- Department: The York Management School
- Module co-ordinator: Prof. Alexander McNeil
- Credit value: 10 credits
- Credit level: H
- Academic year of delivery: 2021-22
- See module specification for other years: 2022-23
The aim of the module is to expose students to a number of advanced statistical topics that are used in actuarial science and quantitative risk management, including Bayesian inferential procedures, credibility theory, extreme value theory, the modelling of dependent risks and machine learning
Occurrence | Teaching period |
---|---|
A | Autumn Term 2021-22 |
The aim of the module is to expose students to a number of advanced statistical topics that are used in actuarial science and quantitative risk management, including Bayesian inferential procedures, credibility theory, extreme value theory, the modelling of dependent risks and machine learning
After successful completion the student is able to:
Subject content
Academic and graduate skills
Task | Length | % of module mark |
---|---|---|
Essay/coursework Coursework |
N/A | 20 |
Online Exam -less than 24hrs (Centrally scheduled) Further Statistics for Actuarial Science |
1 hours | 80 |
None
Task | Length | % of module mark |
---|---|---|
Online Exam - 24 hrs (Centrally scheduled) Reassessment: Exam |
1.5 hours | 100 |
Students will receive feedback within three weeks of the hand-in problem sets. The feedback will be handed to students personally and takes the form of comments and suggestions for improvement written on the handed in work.
McNeil, A, Frey, R and Embrechts, P (2016), “Quantitative Risk Management: Concepts, Techniques & Tools” (2nd ed), Princeton University Press.