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 2020 in Semester 1 at Clayton. It has no prerequisites and unlocks 4 units.
- Credit points
- 6
- Offered in 2020
- Semester 1
- Clayton
This is the 2020 handbook entry. See the 2027 entry.
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Requisites
Before MTH5530
No prerequisites or corequisites besides the enrolment rules below.
After MTH5530
4 units list MTH5530 as a prerequisite or corequisite.
Enrolment rules
COREQUISITE: Only students enrolled in the Master of Financial 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 2020
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Clayton | On campus |
| First semester (Fully flex) | Clayton | Flexible |
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
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
Two 1.5-hour lectures and one 1-hour applied class per week
Contacts
- Chief Examiners
- Dr Tiangang Cui
- Unit Coordinators
- Dr Tiangang Cui