Tail Risk Networks of Insurers Around the Globe: An Empirical Examination of Systemic Risk for G-SIIs v.s. Non G-SIIs

Abstract

In this paper, we investigate systemic risk of 157 insurers around the globe. We construct tail risk networks among these insurers using a single-index model for quantile regressions with a variable selection technique. We develop new network-based systemic risk indices, taking into account expected tail losses of insurers, direct and indirect contagion effects, and the time-varying strength of tail risk spillover. Our systemic risk indices successfully recognize global systemically important insurers (G-SIIs). We find that on average G-SIIs are more systemically relevant than non G-SIIs, particularly during the recent U.S. financial crisis. We also find a small group of non G-SIIs that are more important than G-SIIs. Our results have significant implications for systemic risk regulation.

Keywords: systemic risk, tail risk contagion, network analysis, single-index models with variable selection, the global insurance industry