Further Statistics for Actuarial Science - MAN00049H

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  • Department: The York Management School
  • Module co-ordinator: Prof. Alexander McNeil
  • Credit value: 10 credits
  • Credit level: H
  • Academic year of delivery: 2019-20

Module summary

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

Module will run

Occurrence Teaching cycle
A Autumn Term 2019-20

Module aims

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

Module learning outcomes

After successful completion the student is able to:

Subject content

  • apply Bayes and minimax decision rules;
  • use Bayesian inferential procedures;
  • apply Bayesian credibility theory to insurance tarification problems;
  • describe the main ideas of extreme value theory and dependence modelling via copulas;
  • demonstrate knowledge of the main concepts of machine learning.

 

Academic and graduate skills

  • present decision theoretic analyses in a logical, rigorous, and concise way.
  • strict logical reasoning from assumptions to conclusion;
  • critically assess assumptions necessary to draw certain conclusions.

Assessment

Task Length % of module mark
Essay/coursework
Coursework		 N/A 20
University - closed examination
Further Statistics for Actuarial Science		 1.5 hours 80

Special assessment rules

None

Reassessment

Task Length % of module mark
University - closed examination
Reassessment: Exam		 N/A 100

Module feedback

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.

Indicative reading

McNeil, A, Frey, R and Embrechts, P (2016), “Quantitative Risk Management: Concepts, Techniques & Tools” (2nd ed), Princeton University Press.


The information on this page is indicative of the module that is currently on offer. The University is constantly exploring ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary by the University. Where appropriate, the University will notify and consult with affected students in advance about any changes that are required in line with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.