NMAK18003U Beyond the Classic Markov Chain Life Insurance Setting
MSc Programme in Actuarial Mathematics
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
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.
See Absalon for a list of course literature.
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.
The students will recieve either oral or written feedback in connection with their individual assignments.
- 7,5 ECTS
- Type of assessment
- Continuous assessmentThe 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.
Same as the ordinary exam.
If the ordinary exam is not passed, it is possible to hand in one or more of the three assignments in an improved version. The grade is based on all three assignments – equally weighted.
Criteria for exam assesment
The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.