ECON3206 Financial Econometrics - 2020

ECON3206
Undergraduate
Term 2
6 Units of Credit
Online
Economics
This course outline is for the current semester. To view outlines from other years and/or semesters, visit the archives

1. Course Details

Summary of Course

This course is concerned with the special statistical concerns that arise when modelling time series data, such as commodity/asset prices, stock market returns, interest rates or exchange rates. Topics include key characteristics of financial data, concepts of volatility and risk, modelling time varying volatility (ARCH models), and modelling relationships among financial series. The knowledge and methods acquired in this course are particularly useful and sought after in the public (government) and private (industry) financial sectors.

Teaching Times and Locations

Please note that teaching times and locations are subject to change. Students are strongly advised to refer to the Class Timetable website for the most up-to-date teaching times and locations.

View course timetable

Course Policies & Support

Course Aims and Relationship to Other Courses

The course aims to provide students with the basic framework for modelling financial time series data. In particular, it will benefit students in terms of:

  1. Developing their ability to model the expected mean and volatility in financial data as a means to a more informed assessment of the risk and return associated with different investment strategies;
  2. Developing an awareness of the empirical evidence supporting alternative models of asset price determination;
  3. Developing their proficiency with the computer skills required to actually model financial data in practice. By the end of the course, students should be proficient in a software package commonly used to analyse financial data.

This course is offered as part of the economics stream in the BCom and BEc degrees (ECON3206) and MCom degree (ECON5206).

A prerequisite for ECON3206 is ECON2209 Business Forecasting. A prerequisite for ECON5206 is ECON5248 Business Forecasting.

A good grasp of basic mathematical statistics and linear algebra is necessary for surviving the course. Some familiarity with real analysis would make life easier for understanding the technical details, but is not required. A previous course in time series is not required or assumed. However, a basic knowledge of estimation and inference in linear regression models will be assumed.

2. Staff Contact Details

Position Title Name Email Location Phone Consultation Times
Lecturer-in-chargeDrRachida OuysseRoom 441, UNSW Business School9385 3321Mon 10:30 am-12:30 pm (or by appointment)

Communication with staff

You should feel free to contact your lecturer(s) about any academic matter. However, it is strongly encouraged, for efficiency, that all enquiries about the subject material be made at lecture forums or tutorials or during online consultation time. Discussion of course subject material will not be entered into via lengthy emails.

You should expect responses to email correspondence within 48 hours, but not over weekends. Before communicating with staff, please check relevant components of this course outline as this will provide answers to most common questions. You should also regularly check the course website for announcements and reminders about upcoming events and deadlines. Please note that the lecturer has no advance notice of the date and time of the final exam.

Student Enrolment Requests

Students can vary their own enrolment (including switching lecture streams or tutorials) via myUNSW until the end of Week 1. In general, most other student enrolment requests should be directed to The Nucleus: Student Hub (formerly Student Central). These include enrolment in full courses or tutorials, course timetable clashes, waiving prerequisites for any course, transfer-of-credit (international exchange, transfer to UNSW, cross-institutional study, etc.), or any other request which requires a decision about equivalence of courses and late enrolment for any course. Where appropriate, the request will be passed to the School Office for approval before processing. Note that enrolment changes are rarely considered after Week 2 classes have taken place.

3. Learning and Teaching Activities

Approach to Learning and Teaching in the Course

​The philosophy underpinning this course and its teaching and learning strategies is based on the “Guidelines on Learning that Inform Teaching at UNSW”, which may be viewed at: https://teaching.unsw.edu.au/guidelines. Specifically, the lectures, tutorials and assessment have been designed to appropriately challenge students and support the achievement of the desired learning outcomes. A climate of inquiry and dialogue is encouraged between students and teachers and among students (in and out of class). The lecturer-in-charge and tutors aim to provide meaningful and timely feedback to students to improve learning outcomes.

This is not a course where you can become proficient just by observing. You will need to get involved in class activities - evaluating information, asking and answering questions. You also must learn to organise your independent study and practice enough problems to gain a thorough understanding of concepts and how to apply them. You must get your hands dirty and learn by doing.

Students are expected to:

  • Put a consistent effort into learning activities throughout the term by preparing for the regular tutorial tasks;
  • Take a responsible role in participating in tutorials;
  • Develop communication skills through engaging in discussion forums and preparing for video presentation and project milestones;
  • Concentrate more on understanding how and why to use formulas, and less on memorising them.

Learning Activities and Teaching Strategies

The material given in the lecture schedule, the content of the lectures, and the content of the tutorial program define the examinable content of the course.

Lectures

Lectures will be delivered online. These lectures will mainly be in the form of synchronous video presentations conducted during the scheduled lectures times. These live delivery lectures will allow you to raise your hand and ask while we progress with the concepts. If appropriate, content in some weeks may be delivered via short concept videos. All information about lecture delivery and platforms used will be available on Moodle.

The purpose of lectures is to provide a logical structure for the topics that make up the course, to emphasise the important concepts and methods of each topic, and to provide relevant examples to which the concepts and methods are applied.

Not all of the material in the textbook is included in the lectures, and not all of the material in the lectures is covered in the textbook. The lectures contain all the course material taught at the level required for the assessment tasks, and are your guide to the course content.

As not all topics will be presented extensively, students should refer to the textbook and relevant readings for further details.

This is a lecture-based course, which will proceed as quickly or slowly as is required by the complexity of the content and students' needs.

Class attendance is very important for understanding the lecture notes. Students are expected to develop the skills and ability to derive the results on their own. Memorizing formulae and final results is not a learning outcome we seek for this course; assessments in the course reflect real-life scenarios and only an ability to develop and understand these results will ensure success.

Tutorials

Tutorials will be delivered live synchronously during the scheduled times for each session. Please make every effort to attend your online session. The purpose of the tutorial program is to enable you to raise questions about difficult topics or problems encountered in your studies. You must not expect another lecture, but must come prepared with informed questions of your own. Tutorials afford you the opportunity to send through your questions, raise your hand and have your unanswered questions addressed by your tutor.

The more you read the more you know, but the more you practice the more you learn and understand. Accordingly, the key to the understanding of this course is problem solving. Tutorial discussion will normally be based on a sequence of exercise sheets that will be distributed regularly during the course. You are expected to make a serious attempt at all questions on an exercise sheet before attending your tutorial session. It will not be possible to discuss all the problems set in the allotted time, and you should not expect all questions to be solved in depth at the tutorials. Some tutorial exercises will require the use of statistical software (e.g., Eviews, Python) to undertake estimation of financial models and analysis of the data.

In tutorials, some students will be randomly chosen to discuss their attempts to answer the tutorial problems. The aim of these discussions is to encourage discussion within the classroom and to solve the issues you and your classmates have encountered with the problems.

Out-of-Class Study

All activities and resources necessary for you to complete the necessary out-of-class study are available online.

While students may have preferred individual learning strategies, most learning will be achieved outside of class time. Lectures can only provide a structure to assist your study, and tutorial time is limited.

A recommended strategy (on which the provision of the course materials is based) would consist of:

  • Reading of the relevant chapter(s) of the text/notes/slides and other required material (if any) before the lecture. This will give you a general idea of the topic area;
  • Attending lectures, where the context of the topic in the course and the important elements of the topic are identified, and the relevance of the topic is explained;
  • Attending tutorials, having attempted the tutorial questions beforehand.

5. Course Resources

The website for this course is on UNSW Moodle.

Lecture notes, lecture slides and tutorial questions, with additional readings, will be posted on the Moodle website. Lecture notes provide concise description of lecture material, but cannot be used as a substitute for the textbook and assigned readings.

Textbook

The main textbook for the subject is:

  • Brooks, Chris, Introductory Econometrics for Finance, Cambridge University Press. Third (2014) or Fourth (2019) Edition.

This book is recommended, but it is not mandatory. The book is written at an introductory level and covers most of the material we will discuss in class.

Additional Useful References

  • [Ait-Sahalia] Handbook of Financial Econometrics Volume 1 by Yacine Ait-Sahalia and Lars Peter Hansen ISBN-13: 978-0444508973
  • [BBL] Breitung, J., Bruggemann, R. and H. Lutkepohl, 2004, "Structural Vector
  • [Diebold] Forecasting in Economics, Business, Finance and Beyond’, by Francis X. Diebold, Edition 2015, available for free download at http://www.ssc.upenn.edu/~fdiebold/Teaching221/Forecasting.pdf
  • [Johnston] Jack Johnston and John Dinardo, Econometric Methods (fourth edition), McGraw-Hill, 1997. [Johnston]
  • Autoregressive Modeling and Impulse Responses", in Applied Time Series Econometrics, Lutkepohl, H. and M. Kratzig (eds.), Chapter 4.
  • [Enders] Enders, W., 2010, Applied Time Series Analysis (third edition), Wiley,
  • [Gujarati] Gujarati, D.N., and D.C. Porter, 2009, Basic Econometrics (5fth edition), McGraw-Hill,
  • [Lutkepohl] Lutkepohl, H., 2004, "Vector Autoregressive and Vector Error Correction Models", in Applied Time Series Econometrics, Lutkepohl, H. and M. Kratzig (eds.)
  • [Verbeek] Verbeek, M., 2012, A Guide to Modern Econometrics (fourth edition), John Wiley & Sons
  • Campbell, J.Y., A.W. Lo, and A.C. MacKinlay (1997). The Econometrics of Financial Markets. Princeton University Press.
  • Tsay, Ruey S. (2002), Analysis of Financial Time Series, John Willey & Sons.

Journal Articles
These journal articles are strongly recommended for postgraduate students (ECON5206):

  • Berndt, E., Hall, B., Hall, R. & Hausman, J. (1974), `Estimation and inference in nonlinear structural models', Annals of Economic and Social Measurement 3/4, 653-665;
  • Bollerslev, T. (1986), `Generalized autoregressive conditional heteroskedasticity', Journal of Econometrics 31, 307-327;
  • Cont, R., (2001) Empirical properties of asset returns: stylized facts and statistical issues, Quantitative Finance 1, 223–236;
  • Diebold, F. X. & Mariano, R. S. (1995), `Comparing predictive accuracy', Journal of Business and Economic Statistics 13(3), 253-263;
  • Engle, R.F., (2001) GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics, Journal of Economic Perspectives, 15(4), 157-168;
  • Kunze, F. Predicting exchange rates in Asia: New insights on the accuracy of survey forecasts. Journal of Forecasting. 2020; 39: 313– 333. https://doi.org/10.1002/for.2628
  • Lee T.-H. & Bao Y., Saltoglu B., (2007) Comparing density forecast models, Journal of Forecasting, 26(3), 203-225;
  • Engle (1982), `Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation', Econometrica 50, 987-1007;
  • Giacomini, R. & White, H. (2006), `Tests of conditional predictive ability', Econometrica 74(6), 1545-1578;
  • Ng, S. & Perron, P. (2005), `A note on the selection of time series models', Oxford Bulletin Of Economics And Statistics 67, 115-134.
  • Sharpe, W.F., (1991) Capital Asset Prices with and without Negative Holdings, Journal of Finance, 46(2), 489-509;
  • Sharpe, W.F. (1965), RISK‐AVERSION IN THE STOCK MARKET: SOME EMPIRICAL EVIDENCE. The Journal of Finance, 20: 416-422. doi:10.1111/j.1540-6261.1965.tb02906.x

COMPUTING

An essential component of this course is learning to apply financial analysis tools to real financial data. You will use software to execute all the tasks needed for the tutorial problems and for your course project.

There are many statistical software packages that are suitable for the analysis of time-series data in general, and financial data in particular. In this course we aim to equip students with strong computing skills by the completion of the term. We therefore advocate for learning and using a top-of-league software like Python. This is an innovation in this course from Term 2 2020. Python is one of the leading software packages used in industry and financial companies.

The use of Python is compulsory for our postgraduate student cohort of ECON5206.

For our undergraduate cohort of ECON3206, if you have invested in another software like R, Stata or Eviews, you may choose to use it, but you are strongly advised to achieve familiarity with Python.

We will provide support for Eviews and Python in giving sample codes for the tutorial questions.

If you plan to use Eviews, please check your access via MyAccess.

Python

6. Course Evaluation & Development

Feedback is regularly sought from students and continual improvements are made based on this feedback. At the end of this course, you will be asked to complete the myExperience survey, which provides a key source of student evaluative feedback. Your input into this quality enhancement process is extremely valuable in assisting us to meet the needs of our students and provide an effective and enriching learning experience. The results of all surveys are carefully considered and do lead to action towards enhancing educational quality.

​The School of Economics strives to be responsive to student feedback. If you would like more information on how the design of this course and changes made to it over time have taken students’ needs and preferences into account, please contact the Director of Education at the School of Economics.

The LIC takes students' feedback very seriously and welcomes every constructive comment. The course continues to adapt and change to meet students' expectations and aspirations.

There are two main changes bring undertaken this term to address some of the concerns our students have had in the past.

First, one reoccurring concern has to do with teamwork. It is very hard to monitor the workload within a group and to acknowledge the respective contributions of each team member. This term, we are trialing teamwork discussion forums. In these forums, each team member will contribute to specific tasks related to the project. It is also a place to communicate/discuss some of the project aspects with the team.

Second, we are introducing Python as the primary computing software for the course. This decision comes after some students in the past advocated for its use and/or asked why Python was not learned within this course.

7. Course Schedule

Note: for more information on the UNSW academic calendar and key dates including study period, exam, supplementary exam and result release, please visit: https://student.unsw.edu.au/new-calendar-dates
Week Activity Topic Assessment/Other
Week 1: 1 JuneLecture only

Topic 1 - Introduction

Topic 2 - Linear regression Model and Financial Data

Understanding Financial Data - Brooks Ch.1 + lecture notes

Basic statistical and mathematical concepts - Brooks Ch.2 + lecture notes

Brooks Ch.3 & 5 + lecture notes

Week 2: 8 JuneLecture

Monday: Public Holiday

Topic 2 - Linear regression Model and Financial Data (continued)

 

 

Basic statistical and mathematical concepts - Brooks Ch.2 + lecture notes

Brooks Ch.3 & 5 + lecture notes

 

 

Tutorial

Tutorial questions based on the Week 1 lecture material

Week 3: 15 JuneLecture

Topic 3 - Univariate Time Series Analysis:

  • Estimation
  • Forecasting with ARMA

 

Building ARMA models- Brooks Ch.6 (6.3-6.7, 6.11-6.12) + lecture notes

Estimation of ARMA models- Brooks Ch.6 (6.8) + lecture notes

Additional References Enders Ch.2; Gujarati Ch. 21 & 22; Hamilton Ch. 3&4; Johnston Ch. 7

Forecasting with ARMA models-Brooks Ch.6 (6.11-6.12) + lecture notes

ML Estimation- Brooks Ch.9.9 + lecture notes

Additional References Enders Ch.2; Gujarati Ch. 21&22; Hamilton Ch. 3&4; Johnston Ch. 7

Tutorial

Tutorial questions based on the Week 2 lecture material

Online Quiz

Complete Online Quiz 1: Lecture material from Weeks 1 and 2

See details on Moodle.

Week 4: 22 JuneLecture

Topic 3 (continued) - Univariate Time Series Analysis: Wrap up

Topic 4 - Non-stationary Time Series I

Deterministic and stochastic non-stationarity - Brooks Ch. 8 (8.1-8.3) + lecture notes

Additional References Enders Ch 4; Gujarati Ch. 21

Tutorial

Tutorial questions based on the Week 3 lecture material

Week 5: 29 JuneLecture

Topic 4 (continued) Nonstationary Time Series II

Unit Root- Brooks Ch. 8 (8.1-8.3)+ lecture notes

Additional References Enders Ch 4; Gujarati Ch. 21

Tutorial

Tutorial questions based on the Week 4 lecture material

Week 6: FLEXIBILITY WEEK: 6 July

 

NO LECTURES/TUTORIALS

 

Week 7: 13 JulyLecture

Topic 5 - Long-run relationships: Cointegration and error correction models

 

Cointegration Analysis- Brooks Ch. 8.4 + lecture notes

Error Correction models- Brooks Ch. 8 (8.5-8.7) + lecture notes

Additional References Enders Ch 4; Gujarati Ch. 21

Tutorial

Tutorial questions based on the Week 5 lecture material

Week 8: 20 JulyLecture

Topic 6 - Risk and volatility Analysis: ARCH/GARCH/EGARCH/GJR

 

ARCH/GARCH- Brooks Ch. 9 (9.2-9.10) + lecture notes

EGARCH- Brooks Ch. 9.13 + lecture notes

Topic’s Additional References Enders Ch. 3; Gujarati Ch. 22; Verbee Ch. 8

Tutorial

Tutorial questions based on the Week 7 lecture material

Online Quiz II

Complete Online Quiz II: Material covered from Week 1 to Week 7 inclusive

See details on Moodle

Week 9: 27 JulyLecture

Topic 6 (continued) - Risk and volatility Analysis: ARCH/GARCH/EGARCH/GJR

GJR/GARCH in mean- Brooks Ch. 9 (9.12-9.15) + lecture notes

Stochastic Volatility- Brooks Ch. 9.20 + lecture notes

Tutorial

Tutorial questions based on the Week 8 lecture material

Video Assessment

Video presentation to be submitted this week.

Due by 11.59 pm on Friday 31st July (see details on Moodle)

Week 10: 3 AugustTutorial only

Tutorial questions based on the Week 9 lecture material

Group Project due by 11.59 pm on Friday 7th August (see details on Moodle)

8. Policies and Support

Information about UNSW Business School protocols, University policies, student responsibilities and education quality and support.

Program Learning Outcomes

The Business School places knowledge and capabilities at the core of its curriculum via seven Program Learning Outcomes (PLOs). These PLOs are systematically embedded and developed across the duration of all coursework programs in the Business School.

PLOs embody the knowledge, skills and capabilities that are taught, practised and assessed within each Business School program. They articulate what you should know and be able to do upon successful completion of your degree.

Upon graduation, you should have a high level of specialised business knowledge and capacity for responsible business thinking, underpinned by ethical professional practice. You should be able to harness, manage and communicate business information effectively and work collaboratively with others. You should be an experienced problem-solver and critical thinker, with a global perspective, cultural competence and the potential for innovative leadership.

All UNSW programs and courses are designed to assess the attainment of program and/or course level learning outcomes, as required by the UNSW Assessment Design Procedure. It is important that you become familiar with the Business School PLOs, as they constitute the framework which informs and shapes the components and assessments of the courses within your program of study.

PLO 1: Business knowledge

Students will make informed and effective selection and application of knowledge in a discipline or profession, in the contexts of local and global business.

PLO 2: Problem solving

Students will define and address business problems, and propose effective evidence-based solutions, through the application of rigorous analysis and critical thinking.

PLO 3: Business communication

Students will harness, manage and communicate business information effectively using multiple forms of communication across different channels.

PLO 4: Teamwork

Students will interact and collaborate effectively with others to achieve a common business purpose or fulfil a common business project, and reflect critically on the process and the outcomes.

PLO 5: Responsible business practice

Students will develop and be committed to responsible business thinking and approaches, which are underpinned by ethical professional practice and sustainability considerations.

PLO 6: Global and cultural competence

Students will be aware of business systems in the wider world and actively committed to recognise and respect the cultural norms, beliefs and values of others, and will apply this knowledge to interact, communicate and work effectively in diverse environments.

PLO 7: Leadership development

Students will develop the capacity to take initiative, encourage forward thinking and bring about innovation, while effectively influencing others to achieve desired results.

These PLOs relate to undergraduate and postgraduate coursework programs.  Separate PLOs for honours and postgraduate research programs are included under 'Related Documents'.

Business School course outlines provide detailed information for students on how the course learning outcomes, learning activities, and assessment/s contribute to the development of Program Learning Outcomes.

RELATED DOCUMENTS

 

UNSW Graduate Capabilities

The Business School PLOs also incorporate UNSW graduate capabilities, a set of generic abilities and skills that all students are expected to achieve by graduation. These capabilities articulate the University’s institutional values, as well as future employer expectations.

UNSW Graduate CapabilitiesBusiness School PLOs
Scholars capable of independent and collaborative enquiry, rigorous in their analysis, critique and reflection, and able to innovate by applying their knowledge and skills to the solution of novel as well as routine problems.
  • PLO 1: Business knowledge
  • PLO 2: Problem solving
  • PLO 3: Business communication
  • PLO 4: Teamwork
  • PLO 7: Leadership development

Entrepreneurial leaders capable of initiating and embracing innovation and change, as well as engaging and enabling others to contribute to change
  • PLO 1: Business knowledge
  • PLO 2: Problem solving
  • PLO 3: Business communication
  • PLO 4: Teamwork
  • PLO 6: Global and cultural competence
  • PLO 7: Leadership development

Professionals capable of ethical, self-directed practice and independent lifelong learning
  • PLO 1: Business knowledge
  • PLO 2: Problem solving
  • PLO 3: Business communication
  • PLO 5: Responsible business practice

Global citizens who are culturally adept and capable of respecting diversity and acting in a socially just and responsible way.
  • PLO 1: Business knowledge
  • PLO 2: Problem solving
  • PLO 3: Business communication
  • PLO 4: Teamwork
  • PLO 5: Responsible business practice
  • PLO 6: Global and cultural competence

While our programs are designed to provide coverage of all PLOs and graduate capabilities, they also provide you with a great deal of choice and flexibility.  The Business School strongly advises you to choose a range of courses that assist your development against the seven PLOs and four graduate capabilities, and to keep a record of your achievements as part of your portfolio. You can use a portfolio as evidence in employment applications as well as a reference for work or further study. For support with selecting your courses contact the UNSW Business School Student Centre.




Academic Integrity and Plagiarism

Academic Integrity is honest and responsible scholarship. This form of ethical scholarship is highly valued at UNSW. Terms like Academic Integrity, misconduct, referencing, conventions, plagiarism, academic practices, citations and evidence based learning are all considered basic concepts that successful university students understand. Learning how to communicate original ideas, refer sources, work independently, and report results accurately and honestly are skills that you will be able to carry beyond your studies.

The definition of academic misconduct is broad. It covers practices such as cheating, copying and using another person’s work without appropriate acknowledgement. Incidents of academic misconduct may have serious consequences for students.

Plagiarism

UNSW regards plagiarism as a form of academic misconduct. UNSW has very strict rules regarding plagiarism. Plagiarism at UNSW is using the words or ideas of others and passing them off as your own. All Schools in the Business School have a Student Ethics Officer who will investigate incidents of plagiarism and may result in a student’s name being placed on the Plagiarism and Student Misconduct Registers.

Below are examples of plagiarism including self-plagiarism:

Copying: Using the same or very similar words to the original text or idea without acknowledging the source or using quotation marks. This includes copying materials, ideas or concepts from a book, article, report or other written document, presentation, composition, artwork, design, drawing, circuitry, computer program or software, website, internet, other electronic resource, or another person's assignment, without appropriate acknowledgement of authorship.

Inappropriate Paraphrasing: Changing a few words and phrases while mostly retaining the original structure and/or progression of ideas of the original, and information without acknowledgement. This also applies in presentations where someone paraphrases another’s ideas or words without credit and to piecing together quotes and paraphrases into a new whole, without appropriate referencing.

Collusion: Presenting work as independent work when it has been produced in whole or part in collusion with other people. Collusion includes:

  • Students providing their work to another student before the due date, or for the purpose of them plagiarising at any time
  • Paying another person to perform an academic task and passing it off as your own
  • Stealing or acquiring another person’s academic work and copying it
  • Offering to complete another person’s work or seeking payment for completing academic work

Collusion should not be confused with academic collaboration (i.e., shared contribution towards a group task).

Inappropriate Citation: Citing sources which have not been read, without acknowledging the 'secondary' source from which knowledge of them has been obtained.

Self-Plagiarism: ‘Self-plagiarism’ occurs where an author republishes their own previously written work and presents it as new findings without referencing the earlier work, either in its entirety or partially. Self-plagiarism is also referred to as 'recycling', 'duplication', or 'multiple submissions of research findings' without disclosure. In the student context, self-plagiarism includes re-using parts of, or all of, a body of work that has already been submitted for assessment without proper citation.

To see if you understand plagiarism, do this short quiz: https://student.unsw.edu.au/plagiarism-quiz

Cheating

The University also regards cheating as a form of academic misconduct. Cheating is knowingly submitting the work of others as their own and includes contract cheating (work produced by an external agent or third party that is submitted under the pretences of being a student’s original piece of work). Cheating is not acceptable at UNSW.

If you need to revise or clarify any terms associated with academic integrity you should explore the 'Working with Academic Integrity' self-paced lessons available at: https://student.unsw.edu.au/aim.

For UNSW policies, penalties, and information to help you avoid plagiarism see: https://student.unsw.edu.au/plagiarism as well as the guidelines in the online ELISE tutorials for all new UNSW students: http://subjectguides.library.unsw.edu.au/elise. For information on student conduct see: https://student.unsw.edu.au/conduct.

For information on how to acknowledge your sources and reference correctly, see: https://student.unsw.edu.au/referencing. If you are unsure what referencing style to use in this course, you should ask the lecturer in charge.


Student Responsibilities and Conduct

​Students are expected to be familiar with and adhere to university policies in relation to class attendance and general conduct and behaviour, including maintaining a safe, respectful environment; and to understand their obligations in relation to workload, assessment and keeping informed.

Information and policies on these topics can be found on the 'Managing your Program' website.

Workload

It is expected that you will spend at least ten to twelve hours per week studying for a course except for Summer Term courses which have a minimum weekly workload of twenty to twenty four hours. This time should be made up of reading, research, working on exercises and problems, online activities and attending classes. In periods where you need to complete assignments or prepare for examinations, the workload may be greater. Over-commitment has been a cause of failure for many students. You should take the required workload into account when planning how to balance study with employment and other activities.

We strongly encourage you to connect with your Moodle course websites in the first week of semester. Local and international research indicates that students who engage early and often with their course website are more likely to pass their course.

View more information on expected workload

Attendance

Your regular and punctual attendance at lectures and seminars or in online learning activities is expected in this course. The Business School reserves the right to refuse final assessment to those students who attend less than 80% of scheduled classes where attendance and participation is required as part of the learning process (e.g., tutorials, flipped classroom sessions, seminars, labs, etc.).

View more information on attendance

General Conduct and Behaviour

You are expected to conduct yourself with consideration and respect for the needs of your fellow students and teaching staff. Conduct which unduly disrupts or interferes with a class, such as ringing or talking on mobile phones, is not acceptable and students may be asked to leave the class.

View more information on student conduct

Health and Safety

UNSW Policy requires each person to work safely and responsibly, in order to avoid personal injury and to protect the safety of others.

View more information on Health and Safety

Keeping Informed

You should take note of all announcements made in lectures, tutorials or on the course web site. From time to time, the University will send important announcements to your university e-mail address without providing you with a paper copy. You will be deemed to have received this information. It is also your responsibility to keep the University informed of all changes to your contact details.



Student Support and Resources

​The University and the Business School provide a wide range of support services and resources for students, including:

Business School EQS Consultation Program
The Consultation Program offers academic writing, literacy and numeracy consultations, study skills, exam preparation for Business students. Services include workshops, online resources, individual and group consultations. 
Level 1, Room 1035, Quadrangle Building.
BUS.EQS.Consultations@unsw.edu.au
02 9385 4508

Communication Resources
The Business School Communication and Academic Support programs provide online modules, communication workshops and additional online resources to assist you in developing your academic writing.

Business School Student Centre
The Business School Student Centre provides advice and direction on all aspects of admission, enrolment and graduation.
Level 1, Room 1028 in the Quadrangle Building
02 9385 3189

UNSW Learning & Careers Hub
The UNSW Learning & Careers Hub provides academic skills and careers support services—including workshops, individual consultations and a range of online resources—for all UNSW students. See their website for details.
Lower Ground Floor, North Wing Chancellery Building.
learningcentre@unsw.edu.au
02 9385 2060

Student Support Advisors
Student Support Advisors work with all students to promote the development of skills needed to succeed at university, whilst also providing personal support throughout the process.
John Goodsell Building, Ground Floor.
advisors@unsw.edu.au
02 9385 4734

International Student Support
The International Student Experience Unit (ISEU) is the first point of contact for international students. ISEU staff are always here to help with personalised advice and information about all aspects of university life and life in Australia.
Advisors can support you with your student visa, health and wellbeing, making friends, accommodation and academic performance.
International.student@unsw.edu.au
02 9385 4734

Equitable Learning Services
Equitable Learning Services (formerly Disability Support Services) is a free and confidential service that provides practical support to ensure that your health condition doesn't adversely affect your studies. Register with the service to receive educational adjustments.
Ground Floor, John Goodsell Building.
els@unsw.edu.au
02 9385 4734

UNSW Counselling and Psychological Services
Provides support and services if you need help with your personal life, getting your academic life back on track or just want to know how to stay safe, including free, confidential counselling.
Level 2, East Wing, Quadrangle Building.
counselling@unsw.edu.au
02 9385 5418

Library services and facilities for students
The UNSW Library offers a range of collections, services and facilities both on-campus and online.
Main Library, F21.
02 9385 2650

Moodle eLearning Support
Moodle is the University’s learning management system. You should ensure that you log into Moodle regularly.
externalteltsupport@unsw.edu.au
02 9385 3331

UNSW IT
UNSW IT provides support and services for students such as password access, email services, wireless services and technical support.
UNSW Library Annexe (Ground floor).
02 9385 1333



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