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.
|Withdraw – Financial?||13/02/2021|
|Withdraw – Academic?||06/03/2021|
|Withdraw – Financial?||09/10/2021|
|Withdraw – Academic?||30/10/2021|