Stochastic Processes in Life Insurance (LivStok)

Course content

  • Finite variation processes
  • Markov processes
  • Semi-Markov processes
  • Martingale methods in life insurance
  • Inference for models of counting processes
Education

MSc Programme in Actuarial Mathematics

Learning outcome

Knowledge:
Stochastic processs and methods applied in life insurance models.

Skills: 
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.

 

Competences:
To make the student operational and to give the student knowledge in application of stochastic processes in life insurance.

Blended teaching and learning: 4 hours of video lectures per week for 7 weeks. Worksheets with exercises/problem solving will be provided for the students for in-depth engagement with the course material. There will be regular meetings with the lecturer for discussions of the course material and the exam.

VidSand1 no later than at the same time. Otherwise similar prerequisites.

Academic qualifications equivalent to a BSc degree is recommended.

This course is only available for students enrolled in the MSc Programme in Actuarial Mathematics in the study year 2023/24 and earlier.

Oral

Upon active participation in meetings for discussions of the course material.

ECTS
7,5 ECTS
Type of assessment
Oral examination, 30 minutes (no preparation time)
Aid
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
Re-exam

Same as the ordinary exam

Criteria for exam assessment

The student should convincingly and accurately demonstrate the knowledge, skills and competences described under Intended learning outcome.

  • Category
  • Hours
  • Lectures
  • 28
  • Preparation
  • 170
  • Theory exercises
  • 7
  • Exam
  • 1
  • English
  • 206

Kursusinformation

Language
English
Course number
NMAA05115U
ECTS
7,5 ECTS
Programme level
Full Degree Master
Duration

1 block

Placement
Block 1
Schedulegroup
C
Capacity
No limitation – unless you register in the late-registration period (BSc and MSc) or as a credit or single subject student.
Studyboard
Study Board of Mathematics and Computer Science
Contracting department
  • Department of Mathematical Sciences
Contracting faculty
  • Faculty of Science
Course Coordinator
  • Jesper Lund Pedersen   (6-6d6876736875437064776b316e7831676e)
Phone: +45 35 32 07 75, office: 04.3.11
Saved on the 21-02-2024

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