estimating quantile families of loss distributions for non-life insurance modelling via l-moments

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ID: 163997
2016
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Abstract
This paper discusses different classes of loss models in non-life insurance settings. It then overviews the class of Tukey transform loss models that have not yet been widely considered in non-life insurance modelling, but offer opportunities to produce flexible skewness and kurtosis features often required in loss modelling. In addition, these loss models admit explicit quantile specifications which make them directly relevant for quantile based risk measure calculations. We detail various parameterisations and sub-families of the Tukey transform based models, such as the g-and-h, g-and-k and g-and-j models, including their properties of relevance to loss modelling. One of the challenges that are amenable to practitioners when fitting such models is to perform robust estimation of the model parameters. In this paper we develop a novel, efficient, and robust procedure for estimating the parameters of this family of Tukey transform models, based on L-moments. It is shown to be more efficient than the current state of the art estimation methods for such families of loss models while being simple to implement for practical purposes.
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peters2016risksestimating Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Gareth W. Peters;Wilson Ye Chen;Richard H. Gerlach
Journal world neurosurgery
Year 2016
DOI
10.3390/risks4020014
URL
http://www.mdpi.com/2227-9091/4/2/14
Keywords Keywords not found

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