Outline

Overview

Syllabus covers methods involved in quantifying and measuring risk. Risk measurement techniques; Risk factors; Linear and nonlinear risks; Volatility; Scenario analysis; Stress testing; Value at risk (VAR) frameworks and their limitations. Comparison will be made to real word outcomes using case studies. The handling of "unknown unknowns" and how to incorporate these into a risk analysis will be investigated. An introduction to common financial instruments will be presented.

Unit details and rules

Academic unit	Aerospace, Mechanical and Mechatronic
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Rod Fiford, rod.fiford@sydney.edu.au
Lecturer(s)	Roger Cohen, roger.cohen@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Take-home extended release)	Final exam Take home 48 hour exam	30%	Formal exam period	48 hours
Outcomes assessed:
Participation	Active class participation Students are expected to actively participate in class discussions	15%	Ongoing	N/A
Outcomes assessed:
Assignment	Assignment 1 Assignment 1	10%	Week 04 Due date: 28 Mar 2021 at 23:59	2-4 pages
Outcomes assessed:
Assignment	Project preliminary report Project preliminary report	5%	Week 07 Due date: 25 Apr 2021 at 23:59	2-4 pages
Outcomes assessed:
Assignment	Assignment 2 Assignment 2	10%	Week 08 Due date: 02 May 2021 at 23:59	2-4 pages
Outcomes assessed:
Assignment	Assignment 3 Assignment 3	10%	Week 12 Due date: 30 May 2021 at 23:59	2-4 pages
Outcomes assessed:
Presentation	Project presentations Project final presentations	10%	Week 13 Due date: 10 May 2021 at 14:00	8-10 minutes
Outcomes assessed:
Assignment	Project final report Project final report	10%	Week 13 Due date: 06 Jun 2021 at 23:59	8-20 pages
Outcomes assessed:
= group assignment ? = Type E final exam ?

Assessment summary

Students will discuss and analyse a variety of case studies and scenarios as part of their weekly sessions. Assignments/presentations will consist of a combination of individual and group work.

The Final Exam will be calculation and short answer based.

Assessment criteria

Final grades in this unit are awarded at levels of HD for High Distinction, DI (previously D) for Distinction, CR for Credit, PS (previously P) for Pass and FA (previously F) for Fail as defined by University of Sydney Assessment Policy. Details of the Assessment Policy are available on the Policies website at http://sydney.edu.au/policies . Standards for grades in individual assessment tasks and the summative method for obtaining a final mark in the unit will be set out in a marking guide supplied by the unit coordinator.

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:

5% per day or part thereof.

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
Ongoing	Students are required to undertake independent learning and study	Independent study (90 hr)
Week 01	Note - content and timings may vary as we are reliant on commitment from external practitioners. Course introduction, context and background. Overview of business and financial risk. How financial and business risk relate to engineering	Lecture and tutorial (3 hr)
Week 02	Tutorial exercise TBA	Tutorial (3 hr)
Week 03	Introduction to financial instruments. Valuation, volatility and hedging. Introduction to risk appetite and your personal attitude to risk. Behaviour and risk taking; Preparation for group work	Lecture and tutorial (3 hr)
Week 04	External industry practitioner (tbc) - Business Risk Management. Equities, bonds and futures; Finalising group work topics for approval	Lecture and tutorial (3 hr)
Week 05	Risk as an engineering system. Quantifying. risks. Stress testing	Lecture and tutorial (3 hr)
Week 06	Risk as an engineering system, quantifying, risks, Value at risk (VAR), stress testing, hedging	Lecture and tutorial (3 hr)
Week 07	Asset liability management, the treasury function, foreign exchange	Lecture and tutorial (3 hr)
Week 08	Industry sectors 1	Lecture and tutorial (3 hr)
Week 09	Industry sectors 2; External industry practitioner - trading/dealing room risk	Lecture and tutorial (3 hr)
Week 10	Industry practitioner TBC	Lecture and tutorial (3 hr)
Week 11	Insurance. Hedging and managing risk.	Lecture and tutorial (3 hr)
Week 12	Case studies.	Lecture and tutorial (3 hr)
Week 13	Revision and exam preparation.	Lecture and tutorial (3 hr)

Attendance and class requirements

Students are required to attend and actively participate in class sessions.

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.

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. determine acceptable levels of financial risk and ways to manage or mitigate where unacceptable
LO2. complete and properly document a group project, partake in group discussions and be able to articulate and present your work and generate and answer questions from the audience
LO3. understand industry practice around risk quantification
LO4. perform calculations to quantify risk under various scenarios.

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.

Minor modifications to assessment task weightings

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

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

AMME5105: Risk Management Analysis

Semester 1, 2021 [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

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links