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.
|Bond Business School|
- September 2021 [Standard Offering]
- January 2022 [Standard Offering]
- September 2022 [Standard Offering]
- January 2023 [Standard Offering]
- September 2023 [Standard Offering]
- Commencing in 2021: $5,250
- Commencing in 2022: $5,310
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.