Computational Finance
Course content
See "Knowledge" below.
MSc Programme in Actuarial Mathematics
MSc Programme in Mathematics-Economics
Knowledge (= a rough lecture plan)
- Rudimentary low-level programming.
- Data and computational resources at Copenhagen University and beyond.
- Monte Carlo simulation 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.
Only a selection (based on lecturer and student interest) of the last three topics will be covered.
Skills
High- and low-level programming as fits the problem.
Extracting and handling financial data.
Effcient use computaional resources, both wrt. hardware (distributed computing) and software (error order analysis).
Ability to implement Monte Carlo simulation techniques (to investigate pricing and hedging) for a large range of financial products and models.
Ability to implement a (limited) number of more specialized methods for more specific models and problems.
Competencies
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 in C++.
Academic qualifications equivalent to a BSc degree is
recommended.
Individual feedback given on the basis of assignments.
- ECTS
- 7,5 ECTS
- Type of assessment
-
Continuous assessment3 equally weighted hand-ins over the course of the course.
- Aid
- 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
Kursusinformation
- Language
- English
- Course number
- NMAK16004U
- ECTS
- 7,5 ECTS
- Programme level
- Full Degree Master
- Duration
-
1 block
- Schedulegroup
-
A
- Capacity
- No restrictions/ no limitations
- Studyboard
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Mathematical Sciences
Contracting faculty
- Faculty of Science
Course Coordinator
- David Glavind Skovmand (8-566e727970647167437064776b316e7831676e)
Teacher
David Skovmand
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