Geostatistical models for parametric insurance triggers. Mathematics NERC GW4+ DTP PhD studentship Ref: 3131

About the award

This project is one of a number that are in competition for funding from the NERC Great Western Four+ Doctoral Training Partnership (GW4+ DTP).  The GW4+ DTP consists of the Great Western Four alliance of the University of Bath, University of Bristol, Cardiff University and the University of Exeter plus six Research Organisation partners:  British Antarctic Survey, British Geological Survey, Centre for Ecology and Hydrology, the Met Office, the Natural History Museum and Plymouth Marine Laboratory.  The partnership aims to provide a broad training in earth and environmental sciences, designed to train tomorrow’s leaders in earth and environmental science. For further details about the programme please see

The Studentship will be awarded on the basis of merit and will commence in September 2018.  For eligible students the award will provide funding for a stipend which is currently £14,553 per annum (2017/2018), research costs and UK/EU tuition fees at Research Council UK rates for 42 months (3.5 years) for full-time students, pro rata for part-time students.

Main Supervisor:  Dr. Ben Youngman, (Mathematics, University of Exeter)
Co-Supervisor:  Dr. Theo Economou, (Mathematics, University of Exeter)
Co-Supervisor:  Paul Maisey (Met Office Hadley Centre)

Project description:
Reliably quantifying and predicting natural disaster risk is vital for the exposed/vulnerable population and insurance companies. Reinsurers that underwrite such risk typically rely on catastrophe models for quantification. Parametric insurance is a cost-effective alternative to reinsurance, by avoiding development costs of catastrophe models, which gives developing countries opportunity to promptly receive financial support following natural disasters. One insurer, CCRIF SPC, paid Caribbean countries approximately $31.2 million soon after Hurricane Irma.

Recent NERC calls and Youngman & Stephenson (2016) receiving the 2016 Lloyd’s Science of Risk Prize highlight the importance of parametric insurance and parsimonious modelling. Most parametric insurance products trigger a payout when simple criteria are met, e.g. a flood payout if rainfall exceeds some threshold, irrespective of whether a flood occurs. Basis risk describes the mismatch between payouts and disaster occurrence. This project’s research will aim to help minimise basis risk.

Project aims and methods:
This project will focus on the meteorological component of natural disasters, i.e. rainfall for flooding, or wind speeds for hurricanes. It will involve the development of geostatistical models to support the parametric insurance industry. Such models allow fast, efficient and robust simulation of meteorological hazards associated with natural disasters, such as floods or hurricanes. Compared to typical vendor-operated catastrophe models, geostatistical models require significantly less computing resource and hence expense. In fact such models can be translated into open-source user-friendly computer code, which offers the industry transparency. Both sides of the industry can benefit: parametric insurers can validate and/or modify criteria to reduce basis risk, while customers can better understand and compare products. Ultimately, reinsurance could even become more cost-effective.

The ideal candidate for this project will have a strong background in mathematics, and statistical modelling at undergraduate – and preferably Master’s – level. The candidate will have experience in computer programming in R, Python or similar.

The successful applicant will be encouraged to attend four Academy for Postgraduate Training in Statistics (APTS) courses. Attending relevant ad-hoc courses relevant to statistics, programming, meteorology or natural hazards will also be encouraged (examples include Introduction to Catastrophe Modelling, by Oasis Loss Modelling Framework, or Big Data: tools and statistical methods, by RSS).

Youngman, B. D. & D. B. Stephenson (2016). A geostatistical extreme-value frame-
work for fast simulation of natural hazard events. In Proc. R. Soc. A, Volume 472, pp.
0150855. The Royal Society.
Wood, E, Lamb, R, Warren, S, Hunter, N, Tawn, J, Allan, R & Laeger, S (2016) Development of large scale inland flood scenarios for disaster response planning based on spatial/temporal conditional probability analysis E3S Web of Conferences, vol 7, 01003. DOI: 10.1051/e3sconf/20160701003

Entry requirements:       
Applicants should have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK.   Applicants with a Lower Second Class degree will be considered if they also have Master’s degree.  Applicants with a minimum of Upper Second Class degree and significant relevant non-academic experience are encouraged to apply.

All applicants would need to meet our English language requirements by the start of the  project

Applicants who are classed as International for tuition fee purposes are not eligible for funding.


Application deadline:8th May 2018
Value:£14,553 per annum for 2017-18
Duration of award:per year
Contact: PGR

How to apply

To apply for this funded studentship, please click and follow the 'Apply Now' button on this webpage.
During the application process you will be asked to upload several documents.  Please note our preferred format is PDF, each file named with your surname and the name of the document, eg. “Smith – CV.pdf”, “Smith – Cover Letter.pdf”, “Smith – Transcript.pdf”.

•       CV
•       Letter of application outlining your academic interests, prior research experience and reasons for wishing to
        undertake the project.
•       Transcript(s) giving full details of subjects studied and grades/marks obtained.  This should be an interim transcript
        if you are still studying.
•       If you are not a national of a majority English-speaking country you will need to submit evidence of your current
        proficiency in English.

You will be asked to name 2 referees as part of the application process however we will not contact these people until the shortlisting stage. Your referees should not be from the prospective supervisory team.

The closing date for applications is midnight on 8th May 2018.  Interviews will be held at the University of Exeter in the week commencing 21st May.

If you have any general enquiries about the application process please email Project-specific queries should be directed to the supervisor.

During the application process, the University may need to make certain disclosures of your personal data to third parties to be able to administer your application, carry out interviews and select candidates.  These are not limited to, but may include disclosures to:
• the selection panel and/or management board or equivalent of the relevant programme, which is likely to include staff from one or more other HEIs;
• administrative staff at one or more other HEIs participating in the relevant programme.
Such disclosures will always be kept to the minimum amount of personal data required for the specific purpose. Your sensitive personal data (relating to disability and race/ethnicity) will not be disclosed without your explicit consent.