• Class Number 4882
  • Term Code 3030
  • Class Info
  • Unit Value 6 units
  • Mode of Delivery Online
    • Dr Timothy Higgins
    • Dr Timothy Higgins
  • Class Dates
  • Class Start Date 24/02/2020
  • Class End Date 05/06/2020
  • Census Date 08/05/2020
  • Last Date to Enrol 02/03/2020
SELT Survey Results

This course introduces techniques for risk modelling, with an emphasis on application to insurance portfolios and financial risks.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

Upon successful completion of the requirements for this course, students will be able to:
  1. Describe the properties of loss distributions in a variety of contexts, and estimate individual and compound loss distributions, both with and without risk sharing arrangements.
  2. Describe and utilise extreme value distributions to model severity of loss.
  3. Describe and apply a variety of copulas to model financial risks.
  4. Explain the concepts underlying time series models, and apply the concepts to the modelling and forecasting of financial time series.
  5. Explain and apply principles of machine learning.

Research-Led Teaching

The course convener has 25 years of professional practice and has undertaken research in statistical and actuarial topic areas. Lectures in the course will be informed by practical examples.

Examination Material or equipment

The fiinal examination will be a closed book exam. Students will be permitted to bring in a non-programmable calculator and an unmarked paper based english language dictionary. A formula sheet will be provided for the exam no later than week 12 of the semester.

Required Resources

Lecture notes, lecture slides, R code examples, and other material will be made available on Wattle. There are no prescribed texts.

The R programming language will be used extensively throughout the course. It is recommended that students bring a laptop with them to class, with the R software language loaded on to their laptop. Information about loading and using R will be given in week 1 of lectures. R will be required for completing the assignment.

Staff Feedback

Students will be given feedback in the following forms:

  • Following the quizzes, assignments and mid-semester examination, feedback will be given to the class about the general performance on the assessment pieces.
  • Marked assignments will be handed back to students, and students will have an opportunity to look over their mid-semester examination script-books.
  • Students will have the opportunity to speak with the lecturer and seek feedback about their individual performance in all assessment pieces.

Student Feedback

ANU is committed to the demonstration of educational excellence and regularly seeks feedback from students. Students are encouraged to offer feedback directly to their Course Convener or through their College and Course representatives (if applicable). The feedback given in these surveys is anonymous and provides the Colleges, University Education Committee and Academic Board with opportunities to recognise excellent teaching, and opportunities for improvement. The Surveys and Evaluation website provides more information on student surveys at ANU and reports on the feedback provided on ANU courses.

Class Schedule

Week/Session Summary of Activities Assessment
1 Loss distributions - Introduction to modelling individual losses. Exponential distribution; parameter estimation techniques: method of moments, method of percentiles, maximum likelihood estimation. Estimator precision. Pearson chi-square goodness of fit testing
2 Loss distributions (cont.) - Skewed distributions: Gamma, log-normal, Weibull. Mixture distributions and claim number/frequency distributions
3 Loss distributions with risk sharing - Reinsurance and policy excesses. Proportional and Excess-of-Loss reinsurance. Modelling individual claims with reinsurance.
4 Aggregate loss distributions - Collective Risk Model. Compound Poisson, Binomial and Negative Binomial distributions. Compound distributions and reinsurance.
5 Extreme value theory - Modelling the tails of loss distributions. Types of EVT distributions (GEV and GPD), fitting EVT distributions
6 Copulas - Introduction to measuring and modelling joint risks, definitions of dependence and concordance, tail dependence. Copula theory, copula families, and fitting copulas to model risk dependency.
7 Time series - Introduction and objectives of time series analysis. Introduction to univariate time series. Properties of financial time series. Stationarity, random processes (including random walks), unit roots, operators.
8 Time series (cont.) - AR, MA, ARMA and ARIMA processes. Model selection and diagnostics - identifying unit roots, correlograms, fitting models using software, residual tests. Application to financial returns and losses.
9 Time series (cont.) - Basic time series forecasting - deterministic and stochastic. Limitations with simple univariate models and introduction to extensions for heteroscedasticity (ARCH and GARCH), and multivariate series.
10 Machine learning - Introduction to elementary principles of machine learning. Unsupervised and supervised learning techniques.
11 Machine learning (cont.) - Software for applying machine learning techniques. Examples and applications to risk modelling
12 Course summary

Assessment Summary

Assessment task Value Learning Outcomes
Final Examination 100 % 1,2,3,4,5

* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details


ANU has educational policies, procedures and guidelines, which are designed to ensure that staff and students are aware of the University’s academic standards, and implement them. Students are expected to have read the Academic Misconduct Rule before the commencement of their course. Other key policies and guidelines include:

Assessment Requirements

The ANU is using Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. For additional information regarding Turnitin please visit the ANU Online website. In rare cases where online submission using Turnitin software is not technically possible; or where not using Turnitin software has been justified by the Course Convener and approved by the Associate Dean (Education) on the basis of the teaching model being employed; students shall submit assessment online via ‘Wattle’ outside of Turnitin, or failing that in hard copy, or through a combination of submission methods as approved by the Associate Dean (Education). The submission method is detailed below.

Moderation of Assessment

Marks that are allocated during Semester are to be considered provisional until formalised by the College examiners meeting at the end of each Semester. If appropriate, some moderation of marks might be applied prior to final results being released.


The final examination will be a closed book exam. A formula sheet will be handed out at the start of the exam. Copies of the formula sheet will be made available through Wattle prior to the exam (no later than week 12 of the semester).

Assessment Task 1

Value: 100 %
Learning Outcomes: 1,2,3,4,5

Final Examination

15 minute reading time; 3 hour writing time. The final exam will count for a 100% of your grade. It will cover material from all weeks of the course. The exam will be closed book, but a formula sheet will be provided for use during the exam. Copies of the formula sheet will be made available through Wattle in the weeks prior to the examination (no later than week 12 of the semester).

Centrally administered examinations through Examinations, Graduations & Prizes will be timetabled prior to the examination period. Please check ANU Timetabling for further information. Further information about the examination will be provided in class and on Wattle closer to the date of the examination, and no later than Week 12.

Academic Integrity

Academic integrity is a core part of the ANU culture as a community of scholars. At its heart, academic integrity is about behaving ethically, committing to honest and responsible scholarly practice and upholding these values with respect and fairness.

The ANU commits to assisting all members of our community to understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. The ANU expects staff and students to be familiar with the academic integrity principle and Academic Misconduct Rule, uphold high standards of academic integrity and act ethically and honestly, to ensure the quality and value of the qualification that you will graduate with.

The Academic Misconduct Rule is in place to promote academic integrity and manage academic misconduct. Very minor breaches of the academic integrity principle may result in a reduction of marks of up to 10% of the total marks available for the assessment. The ANU offers a number of online and in person services to assist students with their assignments, examinations, and other learning activities. Visit the Academic Skills website for more information about academic integrity, your responsibilities and for assistance with your assignments, writing skills and study.

Online Submission

You will be required to electronically sign a declaration as part of the submission of your assignment. Please keep a copy of the assignment for your records. Unless an exemption has been approved by the Associate Dean (Education) submission must be through Turnitin.

Hardcopy Submission

For some forms of assessment (hand written assignments, art works, laboratory notes, etc.) hard copy submission is appropriate when approved by the Associate Dean (Education). Hard copy submissions must utilise the Assignment Cover Sheet. Please keep a copy of tasks completed for your records.

Late Submission

No submission of assignments without an extension after the due date will be permitted. If an assignment is not submitted by the due date, a mark of 0 will be awarded.

Referencing Requirements

Accepted academic practice for referencing sources that you use in presentations can be found via the links on the Wattle site, under the file named “ANU and College Policies, Program Information, Student Support Services and Assessment”. Alternatively, you can seek help through the Students Learning Development website.

Returning Assignments

Assignments will be returned in class.

Extensions and Penalties

Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure. Extensions may be granted for assessment pieces that are not examinations or take-home examinations. If you need an extension, you must request an extension in writing on or before the due date. If you have documented and appropriate medical evidence that demonstrates you were not able to request an extension on or before the due date, you may be able to request it after the due date.

Privacy Notice

The ANU has made a number of third party, online, databases available for students to use. Use of each online database is conditional on student end users first agreeing to the database licensor’s terms of service and/or privacy policy. Students should read these carefully. In some cases student end users will be required to register an account with the database licensor and submit personal information, including their: first name; last name; ANU email address; and other information.
In cases where student end users are asked to submit ‘content’ to a database, such as an assignment or short answers, the database licensor may only use the student’s ‘content’ in accordance with the terms of service – including any (copyright) licence the student grants to the database licensor. Any personal information or content a student submits may be stored by the licensor, potentially offshore, and will be used to process the database service in accordance with the licensors terms of service and/or privacy policy.
If any student chooses not to agree to the database licensor’s terms of service or privacy policy, the student will not be able to access and use the database. In these circumstances students should contact their lecturer to enquire about alternative arrangements that are available.

Distribution of grades policy

Academic Quality Assurance Committee monitors the performance of students, including attrition, further study and employment rates and grade distribution, and College reports on quality assurance processes for assessment activities, including alignment with national and international disciplinary and interdisciplinary standards, as well as qualification type learning outcomes.

Since first semester 1994, ANU uses a grading scale for all courses. This grading scale is used by all academic areas of the University.

Support for students

The University offers students support through several different services. You may contact the services listed below directly or seek advice from your Course Convener, Student Administrators, or your College and Course representatives (if applicable).

Dr Timothy Higgins
6125 4507

Research Interests

Income contingent loans, superannuation and retirement income policy, microsimulation modelling

Dr Timothy Higgins

Dr Timothy Higgins
6125 4507

Research Interests

Dr Timothy Higgins

Responsible Officer: Registrar, Student Administration / Page Contact: Website Administrator / Frequently Asked Questions