Uniform asymptotics for a multidimensional renewal risk model with random number of delayed claims and multivariate subexponentiality
Dimitrios G. Konstantinides, Charalampos D. Passalidis, Meng Yuan

TL;DR
This paper develops uniform asymptotic estimates for a multivariate renewal risk model with delayed claims, under multivariate subexponential tail assumptions, applicable over finite and infinite horizons.
Contribution
It introduces new asymptotic results for the entrance probabilities in a multivariate risk model with delayed claims, including explicit formulas and closure properties of subexponential distributions.
Findings
Asymptotic estimates are valid under multivariate subexponential assumptions.
Results include explicit formulas and relaxations for claim distribution classes.
Numerical studies confirm the accuracy of the asymptotic approximations.
Abstract
In this paper we examine a multivariate risk model, with common renewal counting process, constant interest rate, and each claim vector is accompanied by a random number of delayed claim vectors. The interest is focused on the asymptotic behavior of the entrance probability of the discounted aggregate claims into some rare-sets, over a finite and an infinite time horizon. Our results study the the case where the main claims and the delayed claims have in some sense, asymptotic equivalent tails, but also the case where the delayed claims are negligible with comparisons with the main claims. More precisely, our estimations over finite time horizon are equipped with local uniformity, and are valid under the assumption of multivariate subexponential distributions for the claim distributions. On the case of infinite time horizon we need a mild restriction on the distribution class of…
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