Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/167884
Authors: 
Peters, Gareth W.
Chen, Wilson Ye
Gerlach, Richard H.
Year of Publication: 
2016
Citation: 
[Journal:] Risks [ISSN:] 2227-9091 [Volume:] 4 [Year:] 2016 [Issue:] 2 [Pages:] 1-41
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.
Subjects: 
L-moments
method of moments
quantile distributions
Tukey transformations
g-and-h distribution
g-and-k distribution
tail risk
loss distributions
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by/4.0/
Document Type: 
Article
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