Computational Finance

Course content

See "Knowledge" below. 


MSc Programme in Actuarial Mathematics
MSc Programme in Mathematics-Economics

Learning outcome

Knowledge (= a rough lecture plan)


Topics may include but are not limited to:

  • High-level programming
  • Data and computational resources
  • Monte Carlo techniques in option pricing: variance reduction, diffusion (and possibly Levy) process simulation, American options, adjoint techniques
  • Numerical transform methods for option pricing
  • Numerical optimization and model calibration
  • Numerical methods for solving parabolic partial differential equations
  • Machine learning approaches in quantitative finance


Only a selection (based on lecturer and student interest) of the topics will be covered.



High- and low-level programming as fits the problem.

Extracting and handling financial data.

Ability to implement numerical techniques (to investigate pricing and hedging) for a range of financial products and models.

Ability to implement a (limited) number of more specialized methods for more specific models and problems.



Proficieny classical and modern numerical methods for quantitative finance problems. This is a question of having both a sizeable "toolbox" and the ability pick the appropriate on in a given situation. 

4 hours of lectures and and 2 hours of exercises per week for 9 weeks.

See Absalon for a list of course literature.

A bachelor degree from the Departments of Mathematical Sciences (or something suitably close to that; plus (at least) working knowledge of continuous-time finance, and some experience with programming.

Academic qualifications equivalent to a BSc degree is recommended.

Continuous feedback during the course of the semester

Individual feedback given on the basis of assignments. 

7,5 ECTS
Type of assessment
Continuous assessment
Type of assessment details
2 equally weighted hand-ins over the course of the course.
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
One internal examiner.
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
  • Lectures
  • 36
  • Preparation
  • 76
  • Theory exercises
  • 18
  • Project work
  • 76
  • English
  • 206


Course number
7,5 ECTS
Programme level
Full Degree Master

1 block

Block 4
No limit
The number of seats may be reduced in the late registration period
Study Board of Mathematics and Computer Science
Contracting department
  • Department of Mathematical Sciences
Contracting faculty
  • Faculty of Science
Course Coordinator
  • Nils Martin Anders Tegnér   (6-78696b726976447165786c326f7932686f)
Saved on the 28-02-2022

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