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Unit of study_

ECMT6006: Applied Financial Econometrics

Semester 1, 2021 [Normal day] - Remote

This unit provides an introduction to some of the widely used econometric models designed for the analysis of financial data, and the procedures used to estimate them. Special emphasis is placed upon empirical work and applied analysis of real market data. The unit deals with topics such as: the statistical nature of financial data; the specification, estimation and testing of assets pricing models; the analysis of high frequency financial data; and the modelling of volatility in financial returns. Throughout the unit, students are encouraged (especially in assignments) to familiarise themselves with financial data and learn how to apply the models to these data.

Unit details and rules

Unit code ECMT6006
Academic unit Economics
Credit points 6
ECMT6002 or ECMT6702
Assumed knowledge


Available to study abroad and exchange students


Teaching staff

Coordinator Ye Lu,
Type Description Weight Due Length
Final exam (Take-home short release) Type D final exam Final exam
Take-home final exam
50% Formal exam period 3 hours
Outcomes assessed: LO1 LO2 LO3
Assignment Take-home assignment I
Short/Long Answers
15% Week 06
Due date: 15 Apr 2021 at 23:59
Outcomes assessed: LO1 LO2 LO3
In-semester test (Open book) Type C in-semester exam Mid-semester test
Take-home exam
20% Week 07
Due date: 21 Apr 2021 at 18:00
1.5 hours
Outcomes assessed: LO1 LO2 LO3
Assignment Take-home assignment II
Short/Long Answers
15% Week 13
Due date: 03 Jun 2021 at 23:59
Outcomes assessed: LO1 LO2 LO3
Type C in-semester exam = Type C in-semester exam ?
Type D final exam = Type D final exam ?

Assessment summary

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

For more information see guide to grades.

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.

Academic integrity

The Current Student website  provides information on academic integrity 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 integrity breaches seriously.  

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

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

WK Topic Learning activity Learning outcomes
Week 01 Introduction and overview of the course Lecture (3 hr) LO2
Week 02 Review of probability, statistics, and time series concepts Lecture (3 hr) LO2
Week 03 The efficient market hypothesis and financial market predictability Lecture (3 hr) LO2 LO3
Week 04 Modelling asset returns using ARMA models Lecture (3 hr) LO1 LO2 LO3
Week 05 Modelling volatility using (G)ARCH models I Lecture (3 hr) LO1 LO2 LO3
Week 06 Modelling volatility using (G)ARCH models II Lecture (3 hr) LO1 LO2 LO3
Week 07 Midterm exam Lecture (3 hr)  
Week 08 Event-study analysis Lecture (3 hr) LO1 LO2 LO3
Week 09 Value-at-Risk and expected shortfall Lecture (3 hr) LO1 LO2 LO3
Week 10 Evaluating, comparing and combining models Lecture (3 hr) LO1 LO2 LO3
Week 11 Modelling high-frequency financial data I Lecture (3 hr) LO1 LO2 LO3
Week 12 Modelling high-frequency financial data II Lecture (3 hr) LO1 LO2 LO3
Week 13 Realized volatility Lecture (3 hr) LO1 LO2 LO3

Attendance and class requirements

  • Attendance: This unit has both on-campus and remote-learning streams. For the on-campus stream, students are expected to attend 90% of their classes. For the remote-learning stream, students are also encouraged to attend the live lectures in a weekly basis. 
  • Lecture Recording: Lecture recordings will be made available to students on Canvas. However, you should not rely on lecture recording to substitute your live in-class 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

The reference textbooks are

  • Campbell, J. Y., A. Lo and A. C. MacKinlay, The Econometrics of Financial Markets, Princeton Univeresity Press, 1997.
  • Patton, A., Forecasting Financial Markets, Draft Book (Encrypted PDF file posted on Canvas), 2019


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. become familiar with the stylised facts of financial data and with the econometric methods for analysing such data
  • LO2. understand the key features of the classic and latest econometrics models used in financial economics
  • LO3. be able to implement the econometric models in statistical packages, apply them to the data, and interpret the output from these models.

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

This section outlines changes made to this unit following staff and student reviews.

No changes have been made since this unit was last offered


The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

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