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.
We empirically analyze the costs and benefits of financial regulation based on a survey of 76 insurers from Austria, Germany and Switzerland. Our analysis includes both established and new empirical measures for regulatory costs and benefits. This is the first paper that takes costs and benefits combined into account using a latent class regression with covariates. Another feature of this paper is that it analyzes regulatory costs and benefits not only on an industry level, but also at the company level. This allows us to empirically test fundamental principles of financial regulation such as proportionality: the intensity of regulation should reflect the firm-specific amount and complexity of the risk taken. Our empirical findings do not support the proportionality principle; for example, regulatory costs cannot be explained by differences in business complexity. One potential policy implication is that the proportionality principle needs to be more carefully applied to financial regulation.
We empirically analyze the costs and benefits of financial regulation based on a survey of 76 insurers from Austria, Germany and Switzerland. Our analysis includes both established and new empirical measures for regulatory costs and benefits. This is the first paper that takes costs and benefits combined into account using a latent class regression with covariates. Another feature of this paper is that it analyzes regulatory costs and benefits not only on an industry level, but also at the company level. This allows us to empirically test fundamental principles of financial regulation such as proportionality: the intensity of regulation should reflect the firm-specific amount and complexity of the risk taken. Our empirical findings do not support the proportionality principle; for example, regulatory costs cannot be explained by differences in business complexity. One potential policy implication is that the proportionality principle needs to be more carefully applied to financial regulation.
This paper evaluates whether sophisticated or simple systemic risk measures are more suitable in identifying which institutions contribute to systemic risk. In this investigation, CoVaR, Marginal Expected Shortfall (MES), SRISK and Granger-Causality Networks are considered as sophisticated systemic risk measures. Market capitalization, total debt, leverage, the stock market returns of an institution, and the correlation between the stock market returns of an institution and the market, are considered as simple systemic risk measures. Systemic relevance is approximated by the receipt of financial support during the financial crisis and the classification, as a systemically important institution, by national or international regulators. The analyses are performed for all companies included in the S&P 500 composite index. The findings suggest that simple systemic risk measures have more explanatory power than sophisticated risk measures. In particular, total debt is found to be the most suitable indicator to detect institutions which contribute to systemic risk, according to the explanatory power and model fit. The most suitable sophisticated risk measure seems to be SRISK.
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