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ACSC71-304: Stochastic Modelling


The focus of this subject is stochastic and survival modelling. Stochastic processes 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 simple discrete Markov chains, continuous-time stochastic processes and some simple time-series models. Further, the theory, estimation and application of a variety of survival models are covered, spanning parametric, semi-parametric and non-parametric models. 

Subject details

FacultyBond Business School
Semesters offered
  • September 2020 [Standard Offering]
  • January 2021 [Standard Offering]
  • January 2022 [Standard Offering]
Study areas
  • Actuarial Science
Subject fees
  • Commencing in 2020: $5,430
  • Commencing in 2021: $5,710

Learning outcomes

1. Explain the type of a stochastic process and demonstrate 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.
4. Demonstrate an advanced understanding of censoring and lifetime random variables in actuarial modelling.
5. Estimate, analyse and compare a variety survival models, including Weibull, Gompertz, Kaplan-Meier, Nelson-Aalen, Cox Proportional Hazards, Markov multi-state, Binomial and Poisson models.
6. Explain, perform and evaluate some basic time-series models, including ARIMA modelling.
7. Apply and explain the benefits of machine learning techniques in actuarial applications.
8. Use a statistical package frequently used by practitioners to model stochastic processes and survival models.

Enrolment requirements

Requisites: ?


Assumed knowledge:

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.

Assumed Prior Learning (or equivalent):

Possess demonstratable knowledge in mathematical statistics and probability theory to the level of a unit such as ACSC71-200 Mathematical Statistics.

Restrictions: ?


Subject outlines

Subject dates

Standard Offering
Enrolment opens10/11/2019
Semester start13/01/2020
Subject start13/01/2020
Cancellation 1?27/01/2020
Cancellation 2?03/02/2020
Last enrolment26/01/2020
Withdraw – Financial?08/02/2020
Withdraw – Academic?29/02/2020
Teaching census?07/02/2020
Standard Offering
Enrolment opens02/08/2020
Semester start14/09/2020
Subject start14/09/2020
Cancellation 1?28/09/2020
Cancellation 2?05/10/2020
Last enrolment27/09/2020
Withdraw – Financial?10/10/2020
Withdraw – Academic?31/10/2020
Teaching census?09/10/2020