Topics in Non-Life Insurance (Skade2)

Course content

Various distributions that are useful in non-life insurance
The impact of deductibles on the insurance premium calculations
Maximum likelihood methods, including the adjustment for deductibles. Also use of the delta method.
Asymptotic properties of the estimators
Model selection methods
Some linear and generalized linear model theory


Course description: Distributions in insurance, deductibles, maximum likelihood estimation, model choice, non parametric method, linear and generalized linear models.

Education

MSc Programme in Actuarial Mathematics

Learning outcome

Knowledge:

Understand basic claim number and claim frequency models.

Understand the impact of deductibles, and how to compensate for this in modelling.

Know how to use maximum likelihood to fit insurance data.

Have some insight into the art of picking relevant and useful models for the data. 

Know something about the consequences of picking a poor model

A good understanding of linear and generalized linear models, and the difference between continuous and discrete models.

Know how to fit nonstandard models, such as Pareto regression with deductibles.


Skills: 

Be able to take raw data and prepare for statistical analysis.

Be able to fit and select a meaningful model among a variety of alternatives. This includes picking good distributions, good covariates and avoid overfitting.

Run, and if necessary program in R, the necessary calculations.


Competences:

Be able to go from raw data to practical and useful statistical models. A typical application is in pricing insurance products.

5 hours of lectures each week for 7 weeks.

Lecture notes

Bachelor in insurance mathematics or similar.

Academic qualifications equivalent to a BSc degree is recommended.

Collective
ECTS
7,5 ECTS
Type of assessment
Written examination, 3 hours under invigilation
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Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
External censorship
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
  • 3
  • Preparation
  • 128
  • Lectures
  • 35
  • Project work
  • 40
  • English
  • 206