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Unit of study_

ECMT3150: The Econometrics of Financial Markets

This unit studies and develops the econometric models and methods employed for the analysis of data arising in financial markets. It extends and complements the material covered in ECMT2130. The unit will cover econometric models that have proven useful for the analysis of both synchronous and non-synchronous financial time series data over the last two decades. Modern Statistical methodology will be introduced for the estimation of such models. The econometric models and associated methods of estimation will be applied to the analysis of a number of financial datasets. Students will be encouraged to undertake hands-on analysis using an appropriate computing package. Topics covered include: Discrete time financial time series models for asset returns; modelling and forecasting conditional volatility; Value at Risk and modern market risk measurement and management; modelling of high frequency and/or non-synchronous financial data and the econometrics of market microstructure issues. The focus of the unit will be in the econometric models and methods that have been developed recently in the area of financial econometrics and their application to modelling and forecasting market risk measures.


Academic unit Economics
Unit code ECMT3150
Unit name The Econometrics of Financial Markets
Session, year
Semester 1, 2022
Attendance mode Normal day
Location Remote
Credit points 6

Enrolment rules

ECMT2010 or ECMT2110 or ECMT2030 or ECMT2130 or ECMT2160
Available to study abroad and exchange students


Teaching staff and contact details

Coordinator Simon Kwok,
Type Description Weight Due Length
Final exam (Take-home short release) Type D final exam Final Exam
Short-answer and structural questions
35% Formal exam period 2.5 hours
Outcomes assessed: LO1 LO2
Assignment Written Assignments
Analytical questions, computer exercises, short-answer questions
15% Multiple weeks 1000 words
Outcomes assessed: LO1 LO2 LO3 LO4
Presentation group assignment Group report and presentation
Details will be given during the semester
30% STUVAC 1000 words
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
In-semester test (Open book) Type C in-semester exam Mid-semester test
Short-answer and structural questions (1.5 hours + 30 mins upload time)
20% Week 07
Due date: 06 Apr 2022 at 11:00
1.5 hours
Outcomes assessed: LO1 LO2
group assignment = group assignment ?
Type C in-semester exam = Type C in-semester exam ?
Type D final exam = Type D final exam ?

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a High distinction indicates work of an exceptional standard, a Distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range


High distinction

85 - 100



75 - 84



65 - 74



50 - 64



0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

Academic integrity

The Current Student website provides information on academic honesty, academic dishonesty, and the resources available to all students.

The University expects students and staff to act ethically and honestly and will treat all allegations of academic dishonesty or plagiarism seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic dishonesty. If such matches indicate evidence of plagiarism or other forms of dishonesty, your teacher is required to report your work for further investigation.

WK Topic Learning activity Learning outcomes
Week 01 Preliminaries Lecture and tutorial (3 hr) LO1 LO2 LO4
Week 02 Preliminary, Linear time series analysis Lecture and tutorial (3 hr) LO1 LO2
Week 03 Linear time series analysis Lecture and tutorial (3 hr) LO1 LO2
Week 04 Linear time series analysis Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 05 Conditional heteroskedastic models Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 06 Conditional heteroskedastic models Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 07 High frequency data analysis, market microstructure Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 08 Continuous time models, option pricing Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 09 Continuous time models, option pricing Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 10 Extreme values, quantiles, value-at-risk Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 11 Extreme values, quantiles, value-at-risk Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 12 Group presentation for the group project Presentation (3 hr) LO5
Week 13 Review Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 14 (STUVAC) Submission of group report Project (1 hr) LO2 LO3 LO4 LO5

Attendance and class requirements

  • Attendance: According to Faculty Board Resolutions, students in the Faculty of Arts and Social Sciences are expected to attend 90% of their classes. If you attend less than 50% of classes, regardless of the reasons, you may be referred to the Examiner’s Board. The Examiner’s Board will decide whether you should pass or fail the unit of study if your attendance falls below this threshold.
  • Lecture recording: Most lectures (in recording-equipped venues) will be recorded and may be made available to students on the LMS. However, you should not rely on lecture recording to substitute your classroom learning experience.
  • Preparation: Students should commit to spend approximately three hours’ preparation time (reading, studying, homework, essays, etc.) for every hour of scheduled instruction.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

All readings for this unit can be accessed on the Library Catalog (link available on Canvas).

Required text: Tsay, Ruey (2010). Analysis of Financial Time Series (3rd ed.).Wiley.

Supplementary text: Linton, Oliver (2019). Financial Econometrics: Models and methods (1st ed.). Cambridge University Press.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University’s graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. attain a deeper understanding of econometric methods and their application to, and adaption for, modelling and forecasting financial market data
  • LO2. demonstrate proficiency in time series econometric techniques, including estimation, inference and forecasting
  • LO3. develop proficiency in collecting and processing different types of financial time series data efficiently and with integrity
  • LO4. develop proficiency in the use of appropriate computing software and packages for financial time series modelling and forecasting
  • LO5. develop proficiency in presenting the modelling result effectively to both financial econometricians and general audience.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
Pedagogical changes have been made to adapt the unit to online teaching.


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