37003 Application of Numerical and Computational Approaches A
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Subject handbook information prior to 2024 is available in the Archives.
Credit points: 8 cp
Subject level:
Postgraduate
Result type: Grade and marksRequisite(s): 24 credit points of completed study in spk(s): STM91543 Core Subjects (Mathematics) OR 24 credit points of completed study in spk(s): STM91515 Core Subjects (M Quantitative Finance)
These requisites may not apply to students in certain courses. See access conditions.
Anti-requisite(s): 25871 Computational Methods and Model Implementation
Description
Given the increasing complexity of financial markets, the corresponding mathematical models for analysing asset prices and market variables have also grown intricate. Closed-form solutions for problems in quantitative finance, such as derivatives pricing, hedging, portfolio optimisation, equity analysis, yield curve analysis, and risk management, are typically absent under complex models. Therefore, numerical and computational methods have become essential for deriving concrete insights from these intricate financial models.
This subject is one of two capstone subjects that concentrate on developing practical skills for solving quantitative finance problems using numerical and computational techniques. This subject enables students to conceptualise and articulate a quantitative finance problem based on current industry examples, devise and execute a suitable theoretical and computational framework for solving the problem and generate a specialised report communicating their analysis and findings to a target audience.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Implement solutions for quantitative problems in finance using Python |
|---|---|
| 2. | Implement solutions for quantitative problems in finance using market data |
| 3. | Implement solutions for quantitative problems in finance using key computational techniques, including optimisation and neural networks |
| 4. | Build on existing open source libraries for Python to create solutions for more involved problems. |
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the development of following course intended learning outcomes:
- Appraise advanced knowledge and critically evaluate the information's source and relevance, with a focus on applications of mathematical methodologies to quantitative finance problem solving. (1.1)
- Investigate complex and challenging real-world problems in the areas of quantitative finance by critically evaluating information and solutions and conducting appropriate approaches to independent research. (2.1)
- Reflect and evaluate the value, integrity, and relevance of multiple sources of information to derive responsive, innovative solutions, show creativity, innovation and application of technologies in complex quantitative finance problems. (4.1)
- Develop and present complex ideas and justifications using appropriate communication approaches from a variety of methods (oral, written, visual) to communicate with mathematicians, data analysts, scientists, industry, and the general public. (5.1)
Contribution to the development of graduate attributes
The Faculty of Science has determined that our courses will aim to develop the following attributes in students at the
completion of their course of study. Each subject will contribute to the development of these attributes in ways
appropriate to the subject, thus not all attributes are expected to be addressed in all subjects.
This subject contributes to the development of the following graduate attributes:
GA 1 - Disciplinary Knowledge –acquire detailed specialised quantitative finance knowledge and professional competency required to work as a quantitative finance analyst in the modern finance industry
GA 2 - Research, inquiry and critical thinking - develop the ability to apply and demonstrate critical and analytical skills to developing solution to complex real world problems.
GA 4 - Reflection, Innovation and Creativity – develop the ability source and analyse multiple sources of data to develop innovative solutions to real world problems in quantitative finance.
GA 5 - Communication – develop professional communication skills for a range of technical and non-technical audiences.
Teaching and learning strategies
The subject is presented in a seminar format. Essential principles are presented and analysed in short lectures, after which students are guided through practical application exercises. Online delivery of the subject material is integrated with face-to-face teaching, and “flipped learning” permits a significant portion of in-class time to be devoted to guided computer exercises, which should be solved collaboratively in small teams. Students are expected to prepare for classes by accessing the relevant links to online material and by completing assigned readings. Students should be prepared to discuss the material and tutorial questions during classes.
The major assessment is an individual assignment that mirrors the type of task a quantitative finance professional could expect to face. Such tasks include implementing a new method or model, calibrating a model to market data, pricing financial derivatives, measuring and managing risk,and performing other calculations required to solve problems arising in practice. Students will be required to give a short presentation on their assignment problems and their proposed solutions and they will obtain feedback on their approaches. Each student will be required to engage explicitly with another student's work by acting as the assigned discussant for their presentation. Several weeks before the final due date, students will have the opportunity to submit a draft of their assignment submissions, in order to receive feedback on their work to date. They will have the opportunity to incorporate the feedback in their final submissions.
Content (topics)
- Review of key features of the Python language using problems from quant finance
- Optimisation in Quantitative Finance
- Model calibration
- Hedging strategies using deep neural networks
- Algorithmic trading strategies
- Algorithmic differentiation & adjoint algorithmic differentiation
Assessment
Assessment task 1: Assignment (Individual research project)
| Intent: | This assessment task contributes to the development of the following graduate attributes: 1 - Disciplinary Knowledge. 2 - Research, enquiry and critical thinking. 4 - Reflection, Innovation, Creativity. 5 - Communication. |
|---|---|
| Objective(s): | This assessment task addresses subject learning objective(s): 1, 2, 3 and 4 This assessment task contributes to the development of course intended learning outcome(s): 1.1, 2.1, 4.1 and 5.1 |
| Weight: | 50% |
| Criteria: |
|
Assessment task 2: Final Exam (Individual)
| Intent: | This assessment task contributes to the development of the following graduate attributes: 1 - Disciplinary Knowledge. 2 - Research, enquiry and critical thinking. 4 - Reflection, Innovation, Creativity. |
|---|---|
| Objective(s): | This assessment task addresses subject learning objective(s): 1, 2, 3 and 4 This assessment task contributes to the development of course intended learning outcome(s): 1.1, 2.1 and 4.1 |
| Weight: | 50% |
| Criteria: | Evidence of knowledge and competencies in applying techniques in financial mathematics presented in this subject and applied in the final exam. |
Minimum requirements
Students must achieve at least 50% of the subject’s total marks.
Required texts
There is no textbook.
Recommended texts
Students will find the following books useful:
- Hans Petter Langtangen (2012). A Primer on Scientific Programming with Python, 3rd edition, Springer.
- Irv Kalb (2016). Learn to Program with Python, Apress.
- Joey Bernard (2016). Python Recipes Handbook: A Problem-Solution Approach, Apress.
PDF versions of these books can be downloaded for free from the UTS library.