Undergraduate - UnitETC3420 - Bayesian modelling and risk analysis

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Synopsis

To provide a further grounding in mathematical and statistical techniques of particular relevance to insurance and financial work.

Outcomes

The learning goals associated with this unit are to:

1. explain the concepts of decision theory and apply them
2. calculate probabilities and moments of loss distributions both with and without limits and risk-sharing arrangements
3. construct risk models involving frequency and severity distributions and calculate the moment generating function and the moments for the risk models both with and without simple reinsurance arrangements
4. explain the concept of ruin for a risk model
5. explain the fundamental concepts of Bayesian statistics and use these concepts to calculate Bayesian estimators
6. describe the fundamental concepts of risk rating and apply them to simple experience rating systems
7. describe and apply techniques for analysing a delay (or run-off) triangle and projecting the ultimate position
8. explain the fundamental concepts of a generalised linear model (GLM), and describe how a GLM may apply
9. define and apply the main concepts underlying the analysis of time series models
10. explain the concepts of 'Monte Carlo' simulation using a series of pseudo-random numbers.

Assessment

Within semester assessment: 30%
Examination (3 hours): 70%

Chief examiner(s)

Associate Professor Catherine Forbes

Contact hours

4 hours per week

Prerequisites

ETC2520