Faculty of Science

Computational Finance
Provider: Faculty of Science

Activity no.: 5559-23-07-31
	Enrollment deadline: 24/04/2023

Place

Department of Mathematical Sciences

Date and time

24.04.2023, at: 08:00 - 23.06.2023, at: 16:00

Regular seats

ECTS credits

7.50

Contact person

Nina Weisse E-mail address: weisse@math.ku.dk

Enrolment Handling/Course Organiser

Nils Martin Anders Tegnér E-mail address: tegner@math.ku.dk

Written language

English

Teaching language

English

Semester/Block

Block 4

Scheme group

A (Tues 8-12 + Thurs 8-17)

Exam requirements

The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course

Exam form

Continuous assessment

Exam details

2 equally weighted hand-ins over the course of the course.

Exam aids

All aids allowed

Grading scale

7 point grading scale. For PhD students: Passed / Not Passed

Exam re-examination

20 minute oral exam without preparation

Course workload

Course workload category	Hours
Lectures	36.00
Theory exercises	18.00
Project work	76.00
Preparation	76.00

Sum	206.00

Content

See "Knowledge" below.

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.

Skills
- 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.

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.

Literature

Notes, articles and working papers. See Absalon for a list of course literature.

Teaching and learning methods

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

Remarks

Academic qualifications:
A bachelor degree from the Departments of Mathematical Sciences (or something suitably close to that; computer science, polit, engineering, ...) plus (at least) working knowledge of continuous-time finance.
As an exchange, guest and credit student - please see: http://www.science.ku.dk/english/courses-and-programmes/
Continuing Education - please see: http://www.science.ku.dk/english/courses-and-programmes/continuing-education/bsc-msc-courses/

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