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Unit of study_

QBUS6830: Financial Time Series and Forecasting

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.

Details

Academic unit Business Analytics
Unit code QBUS6830
Unit name Financial Time Series and Forecasting
Session, year
? 
Semester 2, 2021
Attendance mode Normal day
Location Remote
Credit points 6

Enrolment rules

Prohibitions
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None
Prerequisites
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ECMT5001 or QBUS5001
Corequisites
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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 and contact details

Coordinator Richard Gerlach, richard.gerlach@sydney.edu.au
Type Description Weight Due Length
Final exam (Take-home extended release) Type E final exam hurdle task Final exam
Written exam
40% Formal exam period 48 hours
Outcomes assessed: LO1 LO3 LO2
In-semester test (Record+) Type B in-semester exam Mid-semester exam
Written exam
20% Week 07
Due date: 25 Sep 2021 at 17:00
2 hours
Outcomes assessed: LO1 LO2 LO3
Assignment group assignment Group assignment
Quantitative data analysis and report
40% Week 13 30 pages
Outcomes assessed: LO1 LO2 LO3 LO4
hurdle task = hurdle task ?
group assignment = group assignment ?
Type B in-semester exam = Type B in-semester exam ?
Type E final exam = Type E final exam ?
  • 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 3-5 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 statistical 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. 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 sydney.edu.au/students/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.

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 1. Properties of financial data and review of statistics and probability; 2. Introduction to Python and financial return data Lecture and tutorial (3 hr) LO1 LO4
Week 02 Regression review and the CAPM and multi-factor models Lecture and tutorial (3 hr) LO2 LO4
Week 03 Regression, CAPM, and factor models (ctd) Lecture and tutorial (3 hr) LO2 LO3 LO4
Week 04 Forecasting, forecast accuracy, and introduction to time series Lecture and tutorial (3 hr) LO2 LO3 LO4
Week 05 Time series (ctd), AR, and MA models Lecture and tutorial (3 hr) LO2 LO3 LO4
Week 07 Forecasting with ARMA models and intro to volatility modelling Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4
Week 08 ARCH and GARCH volatility modelling Lecture and tutorial (3 hr) LO2 LO3
Week 09 GARCH (ctd), risk metrics, and volatility asymmetry Lecture and tutorial (3 hr) LO2 LO4
Week 10 Volatility forecasting and volatility proxies Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4
Week 11 Financial risk and its measurement Lecture and tutorial (3 hr) LO1 LO4
Week 12 Forecasting value at risk (VaR) Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4
Week 13 Forecasting tail risk, VaR, and expected shortfall (ES) Lecture and tutorial (3 hr) LO2 LO3 LO4

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 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. 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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9
No changes have been made since this unit was last offered.
  • Software: This unit will require coding and analysis using the software package Matlab, 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 Matlab, to complete the assignment.

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