NMAK17007U Monte Carlo Methods in Insurance and Finance
MSc programme in Actuarial Mathematics
MSc Programme in Mathematics-Economics
MSc Programme in Statistics
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.
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
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.
Academic qualifications equivalent to a BSc degree is recommended.
- Theory exercises
- Practical exercises
- 7,5 ECTS
- Type of assessment
- Oral examination, 30 minutes
- Type of assessment details
- Oral exam without preparation.
- Exam registration requirements
The two mandatory assignments must both be passed in order to attend the oral exam.
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners
Same as the ordinary exam.
If the assignments were not approved before the ordinary exam they must be resubmitted at the latest two weeks before the beginning of the re-exam week.
Criteria for exam assesment
In order to obtain the grade 12 the student should convincingly and accurately demonstrate the knowledge, skills and competences described under Learning Outcome.
- Course code
- 7,5 ECTS
- Full Degree Master
- 1 block
- Block 2
- Course capacity
The number of seats may be reduced in the late registration period
- Study Board of Mathematics and Computer Science
- Department of Mathematical Sciences
- Faculty of Science
- Jeffrey F. Collamore (9-677370706571737669447165786c326f7932686f)