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ACTL2102 Foundations of Actuarial Models
Course Details & Outcomes
Course Description
This course introduces the stochastic models used by actuaries to model both liabilities and assets and illustrates their applications in actuarial work. Topics covered include main features of a Markov chain and applications to experience rating; Markov process models and applications to insurance, survival, sickness and marriage models; methods for simulation of a stochastic process; estimation of Markov chains. Students will be expected to implement models using the R software in a numerical computer package.
Course Aims
This course provides an introduction to the stochastic models used by actuaries to model both liabilities and assets and illustrates their applications in actuarial work.
Relationship to Other Courses
This course introduces the stochastic models used by actuaries to model both liabilities and assets and illustrates their applications in actuarial work. The material is mathematically rigorous with a strong foundation in mathematics. The required knowledge of the course is a good understanding of probability and statistics as covered in ACTL2131 Probability and Mathematical Statistics or MATH2801 and MATH2831. You should also be proficient with calculus and linear algebra. The assumed knowledge of the course is a good understanding of mathematics as covered in MATH1151 and MATH1251.
The course will have applications in other courses in the actuarial major. More advanced models are covered in ACTL3141 Actuarial Models and Statistics and ACTL3162 General Insurance Techniques. The course is necessary knowledge for the more advanced coverage in ACTL3141 Actuarial Models and Statistics, ACTL3151 Actuarial Mathematics for Insurance and Superannuation, ACTL3162 General Insurance Techniques, and ACTL3182 Asset-Liability and Derivative Models. Advanced- data analytics methods relevant to actuarial work are covered in ACTL3142 Statistical Machine Learning for Risk and Actuarial Applications.
Furthermore, from T2 2022, the time series will be covered in ACTL3301 Quantitative Risk Management.
Course Learning Outcomes
| Course Learning Outcomes | Program learning outcomes |
|---|---|
| CLO1 : Describe and explain concepts and principles of actuarial modelling. |
|
| CLO2 : Describe and explain the main terminology of stochastic processes, including their classification into different types. |
|
| CLO3 : Define the key features and properties of a Markov Chain |
|
| CLO4 : Implement Markov Chains for a frequency-based experience rating No Claim Discount (NCD) scheme using data. | |
| CLO5 : Define the main features of a Markov Process and use simple Markov Processes to analyse insurance, survival, sickness and marriage models. |
|
| CLO6 : Identify and apply Markov processes that can be used for insurance, survival, sickness and financial modelling. |
|
| CLO7 : Identify and apply the main concepts of “Monte Carlo” simulation of a stochastic process and carry out simple simulation procedures. |
|
| CLO8 : Explain the concepts of Monte Carlo simulation of a stochastic process using a series of pseudo-random numbers and apply simulation to simple actuarial problems. |
|
| CLO9 : Estimate transition probability and matrix of discrete-time and continuous-time Markov chains. |
|
| CLO10 : Express his/her views on, and understanding of, an aspect of statistic models. |
|
| CLO11 : Develop communication skills for the presentation of complex statistical models in written report form. |
|
| CLO12 : Construct written work which is logically and professionally presented |
|
| Course Learning Outcomes | Assessment Item |
|---|---|
| CLO1 : Describe and explain concepts and principles of actuarial modelling. |
|
| CLO2 : Describe and explain the main terminology of stochastic processes, including their classification into different types. |
|
| CLO3 : Define the key features and properties of a Markov Chain |
|
| CLO4 : Implement Markov Chains for a frequency-based experience rating No Claim Discount (NCD) scheme using data. |
|
| CLO5 : Define the main features of a Markov Process and use simple Markov Processes to analyse insurance, survival, sickness and marriage models. |
|
| CLO6 : Identify and apply Markov processes that can be used for insurance, survival, sickness and financial modelling. |
|
| CLO7 : Identify and apply the main concepts of “Monte Carlo” simulation of a stochastic process and carry out simple simulation procedures. |
|
| CLO8 : Explain the concepts of Monte Carlo simulation of a stochastic process using a series of pseudo-random numbers and apply simulation to simple actuarial problems. |
|
| CLO9 : Estimate transition probability and matrix of discrete-time and continuous-time Markov chains. |
|
| CLO10 : Express his/her views on, and understanding of, an aspect of statistic models. |
|
| CLO11 : Develop communication skills for the presentation of complex statistical models in written report form. |
|
| CLO12 : Construct written work which is logically and professionally presented |
|
Learning and Teaching Technologies
Moodle - Learning Management System | EdStem
Learning and Teaching in this course
The course website is available on Moodle: https://moodle.telt.unsw.edu.au/login/index.php or via my.unsw.edu.au.
All course contents will be available from the Moodle course website. (It is essential that you visit the site regularly to see any notices posted there by the course coordinator, as it will be assumed that they are known to you within a reasonable time.
Assessments
Assessment Structure
| Assessment Item | Weight | Relevant Dates | Program learning outcomes |
|---|---|---|---|
|
Examination
Assessment FormatIndividual
|
60% |
Start DateNot Applicable
Due DateNot Applicable
|
|
|
Formative assessment
Assessment FormatIndividual
|
10% |
Start DateMondays in Weeks 3, 5, 7 and 9
Due DateWeek 3: 10 June - 16 June, Week 5: 24 June - 30 June, Week 7: 08 July - 14 July, Week 9: 22 July - 28 July
|
|
|
Individual project
Assessment FormatIndividual
|
30% |
Start Date24/06/2024 09:00 AM
Due Date19/07/2024 04:00 PM
|
|
Assessment Details
-
Examination
Assessment Overview
The examination will aim to assess the achievement of the learning course outcomes.
Course Learning Outcomes
- CLO1 : Describe and explain concepts and principles of actuarial modelling.
- CLO2 : Describe and explain the main terminology of stochastic processes, including their classification into different types.
- CLO3 : Define the key features and properties of a Markov Chain
- CLO4 : Implement Markov Chains for a frequency-based experience rating No Claim Discount (NCD) scheme using data.
- CLO5 : Define the main features of a Markov Process and use simple Markov Processes to analyse insurance, survival, sickness and marriage models.
- CLO6 : Identify and apply Markov processes that can be used for insurance, survival, sickness and financial modelling.
- CLO7 : Identify and apply the main concepts of “Monte Carlo” simulation of a stochastic process and carry out simple simulation procedures.
- CLO8 : Explain the concepts of Monte Carlo simulation of a stochastic process using a series of pseudo-random numbers and apply simulation to simple actuarial problems.
- CLO9 : Estimate transition probability and matrix of discrete-time and continuous-time Markov chains.
- CLO10 : Express his/her views on, and understanding of, an aspect of statistic models.
- CLO11 : Develop communication skills for the presentation of complex statistical models in written report form.
- CLO12 : Construct written work which is logically and professionally presented
Detailed Assessment Description
The final examination will assess students understanding of the concepts covered in the course and readings and their ability to apply them to practical problems. A deeper grasp of materials is expected from students at the final exam level than at the tutorial level.
The final examination will be a two-hour written paper. The final examination will be closed book. Students will only be allowed to bring the text "Formulae and Tables for Actuarial Examinations" into the exam. This must not be annotated.
Assessment Length
2 hours
Assignment submission Turnitin type
Not Applicable
-
Formative assessment
Assessment Overview
These are aimed at encouraging students to keep up with the course materials.
Course Learning Outcomes
- CLO1 : Describe and explain concepts and principles of actuarial modelling.
- CLO2 : Describe and explain the main terminology of stochastic processes, including their classification into different types.
- CLO3 : Define the key features and properties of a Markov Chain
- CLO4 : Implement Markov Chains for a frequency-based experience rating No Claim Discount (NCD) scheme using data.
- CLO5 : Define the main features of a Markov Process and use simple Markov Processes to analyse insurance, survival, sickness and marriage models.
Detailed Assessment Description
There are four formative assessments. These comprise online quizzes and online discussion questions. Students are required to complete these via Storywall. These will assess students’ understanding of the concepts covered in the course and their ability to apply them to stochastic actuarial modelling problems. Students will be given 5 days to complete it at home (or any place with an internet connection) and submit it online. Full credit will be given to students who have made a reasonable attempt. More details will be available on the course Moodle page.
Assessment Length
5 days each
Submission notes
Submission via Storywall
Assignment submission Turnitin type
This is not a Turnitin assignment
-
Individual project
Assessment Overview
An assignment task involving application of course concepts.
Course Learning Outcomes
- CLO8 : Explain the concepts of Monte Carlo simulation of a stochastic process using a series of pseudo-random numbers and apply simulation to simple actuarial problems.
- CLO11 : Develop communication skills for the presentation of complex statistical models in written report form.
Detailed Assessment Description
The practical application of the course concepts based on real life actuarial problems is an important graduate attribute that employers require and this course aims to provide at least some introductory exposure to this. Writing skills for technical material are also important.
There will be one major (individual) Assignment for this course involving the practical application and interpretation of course concepts. It is based on the application of the technical concepts introduced within the learning outcomes 1-7. The assignment offers students the opportunity to engage in critical analysis, self-reflection and problem solving, as well as to demonstrate their understanding of the concepts and perspectives that are central to actuarial studies. The assignment specifically assesses the program goals “Knowledge”, “Problem solving and critical thinking”, as well as “Communication”. Full information about the major assignment will be released early in the session.
Assessment Length
26 days
Assignment submission Turnitin type
This assignment is submitted through Turnitin and students do not see Turnitin similarity reports.
General Assessment Information
Grading Basis
Standard
Requirements to pass course
In order to pass the course students must obtain an overall composite mark of 50 at least. It is important that students be punctual and reliable when submitting assessment. This is an important workplace requirement and students need to ensure they meet deadlines. Your regular and punctual attendance at lectures and tutorials is expected in this course.
Course Schedule
| Teaching Week/Module | Activity Type | Content |
|---|---|---|
| Week 1 : 27 May - 2 June | Lecture |
Module 0: Stochastic processes Module 1: Discrete-time Markov Chains |
| Week 2 : 3 June - 9 June | Lecture |
Module 1: Discrete-time Markov Chains Module2: Introduction to Simulation |
| Week 3 : 10 June - 16 June | Lecture |
Module2: Introduction to Simulation Module3: Exponential Distribution and the Poisson Process Formative assessment |
| Week 4 : 17 June - 23 June | Lecture |
Module3: Exponential Distribution and the Poisson Process |
| Week 5 : 24 June - 30 June | Lecture |
Module4: Continuous-time Markov Chains Formative assessment Assignment released |
| Week 6 : 1 July - 7 July | Lecture |
No classes in flexibility week
|
| Week 7 : 8 July - 14 July | Lecture |
Module4: Continuous-time Markov Chains Formative assessment |
| Week 8 : 15 July - 21 July | Lecture |
Module4: Continuous-time Markov Chains Assignment Due |
| Week 9 : 22 July - 28 July | Lecture |
Module 5: Estimating Markov Chain transition probabilities and matrices Formative assessment |
| Week 10 : 29 July - 4 August | Lecture |
Module 5: Estimating Markov Chain transition probabilities and matrices PlusFinal exam instructions |
Attendance Requirements
Students are strongly encouraged to attend all classes and review lecture recordings.
General Schedule Information
The timetable may be altered. Students will be advised of any changes in lectures and via the course web site.
Course Resources
Prescribed Resources
• Sheldon M. Ross, Introduction to Probability Models, 12th edition, Academic Press 2014
• Formulae and Tables for Actuarial Examinations of the Faculty of Actuaries and the Institute of Actuaries
Recommended Resources
• Sheldon M. Ross, Stochastic Processes, 2nd edition, John Wiley, 1996
Course Evaluation and Development
Each year feedback is sought from students and other stakeholders about the courses offered in the School and continual improvements are made based on this feedback. UNSW's myExperience survey is one of the ways in which student evaluative feedback is gathered. In this course, we will seek your feedback through end of semester myExperience responses.