Dependencies and systemic risk in the European insurance sector: Some new evidence based on copula-DCC-GARCH model and selected clustering methods
Anna Denkowska, Stanis{\l}aw Wanat

TL;DR
This study analyzes correlations among major European insurance companies using copula-DCC-GARCH and clustering methods, revealing increased systemic risk during market crises and stronger correlations in turbulent periods.
Contribution
It introduces a novel approach combining copula-DCC-GARCH with clustering to identify market regimes without prior assumptions, assessing systemic risk dynamically.
Findings
Correlations among insurers are positive and intensify during crises.
Systemic risk, measured by CoVaR, is significantly higher in turbulent market regimes.
European insurance sector shows increased exposure to systemic risk during global market turbulences.
Abstract
The subject of the present article is the study of correlations between large insurance companies and their contribution to systemic risk in the insurance sector. Our main goal is to analyze the conditional structure of the correlation on the European insurance market and to compare systemic risk in different regimes of this market. These regimes are identified by monitoring the weekly rates of returns of eight of the largest insurers (five from Europe and the biggest insurers from the USA, Canada and China) during the period January 2005 to December 2018. To this aim we use statistical clustering methods for time units (weeks) to which we assigned the conditional variances obtained from the estimated copula-DCC-GARCH model. The advantage of such an approach is that there is no need to assume a priori a number of market regimes, since this number has been identified by means of…
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Taxonomy
TopicsFinancial Risk and Volatility Modeling · Insurance and Financial Risk Management · Market Dynamics and Volatility
