A tail dependence-based MST and their topological indicators in modelling systemic risk in the European insurance sector

TL;DR

This paper introduces a hybrid method combining copula-DCC-GARCH and MST to analyze systemic risk in the European insurance sector by examining tail dependence and network topology over time.

Contribution

It presents a novel hybrid approach for modeling interlinkages and systemic risk using tail dependence coefficients and MST topological indicators.

Findings

MST topological indicators can predict systemic risk.

The hybrid approach captures market phenomena effectively.

Tail dependence coefficients reflect systemic risk dynamics.

Abstract

In the present work we analyse the dynamics of indirect connections between insurance companies that result from market price channels. In our analysis we assume that the stock quotations of insurance companies reflect market sentiments which constitute a very important systemic risk factor. Interlinkages between insurers and their dynamics have a direct impact on systemic risk contagion in the insurance sector. We propose herein a new hybrid approach to the analysis of interlinkages dynamics based on combining the copula-DCC-GARCH model and Minimum Spanning Trees (MST). Using the copula-DCC-GARCH model we determine the tail dependence coefficients. Then, for each analysed period we construct MST based on these coefficients. The dynamics is analysed by means of time series of selected topological indicators of the MSTs in the years 2005-2019. Our empirical results show the usefulness of the proposed approach to the analysis of systemic risk in the insurance sector. The times series obtained from the proposed hybrid approach reflect the phenomena occurring on the market. The analysed MST topological indicators can be considered as systemic risk predictors.