37010 Statistics and Financial Econometrics
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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)
These requisites may not apply to students in certain courses. See access conditions.
Anti-requisite(s): 25879 Statistics and Financial Econometrics
Description
Modern day financial modelling requires advanced statistical and econometric analysis which is based on the foundations of statistics and time series analysis. This subject aims to introduce the fundamentals of this knowledge, starting with basic of hypothesis testing, linear regression and moving through to ARMA and GARCH modelling. These techniques are introduced using real-world financial and other data. Students develop their computational skills by implementing these techniques using the R programming language.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Define the main probability-theoretic and statistical concepts required in modern finance |
|---|---|
| 2. | Apply a range of statistical techniques to the analysis of financial data |
| 3. | Apply econometric techniques to estimate popular models using financial time series. |
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)
- Practice professionally adhering to confidentiality requirements, ethical conduct, data management, and organisation and collaborative skills in the context of applying mathematical and statistical modelling to quantitative finance problems. (3.1)
- 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)
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:
- Disciplinary Knowledge - acquire detailed specialised quantitative finance knowledge and the professional competency required to work as a quantitative finance analyst in the modern finance industry.
- Research, inquiry and critical thinking - develop the ability to apply and demonstrate critical and analytical skills to developing solution to complex real world problems.
- Professional, Ethical and Social Responsibility - develop an enhanced capacity to work ethically and professionally using collaborative skills in the workplace.
- 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.
Teaching and learning strategies
The subject is presented in lecture and practical workshop format. The theoretical concepts are presented in lectures and students are lead through practical application exercises.
This subject will use the ‘flipped education’ model where students will have access to online learning resources and will undertake preliminary learning tasks prior to coming to classes where they engage in further learning and practical workshops. This subject will enable students to experience an effective integration of online and face-to-face on campus learning. Students will engage in active learning experiences through their participation in in-class tutorials/workshops where they will collaboratively work on applying the theoretical concepts covered in lectures to solving problems.
The teaching and learning concepts take into account typical practice of financial industry professionals, since the assessment tasks are designed to reflect real-life applications.
That is, the assignment questions are selected from a range of problems including challenging exercises. Their solution requires creativity and communication, students can access all information sources, and will have enough time for completing their work
Students will receive feedback on their work during tutorials. Students will receive summative feedback on their assignment solutions.
Content (topics)
- Random variables and distributions
- Expectation of functions of RVs
- Single and 2-sample t-tests
- One and two-ANOVA and F-tests
- Simple and multiple linear regression
- Basics of time series
- AR, MA, ARMA and ARIMA models
- GARCH models
Students are encouraged to work collaboratively through each week's problems and to ask questions during the session. Consultations can be arranged for discussion of difficulties.
Assessment
Assessment task 1: Assignment (Individual)
| Intent: | This assessment task contributes to the development of the following graduate attributes:
|
|---|---|
| Objective(s): | This assessment task addresses subject learning objective(s): 1, 2 and 3 This assessment task contributes to the development of course intended learning outcome(s): 1.1, 2.1, 3.1 and 4.1 |
| Type: | Exercises |
| Groupwork: | Individual |
| Weight: | 50% |
| Criteria: | The assignment aims at broadening disciplinary knowledge: Thereby, the accuracy of answers, presentation of results and correct formulation of explanations are practiced. For advanced problems, the ability to collect and critically assess information is crucial. These problems will provide space for reflection, innovation and creativity. The assessment of assignment takes into account the degree of professional, ethical and social responsibility visible in student's work. |
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 and 3 This assessment task contributes to the development of course intended learning outcome(s): 1.1, 2.1 and 4.1 |
| Type: | Examination |
| Groupwork: | Individual |
| Weight: | 50% |
| Criteria: | The exam will be assessed on accuracy of solutions and explanations. In the final exam, the disciplinary knowledge is assessed in terms of integrity of answers and accuracy of explanations. For exam solutions, also innovation, creativity, ability of critical reflection are recognised and credited. |
Minimum requirements
Students must achieve at least 50% of the subject’s total marks.
Recommended texts
Notes will be provided.