Master of Actuarial Studies

This course covers probability and statistics topics relevant to actuarial studies with applications in insurance and related areas. Topics covered include probability generating functions, moment generating functions, marginal and conditional distributions, independence and convolution, conditional expectation and compound distributions, sampling distributions, estimation methods, hypothesis tests, regression and analysis of variance.

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This course develops the financial and actuarial mathematics required for the analysis of financial and insurance transactions. Topics covered include: mathematics of compound interest, valuation of cash flows of insurance contracts; analysis and valuation of annuities, bonds, loans and other securities; yield curves and immunisation; introduction to stochastic interest rate models and actuarial applications.

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The aim of the course is to provide the future actuary with a basic understanding of corporate finance and financial reporting. The course will cover the instruments used by companies to raise finance and manage financial risk and will develop an understanding of how to interpret the accounts and financial statements of companies and financial institutions.

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An introduction to economic analysis and policy, with particular application to decision-making in business. The course provides students with the tools to use economic principles in decision-making and an understanding of the broader economic environment in which business decisions must be made.

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This course provides a comprehensive analysis of the economics and finance of retirement income provision. It is designed to provide students with a solid understanding of the theoretical and public policy issues associated with the design and implementation of policies for retirement income provision in Australia and internationally. Topics covered include: demographic trends and the economic impact of population ageing; rationale for public provision and/or regulation of private retirement income provision; retirement income provision in Australia; the taxation and regulation of retirement savings and benefits; the structure, performance and efficiency of the superannuation industry; and international comparisons in policy design and implementation.

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Students complete a project under the direction of a supervisor.

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This course develops the theory and practice underlying the actuarial management of risk-based and other products offered by financial institutions. The course draws examples from actuarial practice and discusses implications for life insurance, general insurance, superannuation, asset-liability management and other areas where actuaries are involved in product design, pricing, reserving, investment and surplus management. The course emphasises recent developments in actuarial theory.

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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. Topics covered include the terminology of stochastic processes; main features of Markov chain and application to experience rating; Markov process models and application to survival, sickness and marriage models; simple time series models including random walk and auto-regressive models and their application to investment variables; properties of Brownian motion and applications to investment variables; methods for simulation of a stochastic process. Students will be required to implement models using spreadsheets and programs in a numerical computer package such as Matlab.

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This course covers survival models, their estimation and application to mortality and other decrements. Specific topics include: the concept of a survival model and actuarial notation; estimation of lifetime distributions; multiple state models; maximum likelihood estimation of transition intensities; construction of multiple decrement tables; the binomial model of mortality and its estimation; models with transition intensities depending on age and duration; the census approximation and formulae; statistical comparison of crude rates with standard table; graduation of crude estimates and tests of fidelity and smoothness; analysis of mortality/morbidity and the main forms of selection; models for projection of populations. The analysis of data using spreadsheets developed during the course will form part of the course assessment.

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This course covers the actuarial mathematics and models for use in the analysis and actuarial management of life insurance and superannuation contract. Topics covered include: the main forms of life insurance and annuity contracts, disability and long term care contracts and superannuation fund benefits; actuarial notation and the life table; moments of the value of the benefit payments; Thiele's differential equation for policy values; stochastic modelling of claims and benefit payments; gross premiums, net premiums, policy values and reserves; allowing for expenses and inflation; use of discounted emerging costs and profit tests; asset shares in life insurance; termination and alteration values; cost of guarantees; joint life functions; actuarial valuation of disability insurance contracts.

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This course covers the actuarial mathematics, statistics and models used in non-life insurance actuarial practice. Topics covered include: basic concepts of decision theory and Bayesian statistics; loss distributions and reinsurance, risk models including compound Poisson; estimation of aggregate claims distribution; probability of ruin; premium rating and credibility; experience rating systems; and claims reserving for loss run-off data.

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The aim of this course is to introduce the mathematical and economic models of financial economics used by actuaries and to overview their application to asset-liability management. The topics are illustrated with applications to the valuation, actuarial and risk management of insurance and superannuation contracts especially those with embedded options and financial guarantees.

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This course covers the actuarial professional syllabus for data analysis including techniques for mortality, health, and insurance data used in actuarial analysis and decision-making. The course covers aspects of data analysis including exploratory data analysis, data checking and cleaning, and data visualization; classification and prediction with regression and generalized linear models; descriptive, inferential and predictive analysis and models; and statistical and machine learning including supervised and unsupervised learning.

The course also covers ethical, regulatory and professional issues, and risks and risk management associated with using data and data analysis. A particular focus will be placed on communication of technical results for business applications.

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This course, along with ACTL5100 Actuarial Theory and Practice A, develops the theory and practice underlying the actuarial management of risk-based and other products offered by financial institutions. The course draws examples from actuarial practice and discusses implications for life insurance, general insurance, superannuation, asset-liability management and other areas where actuaries are involved in product design, pricing, reserving, investment and surplus management. The course emphasises recent developments in actuarial theory.

This course, along with ACTL 5100, corresponds to the Part II courses of the professional examinations of The Institute of Actuaries of Australia.

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This course explores quantitative methods of risk measurement and modelling in financial institutions, including insurers, reinsurers, superannuation funds, and banks, and the major types of risks encountered therein. Topics covered include: risk measures; multivariate models for risks; copulas and dependence models; extreme value theory and tails of losses; time series techniques. The links between the different modelling tools are explored, and are further illustrated with models used in different risk types.

Together with ACTL5302 it is designed to cover the course topics for the professional actuarial Enterprise Risk Management / CERA qualification.

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This course covers the motivations and techniques for risk and capital management in financial institutions, including insurers, reinsurers, superannuation funds, and banks and the major types of risks encountered. Topics covered include: economic and regulatory capital; regulatory frameworks for banks and insurers; quantitative and qualitative issues of measuring and management of market risk, credit risk, interest rate risk, operational risk, liquidity risk and model risk, with particular emphasis on data issues met when quantifying those risks; scenario analysis and stress testing methods.

Together with ACTL5301 it is designed to cover the course topics for the professional actuarial Enterprise Risk Management / CERA qualification.

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This course covers the knowledge, skills and judgement necessary to understand investment and asset liability modelling with an emphasis on practical issues. It covers the design and monitoring of investment strategies for a range of liability profiles including life insurance, general insurance and superannuation funds. There is an emphasis on investment and asset issues of relevance for the management of liabilities.

The course has been designed to cover the International Actuarial Association syllabus covering Investments and Asset Analysis and the Institute of Actuaries of Australia Investments professional syllabus.

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This course examines saving and spending decisions by individuals and households over the lifecycle in the context of national retirement savings and incomes policies. We examine the economic and financial risks facing both individuals and product providers, as well as behavioural explanations for deviations from economic rationality and the lifecycle model.

Topics covered include: a taxonomy of the lifecycle; lifecycle theories of consumption and saving; investment strategy, human capital and the retirement decision: risks facing individual retirees and providers of retirement income products (investment risk, longevity risk, interest rate risk, inflation risk, replacement risk); market failure in the retirement benefit market; behavioural explanations for non-rational behaviour including myopia, procrastination, mental accounting, complexity, framing, loss aversion etc.

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"This course aims to introduce students to the complex and diverse range of risks that organisations must manage in today's increasingly global environment. The course discusses the complex nature of the words 'uncertainty' and 'risk', and how their many definitions and interpretations impact risk management. Subsequent lectures demonstrate why organisations must manage risks and what is different in the way they approach risk management today, compared to the past. The fundamental role risk culture and communication play in ensuring an organization’s ability to manage risks at Enterprise level is emphasised, and various frameworks for risk management reviewed.

The course aims to enhance students' ability to think 'outside the box' and encourages them to find solutions to complex problems, given limited information. Class debates and case study lectures are used to achieve these student attributes"

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The course reviews key concepts of statistics and looks at a number of data sources used to analyse risks in various disciplines. Applications such as forecasting, modelling extreme events and dependencies are illustrated through their implementation in a number of practical problems. These are aimed at making students aware of the power of statistics in quantitative risk analysis, and its areas of applicability. Traditional risk measures and models routinely used to analyse financial, insurance, environmental, health and safety, engineering reliability and security risks data are reviewed. Emphasis is placed on the use of these tools in practical applications, which is achieved through the presentation of real-life examples. Simulation is discussed as a tool to analyse risks in complex systems, where data is not available.

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This course aims to introduce students to the field of decision analysis. At the end of the course students should be able to develop solutions to basic decision problems involving uncertainty, using specialised, free-ware software. An intuitive exposition into Bayes’ theorem is given through a number of simple-to-follow problems involving information update in light of new evidence, to show the importance of this theorem in making decisions about risk. Students learn about subjective probabilities and various formal methods of expert opinion elicitation. Two specific decision tools - Bayesian networks and Multi-criteria Decision Analysis (MCDA), are examined in detail.

Bayesian networks, which display graphically the interrelationships between variables, allow for their dependencies to be accounted for in a manner reflecting their causal relationships. This permits the evaluation of predicted consequences associated with different interventions, before these are actually implemented. MCDA allows decisions to be made when the decision-maker is faced with multiple and conflicting objectives where trade-offs must be made, which is most often the case in the real world. Netica and SuperDecisions are free-ware software implementing Bayesian networks and MCDA, respectively, which students will use during the course to solve decision problems from a range of application fields.

Finally the course will discuss current issues in behavioural risk management and the value of first-line employees’ experience, such as their ability to identify ‘early warnings’ about risk, and what an organisation can do to encourage such employees to speak up without the fear of retribution. The aim is to demonstrate the benefits of front-line employees taking charge of local risk management issues and the importance role risk culture plays in allowing this to occur.

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This course covers innovative risk management strategies using capital and insurance market techniques including those used in the alternative risk transfer (ART) market. Topics include: product types; securitisation; pricing risk-linked securities; credit risk; weather and energy risk; modelling individual risks; industry specific case studies; portfolio considerations; accounting, regulatory and legal issues.

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This course is concerned with the special statistical characteristics that arise when modelling time series data, such as commodity prices, 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 and private finance sector.

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This course is designed to provide a rigorous foundation of modern econometrics for applied research in economics. The first part of the course covers classic econometric concepts and techniques, such as moment estimation, maximum likelihood, least squares regression, and hypothesis testing and inference. Where necessary some statistical theory as well as basic matrix algebra will be taught. The second part of the course builds on these sound fundamentals of econometric theory. Topics covered could include instrumental variables, generalized method of moments (GMM), the bootstrap, quantile regression, or panel methods. Students will be required to use statistical packages to conduct econometric data analysis.

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Operational risk is a relatively new area of study, and is now recognised as one of the major risks for institutions. This course will consider the nature of operational risks, how they arise, quantification issues and management. The course will make extensive use of recent papers and articles on the topic as well as case studies to assist learning.

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