Background risk model in presence of heavy tails under dependence - Konstantinides, Dimitrios G.; Passalidis, Charalampos D. (Nonlinear Analysis: Modelling and Control, 2025)
In this paper, we examine two problems on applied probability, which are directly connected with the dependence in presence of heavy tails. The first problem is related to max-sum equivalence of the randomly weighted sums in bivariate setup. Introducing a new dependence, called generalized tail asymptotic independence, we establish the bivariate max-sum equivalence under a rather general dependence structure when the primary random variables follow distributions from the intersection of the dominatedly varying and the long-tailed distributions. Based on this max-sum equivalence, we provide a result about the asymptotic behavior of two kinds of ruin probabilities over a finite-time horizon in a bivariate renewal risk model with constant interest rate. The second problem is related to the asymptotic behavior of the tail distortion risk measure in a static portfolio called background risk model. In opposite to other approaches on this topic, we use a general enough assumption that is based on multivariate regular variation.
Keywords: joint tail behavior; randomly weighted sums; tail distortion risk measure; bivariate renewal risk model; interdependence; multivariate regular variation