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Title: Stable-1/2 bridges and insurance
Authors: Hoyle, E
Hughston, LP
Macrina, A
Keywords: Non-life reserving;Claims development;Reinsurance;Best estimate of ultimate loss;Information-based asset pricing;Lévy processes;Stable processes
Issue Date: 2015
Publisher: Polskiej Akademii Nauk, Instytut Matematyczny
Citation: Banach Center Publications, 104, 2015, pp. 95 - 120
Abstract: We develop a class of non-life reserving models using a stable-1/2 random bridge to to simulate the accumulation of paid claims, allowing for an essentially arbitrary choice of a priori distribution for the ultimate loss. Taking an information-based approach to the reserving problem, we derive the process of the conditional distribution of the ultimate loss. The “best-estimate ultimate loss process” is given by the conditional expectation of the ultimate loss. We derive explicit expressions for the best-estimate ultimate loss process, and for expected recoveries arising from aggregate excess-of-loss reinsurance treaties. Use of a deterministic time change allows for the matching of any initial (increasing) development pattern for the paid claims. We show that these methods are well-suited to the modelling of claims where there is a non-trivial probability of catastrophic loss. The generalized inverse-Gaussian (GIG) distribution is shown to be a natural choice for the a priori ultimate loss distribution. For particular GIG parameter choices, the best-estimate ultimate loss process can be written as a rational function of the paid-claims process. We extend the model to include a second paid-claims process, and allow the two pro- cesses to be dependent. The results obtained can be applied to the modelling of multiple lines of business or multiple origin years. The multi-dimensional model has the property that the dimensionality of calculations remains low, regardless of the number of paid-claims processes. An algorithm is provided for the simulation of the paid-claims processes.
ISSN: 0137-6934
Appears in Collections:Dept of Mathematics Research Papers

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