Estimating large losses in insurance analytics and operational risk using the g-and-h distribution

DOI10.1080/14697688.2020.1849778zbMath1479.91305OpenAlexW3126729572MaRDI QIDQ5014251

Julien Hambuckers, Marco Bee, Luca Trapin

Publication date: 1 December 2021

Published in: Quantitative Finance (Search for Journal in Brave)

Full work available at URL: https://orbi.uliege.be/handle/2268/252207

zbMATH Keywords

computational financeactuarial sciencetail analysisextreme risk and insuranceadvanced econometrics

Mathematics Subject Classification ID

Applications of statistics to actuarial sciences and financial mathematics (62P05) Statistics of extreme values; tail inference (62G32) Actuarial mathematics (91G05)

Related Items (1)

The truncated g-and-h distribution: estimation and application to loss modeling

Uses Software

• GAMLSS
• QRM
• kSamples
• gk

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Numerical maximum likelihood estimation for the \(g\)-and-\(k\) and generalized \(g\)-and-\(h\) distributions
• Estimation of stable distributions by indirect inference
• Tail index estimation and an exponential regression model
• The indirect method: inference based on intermediate statistics -- a synthesis and examples
• Encompassing and indirect inference
• On Bayesian Modeling of Fat Tails and Skewness
• Constrained Indirect Estimation
• A simple approach to the estimation of Tukey's gh distribution
• The Quantitative Modeling of Operational Risk: Between G-and-H and EVT
• Generalized Additive Models for Location, Scale and Shape
• Robust Statistics

This page was built for publication: Estimating large losses in insurance analytics and operational risk using the g-and-h distribution