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Statistics for Finance & Insurance - MAT00092M

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  • Department: Mathematics
  • Credit value: 20 credits
  • Credit level: M
  • Academic year of delivery: 2023-24
    • See module specification for other years: 2024-25

Module summary

This module aims to provide a comprehensive and systematic introduction to statistical models and methods to analyse insurance and financial time series data.

Related modules

Pre-requisite modules

Co-requisite modules

  • None

Prohibited combinations

  • None

Module will run

Occurrence Teaching period
A Semester 2 2023-24

Module aims

This module aims to provide a comprehensive and systematic introduction to statistical models and methods to analyse insurance and financial time series data.

Module learning outcomes

By the end of the module, students should be able to:

  1. Analyse insurance data using distributions and inferential techniques.

  2. Apply appropriate methodologies to model insurance risks in different situations.

  3. Use statistical theory in the analysis of insurance data.

  4. Demonstrate knowledge of the basic characteristics of financial data, and apply financial time series models to such data.

  5. Derive theoretical results relating to important financial time series models.

  6. Fit appropriate models to time series data, and carry out related predictions using appropriate computer software.

Module content

This module is divided into two parts. In part (i), we introduce statistical models and methods to analyse the insurance data and cover the following four topics: claims reserving with run-off triangles, loss distributions, risk theory and ruin theory. In part (ii), we provide a comprehensive and systematic introduction to financial time series models and their applications to modelling and prediction of financial time series data, covering the following topics: returns & their characteristics; simple linear time series models & their applications; univariate volatility modeling & its implications.

Indicative assessment

Task % of module mark
Closed/in-person Exam (Centrally scheduled) 100

Special assessment rules

None

Indicative reassessment

Task % of module mark
Closed/in-person Exam (Centrally scheduled) 100

Module feedback

Current Department policy on feedback is available in the student handbook. Coursework and examinations will be marked and returned in accordance with this policy.

Indicative reading

Boland, P. (2011). Statistical and Probabilistic Methods in Actuarial Science. Chapman & Hall/CRC Interdisciplinary Statistics.

Dickson, D. (2010). Insurance Risk and Ruin. International Series on Actuarial Science, Cambridge University Press.

Kaas, R., Goovaets, M. and Dhaene, J. (2008). Modern Actuarial Risk Theory: Using R. Springer

Ruey S. Tsay (2010), Analysis of Financial Time Series, Wiley.


The information on this page is indicative of the module that is currently on offer. The University constantly explores 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. In some instances it may be appropriate for the University to notify and consult with affected students about module changes in accordance with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.