ECMT2130: Semester 1, 2021

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Units ECMT2130 Semester 1 2021 [Normal day]

Unit outline_

ECMT2130: Financial Econometrics

Semester 1, 2021 [Normal day] - Camperdown/Darlington, Sydney

Overview

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

Academic unit	Economics
Credit points	6
Prerequisites ?	ECMT2110 or ECMT2010 or ECMT1010 or MATH1005 or MATH1905 or DATA1001 or DATA1901 or ENVX1002
Corequisites ?	None
Prohibitions ?	ECMT2030
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Geoff Shuetrim, geoffrey.shuetrim@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Open book)	Final exam Covers all material in the course.	50%	Formal exam period	2 hours
Outcomes assessed:
In-semester test (Open book)	Midterm exam Covers the first material in the first 6 weeks of lectures and tutorials.	30%	Week 07 Due date: 19 Apr 2021 at 09:00	1 hour
Outcomes assessed:
Assignment	Applied analysis of time series data Applied work in R submitted via Canvas Quiz.	20%	Week 09 Due date: 16 May 2021 at 23:59	600 words
Outcomes assessed:
= Type C final 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	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

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.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Learning support

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.

Weekly schedule

WK	Topic	Learning activity
Week 01	Lecture will cover introduction and mathematical foundations. Tutorial will focus on installation of Excel and R software.	Lecture and tutorial (3 hr)
Week 02	The nature of financial returns	Lecture and tutorial (3 hr)
Week 03	Portfolio optimisation through diversification	Lecture and tutorial (3 hr)
Week 04	Classical Linear Regression Model	Lecture and tutorial (3 hr)
Week 05	Tests of the CAPM model	Lecture and tutorial (3 hr)
Week 06	Multifactor asset pricing models, principle component analysis	Lecture and tutorial (3 hr)
Week 07	Mid-semester exam during lecture timeslot. Tutorial as usual.	Lecture and tutorial (3 hr)
Week 08	Inference in the classical linear regression model	Lecture and tutorial (3 hr)
Week 09	Efficient market hypothesis, univariate time-series models, autocorrelation	Lecture and tutorial (3 hr)
Week 10	ARMA models	Lecture and tutorial (3 hr)
Week 11	Non-stationary data and ARIMA models	Lecture and tutorial (3 hr)
Week 12	Non-linear models and Autoregressive Conditional Heteroscedasticity (ARCH)	Lecture and tutorial (3 hr)
Week 13	Generalised Autoregressive Conditional Heteroscedasticity	Lecture and tutorial (3 hr)

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

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.

Outcomes
Graduate qualities

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
Learning outcomes

Responding to student feedback

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

The weight on the assignment has been increased to 20%

Additional information

Additional costs

There are no additional costs for this unit.

Disclaimer

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

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Additional costs

Disclaimer

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