2018
DOI: 10.3390/risks6030086
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Moments of Compound Renewal Sums with Dependent Risks Using Mixing Exponential Models

Abstract: In this paper, we study the discounted renewal aggregate claims with a full dependence structure. Based on a mixing exponential model, the dependence among the inter-claim times, the claim sizes, as well as the dependence between the inter-claim times and the claim sizes are included. The main contribution of this paper is the derivation of the closed-form expressions for the higher moments of the discounted aggregate renewal claims. Then, explicit expressions of these moments are provided for specific copulas… Show more

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Cited by 5 publications
(3 citation statements)
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“…Furthermore, the authors have employed an analogous numerical method to tackle other Gerber-Shiu type functions, such as the Laplace transform of the time of ruin. Marri et al (2018):…”
Section: Liu Et Al (2018)mentioning
confidence: 99%
“…Furthermore, the authors have employed an analogous numerical method to tackle other Gerber-Shiu type functions, such as the Laplace transform of the time of ruin. Marri et al (2018):…”
Section: Liu Et Al (2018)mentioning
confidence: 99%
“…To illustrate, in non-life insurance, the same catastrophic event (e.g., a flood or an earthquake) may lead to frequent and high losses. Inspired by such observations, Marri et al (2018) study discounted renewal aggregate claims under full dependence structure: they allow dependence among the inter-claim times, dependence among the claim sizes, and also dependence between the inter-claim times and the claim sizes.…”
Section: Marri Et Al (2018)mentioning
confidence: 99%
“…Sarabia et al (2016) give explicit formulas for the probability density function of S n for some multivariate mixed exponential distributions for which the dependence structure is an Archimedean copula. Recently, Marri et al (2018) derive explicit expressions for the higher moments of the discounted aggregate renewal claims with dependence.…”
Section: Introductionmentioning
confidence: 99%