# Monte Carlo Methods in Insurance and Finance

### Course content

This will be an introductory course on Monte Carlo simulation techniques. Topics will include: basic principles and sampling methods; variance reduction; quasi-Monte Carlo; discretization methods for stochastic differential equations; applications. Monte Carlo methods are of applied relevance because real-life problems in insurance, finance, and other applied areas are often too complicated to be solved using explicit analytical methods. When simulation is done naively, various problems can arise (e.g., the variance of the estimate may be large compared with the estimate). There are also methodological issues (e.g., effective means for generating random samples). Throughout the course, examples will be drawn from both insurance mathematics and finance.

Education

MSc programme in Actuarial Mathematics
MSc Programme in Statistics
MSc Programme in Mathematics-Economics

Learning outcome

Knowledge:  By the end of the course, the student should develop an understanding of: the basic principles of stochastic simulation, including the generation of random variables and sample paths; the basic principles of importance sampling and other standard variance reduction techniques; discretization methods for simulating stochastic differential equations; and quasi-Monte Carlo methods.

Skills:   The student should develop analytical and computational skills for running complex simulation experiments, involving theoretical knowledge of such techniques as importance sampling, and methods for generating complex stochastic processes.

Competencies:  At the conclusion of the course, the student should be able to generate a variety of random processes, including sample paths of a Brownian motion and of certain stochastic differential equations.  The student should develop a thorough understanding of, and be able to apply, the stadard methods for variance reduction, including importance sampling, control variates, antithetic variables, and stratified sampling.  Finally, the student should develop an understanding of the basic principles behind quasi-Monte Carlo methods.

4 hours of lectures per week for 7 weeks.

A course in probability theory and stochastic processes.

ECTS
7,5 ECTS
Type of assessment
Oral examination, 30 minutes
Oral exam without preparation.
Aid
All aids allowed
Marking scale
Censorship form
No external censorship
Several internal examiners
##### Criteria for exam assessment

In order to obtain the grade 12 the student should convincingly and accurately demonstrate the knowledge, skills and competences described under Learning Outcome.

Single subject courses (day)

• Category
• Hours
• Exam
• 1
• Lectures
• 28
• Practical exercises
• 10
• Theory exercises
• 50
• Preparation
• 117
• English
• 206