Course coordinator
Duy-Minh will lead the course from Week 1 to approximately Week 7 of the course. His consultation hours will be detailed on the Announcements page on Blackboard.
Introduction to computational methods in finance & applications. Topics from binomial trees, numerical solution of stochastic differential equations, and numerical solution of Black-Scholes like partial differential equations.
The past few decades have witnessed an explosion in the trading of sophisticated financial instruments, known as derivatives. Financial derivative securities, such as options, can be viewed as a form of insurance or used for speculation purposes. These instruments are routinely used by large corporations to hedge currency fluctuations, uncertain energy costs and commodity price volatility.
Modern finance now requires use of sophisticated computational tools for pricing and hedging derivative contracts. In this course, we will study a variety of numerical approaches for carrying out these tasks and explore the links among them, namely
In particular, we will focus on applications of these computational methods in pricing and hedging popular types of derivatives, such as European, American, and Asian options, in the context of Black Scholes model, and its extensions, including local/stochastic volatility and/or stochastic interest rates. MATLAB will be the official programming language for this course.
The basics of financial mathematics from MATH3090 (some ofᅠ these topics will be reviewed):
Basics of stochastic processes in finance (some of these topics will be reviewed):
ᅠStatistics and probability theory (some of these topics will be reviewed):
Some basic programming skills.
You'll need to complete the following courses before enrolling in this one:
MATH3090
We recommend completing the following courses before enrolling in this one:
MATH4091
Duy-Minh will lead the course from Week 1 to approximately Week 7 of the course. His consultation hours will be detailed on the Announcements page on Blackboard.
The timetable for this course is available on the UQ Public Timetable.
All classes will be conducted on campus at the times and location advertised in your personal timetable.
Tutorials start the second week of the semester.
Make-up lessons for public holidays will be scheduled. Please check Blackboard for details.
Important: If you are ill, then do not attend any classes in person. Alternative arrangements can be organised – consult Blackboard for details.
To understand a range of numerical methods commonly used to solve problems arising in financial mathematics.
After successfully completing this course you should be able to:
LO1.
Use binomial trees (lattice methods) to price financial derivatives with focus on European and American-style, and barrier (path-dependent) options.
LO2.
Develop efficient Monte Carlo simulation and effective variance reduction techniques for pricing options, with focus on European, American style (early exercise), and Asian (path-dependent) options.
LO3.
Derive partial differential equations arising in option pricing, and solve them efficiently numerically, with focus on European, American-style (early exercise) and Asian (path-dependent) options.
LO4.
Understand the concepts of convergence and stability of numerical partial differential equation methods, and be able to carry out relevant analysis.
LO5.
Understand and be able to develop numerical methods for pricing derivatives under more realistic models, such as local or stochastic volatility, or stochastic interest rates.
Category | Assessment task | Weight | Due date |
---|---|---|---|
Computer Code, Paper/ Report/ Annotation, Essay/ Critique, Tutorial/ Problem Set | Assignment 1 | 20% |
16/08/2024 5:00 pm |
Computer Code, Paper/ Report/ Annotation, Essay/ Critique, Tutorial/ Problem Set | Assignment 2 | 20% |
13/09/2024 5:00 pm |
Computer Code, Paper/ Report/ Annotation, Essay/ Critique, Tutorial/ Problem Set | Assignment 3 | 20% |
18/10/2024 5:00 pm |
Examination |
Examination
|
40% |
End of Semester Exam Period 2/11/2024 - 16/11/2024 |
A hurdle is an assessment requirement that must be satisfied in order to receive a specific grade for the course. Check the assessment details for more information about hurdle requirements.
16/08/2024 5:00 pm
This assignment will involve the applications, development and assessment of the numerical methods developed in lectures. The assignment will require programming. In particular, the assignment will involve certain aspects of numerical computation and programming in Matlab, including, but are not limited to, (i) developing numerical algorithms, (ii) understanding and being able to use start code, and (iii) writing, testing/debugging code in Matlab.
Upload via Blackboard: Please submit your written answers to all the assignment questions as one PDF document. If you need to add code please do so as separate clearly labelled files.
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Solutions for assessment item/s will be released 7 days after the assessment is due and as such, an extension after 7 days will not be possible.
See ADDITIONAL ASSESSMENT INFORMATION for the extension and deferred examination information relating to this assessment item
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
You are required to submit assessable items on time. If you fail to meet the submission deadline for any assessment item then the listed penalty will be deducted per day for up to 7 calendar days, at which point any submission will not receive any marks unless an extension has been approved. Each 24-hour block is recorded from the time the submission is due.
13/09/2024 5:00 pm
This assignment will involve the applications, development and assessment of the numerical methods developed in lectures. The assignment will require programming. In particular, the assignment will involve certain aspects of numerical computation and programming in Matlab, including, but are not limited to, (i) developing numerical algorithms, (ii) understanding and being able to use start code, and (iii) writing, testing/debugging code in Matlab.
Upload via Blackboard: Please submit your written answers to all the assignment questions as one PDF document. If you need to add code please do so as separate clearly labelled files.
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
See ADDITIONAL ASSESSMENT INFORMATION for the extension and deferred examination information relating to this assessment item.
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
You are required to submit assessable items on time. If you fail to meet the submission deadline for any assessment item then the listed penalty will be deducted per day for up to 7 calendar days, at which point any submission will not receive any marks unless an extension has been approved. Each 24-hour block is recorded from the time the submission is due.
18/10/2024 5:00 pm
This assignment will involve the applications, development and assessment of the numerical methods developed in lectures. The assignment will require programming. In particular, the assignment will involve certain aspects of numerical computation and programming in Matlab, including, but are not limited to, (i) developing numerical algorithms, (ii) understanding and being able to use start code, and (iii) writing, testing/debugging code in Matlab.
Upload via Blackboard: Please submit your written answers to all the assignment questions as one PDF document. If you need to add code please do so as separate clearly labelled files.
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Solutions for assessment item/s will be released 7 days after the assessment is due and as such, an extension after 7 days will not be possible.
See ADDITIONAL ASSESSMENT INFORMATION for the extension and deferred examination information relating to this assessment item.
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
You are required to submit assessable items on time. If you fail to meet the submission deadline for any assessment item then the listed penalty will be deducted per day for up to 7 calendar days, at which point any submission will not receive any marks unless an extension has been approved. Each 24-hour block is recorded from the time the submission is due.
End of Semester Exam Period
2/11/2024 - 16/11/2024
The examination will test all topics investigated in the course. The final examination will involve certain aspects of numerical computation, including, but are not limited to, understanding and developing numerical algorithms. No Matlab programming will be tested on the final exam.
Planning time | 10 minutes |
---|---|
Duration | 120 minutes |
Calculator options | (In person) Casio FX82 series only or UQ approved and labelled calculator |
Open/closed book | Closed Book examination - no written materials permitted |
Exam platform | Paper based |
Invigilation | Invigilated in person |
N/A
You may be able to defer this exam.
See ADDITIONAL ASSESSMENT INFORMATION for the extension and deferred examination information relating to this assessment item.
Full criteria for each grade is available in the Assessment Procedure.
Grade | Description |
---|---|
1 (Low Fail) |
Absence of evidence of achievement of course learning outcomes. Course grade description: Total mark less than 20% |
2 (Fail) |
Minimal evidence of achievement of course learning outcomes. Course grade description: Total mark at least 20% and less than 45% |
3 (Marginal Fail) |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: Total mark at least 45% and less than 50%, OR a total mark of at least 50% and less than 40% on the examination |
4 (Pass) |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: Total mark at least 50% and less than 65% Hurdle: Students must achieve a mark of 40% or more on the final exam to pass the subject. |
5 (Credit) |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: Total mark at least 65% and less than 75% Hurdle: Students must achieve a mark of 40% or more on the final exam to pass the subject. |
6 (Distinction) |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: Total mark at least 75% and less than 85% Hurdle: Students must achieve a mark of 40% or more on the final exam to pass the subject. |
7 (High Distinction) |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: Total mark between 85% and 100% Hurdle: Students must achieve a mark of 40% or more on the final exam to pass the subject. |
Hurdle: ᅠStudents must achieve a mark of 40% or more on the examination to pass the subject.
Supplementary assessment is available for this course.
Should you fail a course with a grade of 3, you may be eligible for supplementary assessment. Refer to my.UQ for information on supplementary assessment and how to apply.
Supplementary assessment provides an additional opportunity to demonstrate you have achieved all the required learning outcomes for a course.
If you apply and are granted supplementary assessment, the type of supplementary assessment set will consider which learning outcome(s) have not been met.
Supplementary assessment in this course will be a 2-hour examination similar in style to the end-of-semester examination. To receive a passing grade of 3S4, you must obtain a mark of 50% or more on the supplementary assessment.
Artificial Intelligence
The assessment tasks in this course evaluate students’ abilities, skills and knowledge without the aid of Artificial Intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute misconduct under the Student Code of Conduct.
Applications for Extensions to Assessment Due Dates
Extension requests are submitted online via my.UQ – applying for an extension. Extension requests received in any other way will not be approved. Additional details associated with extension requests, including acceptable and unacceptable reasons, may be found at my.UQ.
Please note:
Applications to defer an exam
In certain circumstances you can apply to take a deferred examination for in-semester and end-of-semester exams. You'll need to demonstrate through supporting documentation how unavoidable circumstances prevented you from sitting your exam. If you can’t, you can apply for a one-off discretionary deferred exam.
Deferred Exam requests are submitted online via mySi-net. Requests received in any other way will not be approved. Additional details associated with deferred examinations, including acceptable and unacceptable reasons may be found at my.UQ.
Please note:
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Find the required and recommended resources for this course on the UQ Library website.
Students should regularly check the Blackboard website https://learn.uq.edu.au for course materials and additional resources.
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Learning period | Activity type | Topic |
---|---|---|
Multiple weeks From Week 1 To Week 13 |
Lecture |
Notes on Computational Finance The lectures will be based on lecture notes developed by the lecturers. Learning outcomes: L01, L02, L03, L04, L05 |
Multiple weeks From Week 2 To Week 13 |
Tutorial |
Enhancing computer-based problem solving skills The tutorials will discuss lecture material, mathematical exercises and computing assignments. The primary focus will be on enhancing students' computer-based problem solving skills. Learning outcomes: L01, L02, L03, L04, L05 |
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.