Continuous Chain Ladder: Reformulating and generalizing a classical insurance problem

Martinez-Miranda, M. D., Nielsen, J. P., Sperlich, S. & Verrall, R. J. (2013). Continuous Chain Ladder: Reformulating and generalizing a classical insurance problem. Expert Systems with Applications, 40(14), pp. 5588-5603. doi: 10.1016/j.eswa.2013.04.006

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Abstract

The single most important number in the accounts of a non-life insurance company is likely to be the estimate of the outlying liabilities. Since non-life insurance is a major part of our financial industry (amounting to up to 5% of BNP in western countries), it is perhaps surprising that mathematical statisticians and experts of operational research (the natural experts of the underlying problem) have left the intellectual work on estimating this number to actuaries. This paper establishes this important problem in a vocabulary accessible to experts of operations research and mathematical statistics and it can be seen as an open invitation to these two important groups of scholars to join this research. The paper introduces a number of new methodologies and approaches to estimating outstanding liabilities in non-life insurance. In particular it reformulates the classical actuarial technique as a histogram type of approach and improves this classical technique by replacing this histogram by a kernel smoother.

Item Type: Article
Additional Information: NOTICE: this is the author’s version of a work that was accepted for publication in Expert Systems with Applications. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Expert Systems with Applications, 40 (14), 2013, pp.5588–5603 http://dx.doi.org/10.1016/j.eswa.2013.04.006
Uncontrolled Keywords: Chain Ladder, Claims, Reserves, Reserve Risk, Multiplicative bias correction, Density estimation, Crossvalidation, smoothing, kernal
Subjects: H Social Sciences > HG Finance
Divisions: Cass Business School > Faculty of Actuarial Science & Insurance
URI: http://openaccess.city.ac.uk/id/eprint/3757

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