MTH5530 Computational methods in finance
Faculty of Science
MTH5530 Computational methods in finance is a level 5, 6-credit-point, postgraduate unit from the Faculty of Science, offered in 2027 in Semester 1 at Clayton. It has no prerequisites.
- Credit points
- 6
- Offered in 2027
- Semester 1
- Clayton
- Assessment
- Exam 50%
- and 1 other task
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Requisites
Before MTH5530
No prerequisites or corequisites besides the enrolment rules below.
After MTH5530
No unit lists MTH5530 as a prerequisite in the 2027 handbook.
Enrolment rules
COREQUISITE: Only students enrolled in the Master of Financial Mathematics and the Master of Mathematics can enrol in this unit. Exceptions can be made with permission from the unit coordinator.
Overview
The overall aim of this unit is to study the fundamental computational methods for solving problems in financial mathematics. This includes a full overview of finite-difference methods for obtaining numerical solutions of partial differential equations, convergence and stability analysis of finite-difference methods, iterative techniques for solving large-scale linear systems arising from numerical solutions of PDEs, the Black-Scholes equation and stochastic volatility models, option pricing, Monte Carlo computation, and selected topics in mathematical finance.
Offerings in 2027
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Clayton | On campus |
Assessment
- Continuous assessmentDemonstration50%
- Final assessment - Exam (3 hours and 10 minutes)Examination50%
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
Learning outcomes
When you finish this unit, you should be able to:
- 1
Develop specialised mathematical knowledge and computational skills within the fields of partial differential equations and probability theory;
- 2
Develop and understand the complex connections between specialised financial and mathematical concepts;
- 3
Apply critical thinking to problems in partial differential equations that relate to financial derivatives;
- 4
Apply computational problem solving skills within the finance context;
- 5
Formulate expert solutions to practical financial problems using specialised cognitive and technical skills within the fields of partial differential equations and probability theory;
- 6
Communicate complex information in an accessible format to a non-mathematical audience.
Workload and teaching
- Applied sessions11 hours
- Seminars36 hours
- Teaching approachActive learning
- Two 1.5 -hour seminars;
- One 1-hour applied class (in weeks 2-12) and
- Eight hours of independent study per week.
This is an established way to teach high-level abstract mathematics effectively. In this model, you are expected to revise the theory explained in the lectures at home. This active learning process will assist students in fully understanding the new concepts.
Learning resources
Required resources
R. U. Seydel. Tools for Computational Finance. Springer, 2017.
E. E. Qian, R. H. Hua, E. H. Sorensen. Quantitative Equity Portfolio Management: Modern Techniques and Applications. Taylor & Francis, 2007.
Contacts
- Unit Coordinators
- Dr Oscar Yu Tian
- Chief Examiners
- Dr Oscar Yu Tian
Common questions
What are the prerequisites for MTH5530?
MTH5530 has no prerequisites, but enrolment rules apply.
When is MTH5530 offered?
In 2027, MTH5530 runs in Semester 1 at Clayton.
Does MTH5530 have an exam?
Yes. The exam is worth 50% of the final mark, alongside 1 other task.