Outline

Overview

Time series and statistical modelling is a fundamental component of the theory and practice of modern financial asset pricing as well as financial risk measurement and management. Further, forecasting is a required component of financial and investment decision making. This unit provides an introduction to the time series models used for the analysis of data arising in financial markets. It then considers methods for forecasting, testing and sensitivity analyses, in the context of these models. Topics include: the properties of financial return data; the Capital Asset Pricing Model (CAPM); financial return factor models, with known and unknown factors, in panel data settings; modelling and forecasting conditional volatility, via ARCH and GARCH; forecasting market risk measures such as Value at Risk. Emphasis is placed on applications involving the analysis of many real market datasets. Students are encouraged to undertake hands-on analysis using an appropriate computing package.

Unit details and rules

Academic unit	Business Analytics
Credit points	6
Prerequisites ?	ECMT5001 or QBUS5001
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Basic knowledge of quantitative methods including statistics, basic probability theory, and introductory regression analysis
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Richard Gerlach, richard.gerlach@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Take-home extended release)	Final exam Written exam	40%	Formal exam period	48 hours
Outcomes assessed:
In-semester test (Record+)	Mid-semester exam Written exam	20%	Week 07 Due date: 17 Sep 2022 at 17:00	2 hours
Outcomes assessed:
Assignment	Group assignment Quantitative data analysis and report	40%	Week 13	30 pages
Outcomes assessed:
= hurdle task ? = group assignment ? = Type B in-semester exam ? = Type E final exam ?

Assessment summary

Mid-semester exam: The exam will examine concepts covered in weeks 1-6 of this unit. The questions intend to measure students’ knowledge of major principles in financial time series and forecasting, and their ability to provide a complete description of their essential characteristics, as well as understand and interpret statistical output, as discussed in weeks 1-6 of this unit.
Group assignment: In groups of 3 members, students will be required to complete a two-part assignment, due in weeks 9 and 13 respectively. Students will perform an analytical exercise and quantitative analysis of a dataset. Students need to construct descriptive statistics and relevant statistical charts and tables, build and select appropriate time series models, estimate these and create forecasts, as well as draw appropriate conclusions. There will be a peer review and peer assessment component to assess everyone’s contribution to this assignment. This will lead to a mark out of 5 for each individual group member, reflecting their individual performance, effort and facilitation of group activity in this project.
Final exam: The final exam will assess all aspects of this unit from weeks 1-13, with emphasis on weeks 6-13. The questions intend to measure students’ knowledge of major principles in financial time series and forecasting and their ability to provide a complete description of their essential characteristics, as well as discuss and interpret Python statistical output.

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	Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Distinction	75 - 84	Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Credit	65 - 74	Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Pass	50 - 64	Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
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.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

The usual penalties apply for late assignments

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	1. Properties of financial data and review of statistics and probability; 2. Introduction to Python and financial return data	Lecture and tutorial (3 hr)
Week 02	Regression review and the CAPM and multi-factor models	Lecture and tutorial (3 hr)
Week 03	Regression, CAPM, and factor models (ctd)	Lecture and tutorial (3 hr)
Week 04	Forecasting, forecast accuracy, and introduction to time series: AR, and MA models	Lecture and tutorial (3 hr)
Week 05	Forecasting with ARMA models and introduction to volatility modelling	Lecture and tutorial (3 hr)
Week 07	ARCH and GARCH volatility modelling	Lecture and tutorial (3 hr)
Week 08	GARCH (ctd), Riskmetrics, and volatility asymmetry	Lecture and tutorial (3 hr)
Week 09	Volatility forecasting and volatility proxies	Lecture and tutorial (3 hr)
Week 10	Financial risk and its measurement; Forecasting value at risk (VaR) 1 day ahead	Lecture and tutorial (3 hr)
Week 11	Forecasting VaR and expected shortfall (ES) one day ahead	Lecture and tutorial (3 hr)
Week 12	Forecasting VaR and ES for multi-periods ahead	Lecture and tutorial (3 hr)
Week 13	Recent developments in tail risk forecasting	Lecture and tutorial (3 hr)

Attendance and class requirements

Lecture recordings: All lectures and seminars are recorded and will be available on Canvas for student use. Please note the Business School does not own the system and cannot guarantee that the system will operate or that every class will be recorded. Students should ensure they attend and participate in all classes.

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 through the Library eReserve, available on Canvas.

Tsay, R, (2010), Analysis of Financial Time Series, Wiley: New York. 3rd edition.

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. describe and summarise, with appropriate statistics, the empirical properties of financial prices and returns data
LO2. design and estimate of a range of quantitative, statistical models used by financial analysts and forecasters
LO3. appraise the suitability of both models and methods of forecasting financial data, financial quantities, and outcomes
LO4. develop complex programs in Python software for estimation of financial time series models and forecasting.

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.

Since this unit was offered in 2021, material on volatility models and tail risk forecasting has been brought forward one or two weeks so as to allow students more time to develop an understanding of these concepts and to complete their group assignment.

Additional information

Software: This unit will require coding and analysis using the software package Python, which is available in all computer labs in Building H69. It is each student's responsibility to obtain sufficient access to these labs, as well as sufficient proficiency in Python, to complete the assignment. More info will be given in week 1 classes.

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

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

QBUS6830: Financial Time Series and Forecasting

Semester 2, 2022 [Normal day] - Remote

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

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect