Daniela Escobar

This course is available on the MSc in Financial Mathematics, MSc in Quantitative Methods for Risk Management, MSc in Statistics (Financial Statistics), MSc in Statistics (Financial Statistics) (LSE and Fudan) and MSc in Statistics (Financial Statistics) (Research). This course is available with permission as an outside option to students on other programmes where regulations permit.

The course is available with permission when students meet requirement on pre-requisities. 

Students must have completed Stochastic Processes (ST302) and Probability, Distribution Theory and Inference (ST202).

Good undergraduate understanding of distribution theory and stochastic processes. ST202, ST302 or their equivalent.

A self-contained introduction to insurance risk analysis. Starting from classical actuarial modelling of insurance risk, axiomatic approach to ordering of risks is presented, optimal forms of insurance from the insured's and from the insurer's point of view are analysed, and Pareto-optimal risk exchanges are introduced. Standard schemes of reinsurance are introduced and analysed. Ruin theory for an insurance company and capital requirement are studied. Heavy tail distributions and the extreme value theory are introduced. In the second part of the course, securitisation of insurance-related risks will be introduced. Case studies on current developments in life and non-life insurance industry and their stochastic modelling are also presented.

20 hours of lectures and 10 hours of seminars in the MT.

Week 6 will be used as a reading week.

Students will be expected to produce 6 problem sets in the MT.

Thomas Mikosch, Non-Life Insurance Mathematics

Ragnar Norberg, Non-life Insurance Mathematics (Lecture notes)

Pauline Barrieu, Luca Albertini, The Handbook of Insurance-Linked Securities

Exam (90%, duration: 2 hours) in the summer exam period.
Presentation (10%) in the MT.

During weeks 10 and 11, students will be required to give group presentations using material which will have been provided in week 1 or 2.