An improved approach for estimating large losses in insurance analytics and operational risk using the g-and-h distribution

Marco Bee, Julien Hambuckers and Luca Trapin

No 2019/11, DEM Working Papers from Department of Economics and Management

Abstract: In this paper, we study the estimation of parameters for g-and-h distributions. These distributions find applications in modeling highly skewed and fat-tailed data, like extreme losses in the banking and insurance sector. We first introduce two estimation methods: a numerical maximum likelihood technique, and an indirect inference approach with a bootstrap weighting scheme. In a realistic simulation study, we show that indirect inference is computationally more efficient and provides better estimates in case of extreme features of the data. Empirical illustrations on insurance and operational losses illustrate these findings.

Keywords: Intractable likelihood; indirect inference; skewed distribution; tail modeling; bootstrap (search for similar items in EconPapers)
JEL-codes: C15 C46 C51 G22 (search for similar items in EconPapers)
Date: 2019
New Economics Papers: this item is included in nep-ecm, nep-ias, nep-ore and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:trn:utwprg:2019/11

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