The focus of this subject is stochastic processes that are typically used to model the dynamic behaviour of random variables indexed by time. The close-of-day exchange rate is an example of a discrete-time stochastic process. There are also continuous-time stochastic processes that involve continuously observing variables, such as the water level within significant rivers. This subject covers discrete Markov chains, continuous-time stochastic processes and some simple time-series models. It also covers applications to insurance, reinsurance and insurance policy excesses, amongst others.
|Faculty||Bond Business School|
1. Explain in detail the type of a stochastic process and whether it possesses certain well-known properties.
2. Define, estimate and analyse Markov chains, including their long-run behaviour.
3. Define, estimate and analyse Markov jump processes, both time-homogeneous and time-inhomogeneous.
4. Define, estimate, analyse and compare compound stochastic processes including their applications to insurance, reinsurance and policy excess.
5. Estimate, analyse and compare some basic time-series models, including ARIMA and exponential smoothing models.
6. Use statistical software commonly used by practitioners to model stochastic processes.
There are no co-requisites.
Assumed knowledge is the minimum level of knowledge of a subject area that students are assumed to have acquired through previous study. It is the responsibility of students to ensure they meet the assumed knowledge expectations of the subject. Students who do not possess this prior knowledge are strongly recommended against enrolling and do so at their own risk. No concessions will be made for students’ lack of prior knowledge.
Possess demonstratable knowledge in mathematical statistics and probability theory to the level of a unit such as ACSC71-200 Mathematical Statistics.
|Withdraw – Financial?||13/02/2021|
|Withdraw – Academic?||06/03/2021|