Multivariate subexponentiality and interplay of insurance and financial risks in a renwal risk model

TL;DR

This paper develops asymptotic formulas for the probability of large aggregate claims in a multivariate risk model with financial investments, considering various dependence structures and extending known results to new classes of distributions.

Contribution

It introduces new asymptotic results for multivariate subexponential distributions in risk models with financial and insurance risks, including cases with dependence and risky investments.

Findings

Asymptotic expression for rare event probabilities in multivariate risk models.

Extension of subexponential distribution classes with mild conditions.

Explicit relations for models with marginal distributions and dependence structures.

Abstract

In this paper we consider a multivariate risk model with common renewal process, while the logarithmic returns of the insurers investment portfolio, are described by a Levy process. In the two main results are established an asymptotic expression for the entrance probability of the discounted aggregate claims in some rare sets x A. This asymptotic expression highlights the multivariate linear single big jump principle in asymptotic behavior of these probabilities. In the first result, we are restricted in the case where the insurer makes risk free investments, and hence we consider a non-negative Levy process. We assume that the claim vectors follow a distribution from a class, introduced here, and represents a negligibly smaller subclass of multivariate subexponential distributions, since the additional requirement for positive lower Karamata index, looks as a mild condition. Further, we consider that the insurance and financial risks, satisfy a weak dependence structure. In the second result, we allow arbitrarily dependence between the two risks, and we assume that the distribution of their product, at each renewal epoch, belongs to the intersection of the class of multivariate subexponential positively decreasing distributions with multivariate dominatedly varying distributions. In this theorem we also permit risky investment, putting a condition to Laplace exponent of the Levy process. We also note that even in the special one-dimensional subcase the main results are new. Furthermore, we present two examples, where we demand only conditions for the marginal distributions of both risks and their dependence structure. Both examples, under the restriction on multivariate regularly varying distributions provide more explicit and elegant relations in relation with that established in the main results.