Consolidation and systemic risk in the international insurance industry

Mühlnickel, Janina; Weiß, Gregor N. F.

doi:10.1016/j.jfs.2015.04.005

Cited by 36 publications

(17 citation statements)

References 46 publications

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“…Our paper is one of the few quantitative studies on systemic risk in the European and global insurance sector. Although SR in in the financial sector is analyzed by: Bierth et al (2015), Mühlnickel and Weiß (2015), Kanno (2016), Giglio et al (2016), Adrian and Brunnermeier (2016), Koijen (2016), Brownlees (2017), Kaserer (2018) and risk infection is studied by Hautsch et al (2015), Härdle et al (2016), Fan et al (2018), nevertheless, none of these approaches is a hybrid approach in which the possibility of combining different measures would be analyzed on such a scale as proposed in our project.…”

Section: Systemic Risk In the Insurance Sectormentioning

confidence: 99%

A Tail Dependence-Based MST and Their Topological Indicators in Modeling Systemic Risk in the European Insurance Sector

Denkowska¹,

Wanat²

2020

Risks

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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.

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Section: Systemic Risk In the Insurance Sectormentioning

confidence: 99%

A Tail Dependence-Based MST and Their Topological Indicators in Modeling Systemic Risk in the European Insurance Sector

Denkowska¹,

Wanat²

2020

Risks

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“…The second strand of literature calculates systemic risk measures directly for insurers. For example, Mühlnickel and Weiß () apply LTD as a systemic risk measure in an attempt to discover whether insurance mergers increase the contribution to the systemic risk of an insurer. The authors find only slightly significant results for the North American banking sector, indicating that insurance mergers might affect the systemic risk contribution of banks.…”

Section: Systemic Risk In the Insurance Sectormentioning

confidence: 99%

“…Generally, however, they find no evidence for an increased systemic risk contribution due to merger and acquisition (M&A) activities in the insurance sector. Furthermore, Cummins and Weiss () and Mühlnickel and Weiß () show that based on SRISK and MES, insurers’ stock prices are severely negatively affected in times of crisis. These findings do not actually contradict the results noted above because they focus on insurance companies, not on specific business activities.…”

Section: Systemic Risk In the Insurance Sectormentioning

confidence: 99%

Systemic Risk in the Insurance Sector: A Review and Directions for Future Research

Eling

Pankoke

2016

Risk Manage Insurance Review

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This article reviews the extant research on systemic risk in the insurance sector and outlines new areas of research in this field. We summarize and classify 48 theoretical and empirical research papers from both academia and practitioner organizations. The survey reveals that traditional insurance activity in the life, nonlife, and reinsurance sectors neither contributes to systemic risk nor increases insurers’ vulnerability to impairments of the financial system. However, nontraditional activities (e.g., credit default swap underwriting) might increase vulnerability, and life insurers might be more vulnerable than nonlife insurers due to higher leverage. Whether nontraditional activities also contribute to systemic risk is not entirely clear; however, the activities with the potential to contribute to systemic risk include underwriting financial derivatives and providing financial guarantees. This article is not only likely of interest to academics but also highly relevant for the industry, regulators, and policymakers.

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“…For example, Cummins and Weiss (2014) and Weiß and Mühlnickel (2014) study the effect of different factors from the IAIS methodology on the systemic risk of U.S. insurers. In addition, Mühlnickel and Weiß (2015) support the too‐big‐to‐fail conjecture for insurers by showing that insurer mergers tend to increase the systemic risk of the acquiring insurers.…”

Section: Introductionmentioning

confidence: 99%

Size is everything: Explaining SIFI designations

2016

Self Cite

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In this paper, we study the determinants of the systemic importance of banks and insurers during the financial crisis. We investigate the methodology of regulators to identify global systemically important financial institutions and find that firm size is the only significant predictor of the decision of regulators to designate a financial institution as systemically important. Further, using a cross‐sectional quantile regression approach, we find that Marginal Expected Shortfall and ΔCoVaR as two common measures of systemic risk produce inconclusive results concerning the systemic relevance of banks and insurers during the crisis.

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Consolidation and systemic risk in the international insurance industry

Cited by 36 publications

References 46 publications

A Tail Dependence-Based MST and Their Topological Indicators in Modeling Systemic Risk in the European Insurance Sector

A Tail Dependence-Based MST and Their Topological Indicators in Modeling Systemic Risk in the European Insurance Sector

Systemic Risk in the Insurance Sector: A Review and Directions for Future Research

Size is everything: Explaining SIFI designations

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