Mr Jon Aster
Course overview
- Study period
- Semester 2, 2024 (22/07/2024 - 16/11/2024)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Business School
An advanced course in financial modelling with a strong focus on practical application. Topics covered will include valuation of derivative securities, simulation and lattice methods, and volatility modelling.
Course requirements
Prerequisites
You'll need to complete the following courses before enrolling in this one:
FINM3422
Restrictions
Restricted to students enrolled in the BAdvFinEcon(Hons)
Course staff
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
Please note: Teaching staff do not have access to the timetabling system to help with class allocation. Therefore, should you need help with your timetable and/or allocation of classes, please ensure you email business.mytimetable@uq.edu.au from your UQ student email account with the following details:
Full Name
Student ID
Course Code
Aims and outcomes
This course aims to provide students with the opportunity to apply advanced financial modelling theories and techniques to the development of solutions for industry problems. In particular, students will be equipped to work collaboratively to integrate their knowledge of a variety of financial theories, collect and analyse data, and utilise a range of technical tools in order to solve problems relevant to industry.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Value a range of options and other derivative securities.
LO2.
Explain how derivative securities are traded in a range of markets.
LO3.
Implement a range of techniques for modelling volatility.
LO4.
Apply spreadsheet and programming tools in quantifying volatility and other metrics.
LO5.
Demonstrate core collaborative competencies needed to work effectively in a team.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Examination |
In-Semester Exam During Class
|
30% |
28/08/2024 10:10 am |
| Computer Code, Paper/ Report/ Annotation | Assignment - Part 1 (Individual) | 30% |
20/09/2024 5:00 pm |
| Computer Code, Paper/ Report/ Annotation |
Assignment - Part 2 (Group)
|
40% |
25/10/2024 5:00 pm |
Assessment details
In-Semester Exam During Class
- In-person
- Mode
- Written
- Category
- Examination
- Weight
- 30%
- Due date
28/08/2024 10:10 am
- Learning outcomes
- L01, L02
Task description
60-minute sit-down closed-book exam to take held inside the usual time and venue for this class.
The exam will cover content delivered in weeks 1-4 inclusive and will be comprised of around 15 multiple choice questions, and 1-3 questions from each of the following formats: short answer, calculation, coding.
This assessment task evaluates students' abilities, skills, and knowledge without the aid of generative Artificial Intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 60 minutes |
| Calculator options | (In person) Casio FX82 series or UQ approved , labelled calculator only |
| Open/closed book | Closed Book examination - no written materials permitted |
| Exam platform | Paper based |
| Invigilation | Invigilated in person |
Submission guidelines
Students to write their answers in the answer booklet provided and submit it at the end of the exam.
Deferral or extension
You may be able to defer this exam.
Late submission
You will receive a mark of 0 if this assessment is submitted late.
Assignment - Part 1 (Individual)
- Mode
- Written
- Category
- Computer Code, Paper/ Report/ Annotation
- Weight
- 30%
- Due date
20/09/2024 5:00 pm
- Learning outcomes
- L01, L04
Task description
You will design and code an algorithm that can accomplish a series of defined objectives. Detailed information about the requirements of the assignment and the marking criteria will be made available to students on Blackboard.
This assessment task evaluates students' abilities, skills, and knowledge without the aid of generative Artificial Intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Electronic submission through Blackboard assignment link.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 14 days. Extensions are given in multiples of 24 hours.
Late submission
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.
Assignment - Part 2 (Group)
- Team or group-based
- Mode
- Written
- Category
- Computer Code, Paper/ Report/ Annotation
- Weight
- 40%
- Due date
25/10/2024 5:00 pm
- Learning outcomes
- L01, L03, L04, L05
Task description
You will be formed into pairs and will integrate, innovate and refine on each of your individual algorithms submitted for "Assignment - Part 1 (Individual)" in response to specific feedback provided for that item and a new series of defined objectives. Detailed information about the requirements of the assignment and the marking criteria will be made available to students on Blackboard.
This assessment task evaluates students' abilities, skills, and knowledge without the aid of generative Artificial Intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Electronic submission through Blackboard assignment link.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 28 days. Extensions are given in multiples of 24 hours.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0 - 29 |
Absence of evidence of achievement of course learning outcomes. |
| 2 (Fail) | 30 - 46 |
Minimal evidence of achievement of course learning outcomes. |
| 3 (Marginal Fail) | 47 - 49 |
Demonstrated evidence of developing achievement of course learning outcomes |
| 4 (Pass) | 50 - 64 |
Demonstrated evidence of functional achievement of course learning outcomes. |
| 5 (Credit) | 65 - 74 |
Demonstrated evidence of proficient achievement of course learning outcomes. |
| 6 (Distinction) | 75 - 84 |
Demonstrated evidence of advanced achievement of course learning outcomes. |
| 7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. |
Additional course grading information
Grades will be allocated according to University-wide standards of criterion-based assessment.
Supplementary assessment
Supplementary assessment is available for this course.
Learning resources
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.
Library resources
Library resources are available on the UQ Library website.
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Week 1 |
Seminar |
Seminar 1 - Market Making and Orderbooks Resource: Chapter 1, Course Reader Lecture (3 hours): Learn the concept of making a market, the role of market makers, and the mechanics of order books. Learning outcomes: L01, L02 |
Multiple weeks From Week 2 To Week 5 |
General contact hours |
Consultation with Tutors Each week from week 2 - week 5 consultations will be held that serve as additional drop-in help sessions to review course materials. There will not be defined activities for these sessions, but they will be open for you to seek assistance with course content. Consultations will NOT be held from weeks 6 onwards, but tutors will be available to assist students attending the 3-hour open labs each week. Learning outcomes: L01, L02, L03, L04, L05 |
Week 2 |
Seminar |
Seminar 2 - Introduction to Python Resource: Chapter 2, Course Reader Lecture (2 hours): Learn the course-relevant essentials of the Python programming language. Lab (1 hour): Practice programming in Python by attempting exercises. Learning outcomes: L04 |
Week 3 |
Seminar |
Seminar 3 - Time Series Analysis I Resource: Chapter 3, Course Reader Lecture (2 hours): Learn the concept of time series analysis, and about two particular models for time series data: autocorrelation and ARMA. Lab (1 hour): Practice working with autocorrelation and ARMA models. Learning outcomes: L01, L04 |
Week 4 |
Seminar |
Seminar 4 - Time Series Analysis II NOTE: This lecture falls on a public holiday. No class will be held and a pre-recorded video version of the lecture will be posted on Blackboard instead. Resource: Chapter 3, Course Reader Lecture (2 hours): Learn how to apply time series techniques to model stock prices. Lab (1 hour): Practice working with a trading strategy that utilises the concepts learned in this lesson: pair trading. Learning outcomes: L01, L04 |
Week 5 |
Seminar |
Seminar 5 - The Optibook Exchange Resource: Optibook Manual Lecture (2 hours): Meet Optibook, a virtual sandbox trading platform designed by Optiver. Lab (1 hour): Get hands-on practice using Optibook. Learning outcomes: L04 |
Week 6 |
Seminar |
Seminar 6 - Our First Algorithm Resource: Chapter 4, Course Reader MID-SEMESTER EXAM (1 hour): In the first hour of class this week students will sit the mid-semester exam in-person in the usual venue. Lecture (1 hour): Step through the development of a trading algorithm using the concepts and tools learned in previous sessions. Lab (1 hour): Practice implementing and experimenting with the trading algorithm covered in this seminar. Learning outcomes: L01, L04 |
Week 7 |
Seminar |
Seminar 7 - Finishing the Algo, Market Making Resource: Chapter 4, Course Reader Open Lab (3 hours): Interactively refine the algorithm from the last seminar by learning to implement stellage, a strategy for scaling the size of your trades depending on the size of the spread being captured. Learning outcomes: L01 |
Week 8 |
Seminar |
Seminar 8 - Basket Trading Resource: Chapter 5, Course Reader Open Lab (3 hours): Implement and explore the strategy of basket trading used by market makers. Learning outcomes: L01 |
Week 9 |
Seminar |
Seminar 9 - Assignment Help Resource: N/A Open Lab (3 hours): Get help working through your assignment. Learning outcomes: L01 |
Mid Sem break |
No student involvement (Breaks, information) |
In-Semester Break No classes this week. |
Week 10 |
Seminar |
Seminar 10 - Options Pricing and Volatility Resource: Chapter 6, Course Reader Lecture (2 hours): Learn about how to price options and how to estimate volatility using the market price of options. Lab (1 hour): Practice modelling volatility using the concepts from this seminar. Learning outcomes: L01, L03, L04, L05 |
Week 11 |
Seminar |
Seminar 11 - Options Trading I Resource: Chapter 7, Course Reader Open Lab (3 hours): Interactively work through implementing an options trading strategy. Learning outcomes: L01, L03, L04, L05 |
Week 12 |
Seminar |
Seminar 12 - Options Trading II Resource: Chapter 7, Course Reader Open Lab (3 hours): Continue working through and improving a strategy for trading options. Learning outcomes: L01, L03, L04, L05 |
Week 13 |
Seminar |
Seminar 13 - Summary Resource: N/A Open Lab (3 hours): Revise content from throughout the course. Learning outcomes: L01, L03, L04, L05 |
Policies and procedures
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:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.