NMAA05115U Stochastic Processes in Life Insurance (LivStok)
MSc Programme in Actuarial Mathematics
MSc Programme in Mathematics-Economics
- Finite variation processes
- Markov processes
- Semi-Markov processes
- Martingale methods in life insurance
- Inference for models of counting processes
Stochastic processs and methods applied in life insurance models.
At the end of the course, the students are expected to be able to
- Apply theorems on stochastic processes of finite variation, including theorems on counting processes,
- Markov chains, integral processes, martingales.
- Analyse Markov chain models and derive Thiele differential equation for reservs using martingale methods.
- Analyse extended models and derive differential equations for reservs.
- Analyse statistical parametric life history models.
- Analyse statistical nonparametric life history models.
To make the student operational and to give the student knowledge in application of stochastic processes in life insurance.
Academic qualifications equivalent to a BSc degree is recommended.
There is feedback on the two mandatory assignments.
- 7,5 ECTS
- Type of assessment
- Oral examination, 30 minutes30-minutes oral exam without time for preparation.
- Exam registration requirements
Two mandatory assignments must be approved and valid before the student is allowed attending the exam
- Only certain aids allowed
The student may bring notes to the oral exam, but they are only allowed to consult these in the first minute after they have drawn a question. After that, all notes must be put away.
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners
As the ordinary exam. If the two mandatory assignments have not been approved during the course the non-approved project(s) must be (re)submitted. The assignments must be approved no later than three weeks before the beginning of the re-exam week.
Criteria for exam assesment
The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome.