Article (Scientific journals)
Estimating large losses in insurance analytics and operational risk using the g-and-h distribution
Bee, Marco; Hambuckers, Julien; Trapin, Luca
2021In Quantitative Finance, 21 (7), p. 1207-1221
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Keywords :
Actuarial Science; Tail Analysis; extreme risk and insurance
Abstract :
[en] 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 than the maximum likelihood method in case of extreme features of the data. Empirical illustrations on insurance and operational losses illustrate these findings
Disciplines :
Quantitative methods in economics & management
Author, co-author :
Bee, Marco
Hambuckers, Julien ;  Université de Liège - ULiège > HEC Liège : UER > UER Finance et Droit : Finance de Marché
Trapin, Luca
Language :
English
Title :
Estimating large losses in insurance analytics and operational risk using the g-and-h distribution
Publication date :
2021
Journal title :
Quantitative Finance
ISSN :
1469-7688
eISSN :
1469-7696
Publisher :
Taylor & Francis, United Kingdom
Volume :
21
Issue :
7
Pages :
1207-1221
Peer reviewed :
Peer Reviewed verified by ORBi
Available on ORBi :
since 06 November 2020

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