This subject covers both stochastic and survival modelling. Stochastic processes are very useful in actuarial science and finance. Stochastic processes are used to model the dynamic behaviour of the random variables over time. They are typically collections of random variables indexed by time, such as the close of day exchange rate, which is a discrete stochastic process. There are also continuous-time stochastic processes that involve continuously observing variables, for example the height of water in the Brisbane river. This subject covers simple discrete Markov chains, continuous-time stochastic processes with a focus on Poisson processes, Brownian motion and other related Gaussian processes. Survival models have proved to be very useful in actuarial science. This subject also discusses the theory, estimation and application of survival models. The life table is introduced, followed by survival models with a focus on the key parametric models. Estimation methods for lifetime distributions are discussed. Statistical models and maximum likelihood estimation of multistate processes are considered along with techniques such as the binomial model of mortality and exposed to risk. Methods for smoothing and testing crude mortality data are also studied.
|Bond Business School|
- January 2018 [Standard Offering]
- September 2018 [Standard Offering]
|Available to Study Abroad students|
- Commencing in 2017: $4,205
- Commencing in 2018: $4,247
1. Demonstrate expertise in the basic theory and the long run behaviour of simple discrete-time stochastic processes. An understanding of the properties of important continuous-time stochastic process with particular emphasis on Poisson processes. An understanding of other Markov pure jump processes, Brownian motion including important applications and other relevant Gaussian processes.
2. Display acquired facility in the theory and application of survival models. Facility in the application of multi state models including those with single and multiple decrements, competence in deriving transfer probabilities and transition intensities and an ability to derive maximum likelihood estimates in these contexts. Facility in estimating lifetime distributions, age based transition intensities and in testing estimates against the standard and graduated life tables. Define and analyse basic compound interest problems.
Students must have successfully completed ACSC12-200 Mathematical Statistics or equivalent prior to undertaking ACSC13-302