| Location | Term | Level | Credits (ECTS) | Current Convenor | 2019-20 |
|---|---|---|---|---|---|
| Canterbury | Spring View Timetable |
5 | 15 (7.5) | DR X Wang |
Pre-requisites
Pre-requisite: MAST5007 Mathematical Statistics or MACT5290 Probability and Statistics for Actuarial Science 2
Restrictions
None
Overview
This module covers aspects of Statistics which are particularly relevant to insurance. Some topics (such as risk theory and credibility theory) have been developed specifically for actuarial use. Other areas (such as Bayesian Statistics) have been developed in other contexts but now find applications in actuarial fields. Stochastic processes of events such as accidents, together with the financial flow of their payouts underpin much of the work. Since the earliest games of chance, the probability of ruin has been a topic of interest. Outline Syllabus includes: Decision Theory; Bayesian Statistics; Loss Distributions; Reinsurance; Credibility Theory; Empirical Bayes Credibility theory; Risk Models; Ruin Theory; Generalised Linear Models; Run-off Triangles.Details
This module appears in:
Contact hours
35 hours
Method of assessment
90% Examination, 10% Coursework
Indicative reading
The students are provided with the study notes published by the Actuarial Education Company and a copy of "Formulae and Tables for Examinations".
The following book is also relevant:
PJ Boland Statistical and Probabilistic Methods in Actuarial Science (Chapman & Hall, 2007) (R)
Learning outcomes
The intended subject specific learning outcomes. On successfully completing the module students will be able to:
1 explain basic concepts and models in decision analysis and statistics, as presented in the module, and apply them in insurance;
2 construct risk models appropriate to short term insurance contracts and make the related statistical inference;
3 describe and apply the fundamental concepts of credibility theory;
4 describe and apply the basic methodology used in rating general insurance business;
5 describe and apply techniques for analysing a delay (or run-off) triangle.
The intended generic learning outcomes. On successfully completing the module students will be able to:
1 demonstrate probabilistic and statistical skills in solving financial problems;
2 demonstrate enhanced conceptual skills and logical reasoning ability;
3 demonstrate a broad understanding of the range of application of statistics to financial processes.