 Bootstrap Mean Squared Error of Prediction in Loss ReservingAlicja Wolny-Dominiak ()
A chapter in Contemporary Trends and Challenges in Finance, 2017, pp 213-220 from Springer Abstract: Abstract The prediction of total loss reserve in non-life insurance company is considered. One of the methods currently used in practice applies the generalized linear model (GLM). In the literature one can find the justified extension of the GLM to the hierarchical generalized linear model (HGLM) for loss reserving. A limitation in the use of the HGLM is the fact that the mean squared error of prediction (MSEP) is expressed by a complex analytical formula. An alternative to the analytical formula is to use the bootstrap procedure approximating the sampling distribution of the MSEP. This paper study two ways of bootstrap. The first one is the parametric bootstrap in which one simulates from fitted values of the HGLM. This approach is sensitive to incorrect fit of the model. Therefore the alternative is the simulation by resampling residuals and adding them back to the fitted values. The paper contains the comparison of this two approaches applying the loss triangle investigated by several authors. Bootstrap procedures are implemented in R software and the code is available to download (see http://web.ue.katowice.pl/woali/ ). Date: 2017 There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-319-54885-2_20 Ordering information: This item can be ordered from DOI: 10.1007/978-3-319-54885-2_20 Access Statistics for this chapter More chapters in Springer Proceedings in Business and Economics from Springer |
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