, homework.

Language of Instruction

English

Course Policies and Rules

Student responsibilities: Attendance to at least 70% for the lectures is an essential requirement of this course and is the responsibility of the student. It is necessary that attendance to the lecture and homework delivery must be on time. Any unethical behavior that occurs either in presentations or in exams will be dealt with as outlined in school policy. You can find the undergraduate policy at http://web.deu.edu.tr/fen.

Contact Details for the Lecturer(s)

DEU Faculty of Science, Department of Statistic
e-mail: guckan.yapar@deu.edu.tr
Tel: 0232 301 85 59

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparations before/after weekly lectures 14 3 42
Preparation for midterm exam 1 28 28
Preparation for final exam 1 34 34
Preparing assignments 1 40 40
Midterm 1 2 2
Final 1 2 2
TOTAL WORKLOAD (hours) 190

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.155545
LO.255545
LO.355545
LO.455545
LO.555545