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Unit of study_

ECMT6006: Applied Financial Econometrics

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.


Academic unit Economics
Unit code ECMT6006
Unit name Applied Financial Econometrics
Session, year
Semester 1, 2020
Attendance mode Normal day
Location Camperdown/Darlington, Sydney
Credit points 6

Enrolment rules

ECMT6002 or ECMT6702
Available to study abroad and exchange students


Teaching staff and contact details

Coordinator Ye Lu,
Type Description Weight Due Length
Final exam Final exam
Set Formal Exam
50% Formal exam period 2 hours
Outcomes assessed: LO1 LO2
Assignment Take-home assignment I
Short/Long Answers
15% Week 05
Due date: 27 Mar 2020 at 18:00
Outcomes assessed: LO1 LO2 LO3
In-semester test Mid-semester test (take home)
Take home exam
20% Week 07
Due date: 09 Apr 2020 at 18:00
1 hour
Outcomes assessed: LO1 LO2
Assignment Take-home assignment II
Short/Long Answers
15% Week 12
Due date: 22 May 2020 at 18:00
Outcomes assessed: LO1 LO2 LO3

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 Introduction and Review of Probability/Statistics Lecture (3 hr) LO2
Week 02 Stylised Facts of Asset Returns and Financial Market Forecastability Lecture (3 hr) LO1 LO2 LO3
Week 03 Time Series Basics and Testing for Return Predictability Lecture (3 hr) LO2 LO3
Week 04 Estimation of Forecasting Using ARMA Models Lecture (3 hr) LO1 LO2 LO3
Week 05 Volatility Models I Lecture (3 hr) LO1 LO2 LO3
Week 06 Volatility Models II Lecture (3 hr) LO1 LO2 LO3
Week 07 Midterm exam Lecture (3 hr)  
Week 08 Multivariate Volatility Models Lecture (3 hr) LO1 LO2 LO3
Week 09 Value at Risk and Expected Shortfall Modelling Lecture (3 hr) LO1 LO2 LO3
Week 10 Market Microstructure Lecture (3 hr) LO1 LO2 LO3
Week 11 High Frequency Financial Econometrics I Lecture (3 hr) LO1 LO2 LO3
Week 12 High Frequency Financial Econometrics II Lecture (3 hr) LO1 LO2 LO3
Week 13 Review Lecture (3 hr)  

Attendance and class requirements

  • Attendance: Students 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 which 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

The main textbook of this course is

Patton, A., 2019, Forecasting Financial Markets, Draft Book.

The PDF file of this draft book is posted on the University’s Learning Management System (Canvas).

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
No changes have been made since this unit was last offered


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