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Unit of study_

ECMT2130: Financial Econometrics

Semester 2, 2021 [Normal day] - Remote

Over the last decade econometric modelling of financial data has become an important part of the operations of merchant banks and major trading houses and a vibrant area of employment for econometricians. This unit provides an introduction to some of the widely used econometric models for financial data and the procedures used to estimate them. Special emphasis is placed upon empirical work and applied analysis of real market data. Topics covered may include the statistical characteristics of financial data, the specification, estimation and testing of asset pricing models, the analysis of high frequency financial data, and the modelling of volatility in financial returns.

Unit details and rules

Unit code ECMT2130
Academic unit Economics
Credit points 6
ECMT2110 or ECMT2010 or ECMT1010 or MATH1005 or MATH1905 or DATA1001 or DATA1901 or ENVX1002
Assumed knowledge


Available to study abroad and exchange students


Teaching staff

Coordinator Geoff Shuetrim,
Type Description Weight Due Length
Final exam (Open book) Type C final exam Final exam
Covers all material in the course.
50% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4
Assignment Tutorial assignments
10% of the final mark is based upon weekly assignment completion.
10% Multiple weeks Complete the weekly tutorial assignment.
Outcomes assessed: LO1 LO2 LO3 LO4
In-semester test (Open book) Type C in-semester exam Midterm exam
Examines understanding of the material presented in the first 7 weeks.
20% Week 08
Due date: 05 Oct 2021 at 10:00
1.5 hours
Outcomes assessed: LO1 LO2 LO3 LO4
Assignment hurdle task Applied analysis of time-series data
Applied work in R submitted via a Canvas Quiz.
20% Week 12
Due date: 07 Nov 2021 at 23:59

Closing date: 07 Nov 2021
600 words
Outcomes assessed: LO1 LO4 LO3 LO2
hurdle task = hurdle task ?
Type C final exam = Type C final exam ?
Type C in-semester exam = Type C in-semester 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 Lecture will cover introduction and mathematical foundations. Tutorial will focus on installation of Excel and R software. Lecture and tutorial (3 hr) LO2 LO3 LO4
Week 02 The nature of financial returns Lecture and tutorial (3 hr) LO3 LO4
Week 03 Portfolio risk and return and optimisation. Lecture and tutorial (3 hr) LO1 LO3
Week 04 Classical Linear Regression Model Lecture and tutorial (3 hr) LO2 LO3 LO4
Week 05 Inference in the classical linear regression model Lecture and tutorial (3 hr) LO2 LO4
Week 06 Tests of the CAPM model Lecture and tutorial (3 hr) LO1 LO3
Week 07 Arbitrage Pricing Theory and empirical multifactor asset pricing models. Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4
Week 08 Mid-semester exam during lecture timeslot. Tutorial as usual. Lecture and tutorial (3 hr)  
Week 09 Efficient market hypothesis, univariate time-series models, autocorrelation Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4
Week 10 ARMA models Lecture and tutorial (3 hr) LO2 LO3 LO4
Week 11 Non-stationary data and ARIMA models Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4
Week 12 Non-linear models and Autoregressive Conditional Heteroscedasticity (ARCH) Lecture and tutorial (3 hr) LO1 LO2 LO3
Week 13 Generalised Autoregressive Conditional Heteroscedasticity Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4

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: Lectures will be recorded and made available to students on Canvas.
  • 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 recommended finance text is Chris Brooks, Introductory Econometrics for Finance, Cambridge.

Zvi Bodie, Alex Kane and Alan J. Marcus, Investments, ISE is used as an auxiliary reference for finance theory. Several copies are available electronically from the university library.

Wooldridge, Introductory Econometrics, A Modern Approach, is an auxiliary reference for linear regression.

A complete list of readings for this unit can be accessed in 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. demonstrate an understanding of the basic principles and theories of financial economics
  • LO2. analyse and interpret financial data from diverse sources using economic and econometric models
  • LO3. select and utilise relevant techniques and principles to analyse risk and return characteristics of financial time-series data
  • LO4. compile and present relevant commercial information to decision-makers using appropriate data management and IT tools.

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.

Completion of the weekly assignments is now assessable and worth 10% of the final mark.

Additional costs

There are no additional costs for this unit.


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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