MA417 Half Unit
Computational Methods in Finance
This information is for the 2013/14 session.
Teacher responsible
Dr Luitgard Veraart
Availability
This course is compulsory on the MSc in Financial Mathematics. This course is available on the MSc in Finance (full-time), MSc in Finance and Economics and MSc in Management and Regulation of Risk. This course is available with permission as an outside option to students on other programmes where regulations permit.
Pre-requisites
Students must have completed September Introductory Course (Financial Mathmatics) (MA400).
Course content
The purpose of this course is to (a) develop the students' computational skills, and (b) introduce a range of numerical techniques of importance to financial engineering. The course starts with the implementation of binomial and trinomial trees. Random number generation, the fundamentals of Monte Carlo simulation and a number of related issues follow. Numerical solutions to stochastic differential equations and their implementation are considered. The course then addresses finite-difference schemes for the solution of partial differential equations arising in finance.
Teaching
8 hours of lectures and 12 hours of computer workshops in the MT. 20 hours of lectures, 4 hours of seminars and 10 hours of computer workshops in the LT.
Formative coursework
Weekly exercises and practicals are set and form the basis of the seminars.
Indicative reading
P.Glasserman, Monte Carlo Methods in Financial Engineering, Springer; R.U. Seydel, Tools for Computational Finance, Springer; P.E.Kloeden and E.Platen, Numerical Solution of Stochastic Differential Equations, Springer; D.M. Capper, Introducing C++ for Scientists, Engineers and Mathematicians, Springer. B. Stroustrup, The C++ Programming Language, Addison Wesley; M. J. Capinski, T. Zastawniak, Numerical Methods in Finance with C++, Cambridge University Press; M. S. Joshi, C++ Design Patterns and Derivatives Pricing, Cambridge University Press;
Assessment
Exam (50%, duration: 2 hours) in the main exam period.
Project (50%) in the ST.
Key facts
Department: Mathematics
Total students 2012/13: 28
Average class size 2012/13: Unavailable
Value: Half Unit
Personal development skills
- Self-management
- Problem solving
- Application of information skills
- Communication
- Application of numeracy skills
- Specialist skills
Course survey results
(2010/11 - 2012/13 combined)
1 = "best" score, 5 = "worst" scoreThe scores below are average responses.
Response rate: 91.5%
Question |
Average | ||||||
---|---|---|---|---|---|---|---|
Reading list (Q2.1) |
1.9 | ||||||
Materials (Q2.3) |
1.6 | ||||||
Course satisfied (Q2.4) |
1.7 | ||||||
Lectures (Q2.5) |
1.7 | ||||||
Integration (Q2.6) |
1.6 | ||||||
Contact (Q2.7) |
1.8 | ||||||
Feedback (Q2.8) |
1.9 | ||||||
Recommend (Q2.9) |
|