<tt>
	      <b>mvClaim</b>
	    </tt>: an <tt>
	      <b>R</b>
	    </tt> package for multivariate general insurance claims severity modelling

Hu, Sen; Murphy, Brendan; O’Hagan, Adrian

doi:10.1017/s1748499521000099

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…Genest and Favre, 2007). An extensive literature has emerged on specifically actuarial applications of copula modelling, in credibility (Frees and Wang, 2005), stochastic reserving (Shi and Frees, 2011, Shi, 2014, Abdallah et al, 2015, and claims modelling (Czado et al, 2012, Shi and Valdez, 2014, Hu et al, 2021, Tzougas and Pignatelli di Cerchiara, 2021.…”

Section: Introductionmentioning

confidence: 99%

Copula model selection using image recognition

Tsanakas

Zhu

2021

SSRN Journal

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Section: Introductionmentioning

confidence: 99%

Copula model selection using image recognition

Tsanakas

Zhu

2021

SSRN Journal

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“…The article concludes in Section 8, summarising the main research findings and highlighting potential further advances. The package copula (Hofert et al, 2018a) is extensively used in R (R Core Team, 2018), and software implementation in R for empirical TDC estimators, copula regression and mixture of copulas is available in Hu et al (2019b).…”

Section: Introductionmentioning

confidence: 99%

Copula Averaging for Tail Dependence in Insurance Claims Data

Hu¹,

O’Hagan²

2021

Preprint

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Analysing dependent risks is an important task for insurance companies. A dependency is reflected in the fact that information about one random variable provides information about the likely distribution of values of another random variable. Insurance companies in particular must investigate such dependencies between different lines of business and the effects that an extreme loss event, such as an earthquake or hurricane, has across multiple lines of business simultaneously.Copulas provide a popular model-based approach to analysing the dependency between risks, and the coefficient of tail dependence is a measure of dependence for extreme losses. Besides commonly used empirical estimators for estimating the tail dependence coefficient, copula fitting can lead to estimation of such coefficients directly or can verify their existence. Generally, a range of copula models is available to fit a data set well, leading to multiple different tail dependence results; a method based on Bayesian model averaging is designed to obtain a unified estimate of tail dependence. In this article, this model-based coefficient estimation method is illustrated through a variety of copula fitting approaches and results are presented for several simulated data sets and also a real general insurance loss data set.

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“…Tseung et al (2021) developed the julia package, LRMoE, which enables the flexible modelling of insurance loss frequencies and severities using the Logit-weighted Reduced Mixtureof-Experts model. Within the same theme, Hu et al (2021b) introduce the mvClaim package in R, which focuses on frameworks for multivariate insurance claim severity modelling, specifically mixtures of experts with bivariate gamma distributions and finite mixtures of copula regressions. Finally, Pesenti et al (2021) present the R package SWIM, which implements a sensitivity analysis approach that allows users to produce stressed versions of their simulation models, without requiring additional model runs or full model specifications.The production of this special issue was a result of a substantial collective effort and we are proud of the result.…”

mentioning

confidence: 99%