Beyond the Classic Markov Chain Life Insurance Setting

Course content

Parametric modeling and inference for the classic (semi-)Markov chain life insurance setting; credibility in life insurance using hierarchical (random effect, frailty) extensions of classic models; implementation of estimation procedures in R; practical issues related to valuation and pricing


MSc Programme in Actuarial Mathematics

Learning outcome

At the end of the course the student is expected to have:


Knowledge about parametric modeling and inference beyond the classic (semi-)Markov chain life insurance setting, including the relationship to frailty models in event history and survival analysis and knowledge about likelihoods for random counting measure distributions. Knowledge about credibility theory in life insurance.


Skills to implement procedures related to estimation beyond the classic (semi-)Markov chain life insurance setting in R.


The course will strengthen the student's competences in navigating inside the classic (semi-)Markov life insurance setting and develop the student's ability to formulate, handle, and interpret hierarchical (credibility) extensions of the setting.

4 hours of lectures and 2 hours of practical/exercise classes each week for 8 weeks. Active participation is expected.

See Absalon for a list of course literature.

Stochastic Processes in Life Insurance (LivStok).

The student is also recommended to have prior experience in programming using the language R, e.g. obtained through the bachelor programme in Actuarial Mathematics from the University of Copenhagen.

Continuous feedback during the course of the semester


The students will recieve either oral or written feedback in connection with their individual assignments.

7,5 ECTS
Type of assessment
Continuous assessment
The exam consists of three individual assignments. Each assignment counts 1/3 towards the total grade.
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
One internal examiner.
Criteria for exam assessment

The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.

Single subject courses (day)

  • Category
  • Hours
  • Exam
  • 60
  • Lectures
  • 32
  • Exercises
  • 16
  • Preparation
  • 98
  • English
  • 206