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Title: Extreme insurance and the dynamics of risk
Author: Maynard, Trevor
ISNI:       0000 0004 5922 2759
Awarding Body: London School of Economics and Political Science (LSE)
Current Institution: London School of Economics and Political Science (University of London)
Date of Award: 2016
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The aim of this thesis is to explore the question: can scientific models improve insurance pricing? Model outputs are often converted to forecasts and, in the context of insurance, the supplementary questions: ‘are forecasts skillful?’ and ‘are forecasts useful?’ are examined. Skill score comparison experiments are developed allowing several scores in common use to be ranked. One score is shown to perform well; several others are shown to have systematic failings; with the conclusion that these should not be used by insurers. A new skill score property ‘Feasibility’ is proposed which highlights a key shortcoming of some scores in common use. Variables from a well known dynamical system are used as a proxy for an insurable index. A new method relating the system and its models is presented using skill scores to find their score optimal piecewise linear relationship. The index is priced using both traditional techniques and new methods that use the score optimal relationship. One new method is very successful in that it produces lower prices on average, is more profitable and leads to a lower probability of insurer failure. In this context the forecasts are both skilful and useful. The efficacy of forecast use is further explored by considering hurricane insurance. Here forecasts are shown to be useful only if very simple adjustments to pricing are made. A novel agent based model of a two company insurance industry containing many key features in the real world is presented enabling the impact of regulation and competition to be assessed. Several common practices are shown to reduce expected company lifetime.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: HA Statistics