ETC3420 - Bayesian modelling and risk analysis

Leader(s): Professor Don Poskitt

Offered

Clayton Second semester 2009 (Day)

Synopsis

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

Objectives

The learning goals associated with this unit are to:

• explain the concepts of decision theory and apply them
• calculate probabilities and moments of loss distributions both with and without limits and risk-sharing arrangements
• 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
• explain the concept of ruin for a risk model
• explain the fundamental concepts of Bayesian statistics and use these concepts to calculate Bayesian estimators
• describe the fundamental concepts of risk rating and apply them to simple experience rating systems
• describe and apply techniques for analysing a delay (or run-off) triangle and projecting the ultimate position
• explain the fundamental concepts of a generalised linear model (GLM), and describe how a GLM may apply
• define and apply the main concepts underlying the analysis of time series models
• explain the concepts of 'Monte Carlo' simulation using a series of pseudo-random numbers.

Assessment

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

Contact hours

Two 1-hour lectures and one 2-hour tutorial per week

Prerequisites

ETC2520