This paper demonstrates the presence of adverse selection in the group insurance market for policies that allow no individual choice. As a "conventional wisdom," group insurance mitigates adverse selection, since individual choice is minimized and group losses have less variability than individual losses. We complement this "conventional wisdom" by analyzing a group insurance scenario in which individual choice is excluded, and find that there is still adverse selection at the level of group, i.e. between-group adverse selection. Between-group adverse selection, however, disappears over time if the group renews with the same insurer for certain periods. Our results thus indicate that addressing adverse selection via group insurance is not necessarily effective enough to mitigate adverse selection, but that experience rating and underwriting based on the information that insurers learn over time are important.
We analyze the interdependencies of the three main strategic goals of many companies: growth, profitability, and safety. Extant literature suggests that the relationships among these goals are reciprocal. Therefore, we develop a simultaneous equation model to empirically test three pairs of hypotheses simultaneously and over time considering a sample of 1,988 European insurance companies over eleven years. Our results suggest that moderate firm growth has a positive im-pact on profitability; however, extremely high growth reduces profitability. Moderate firm growth also tends to reduce risk. In addition, we find evidence that insurers with relatively low profitability are risk-seeking, a result in line with prospect theory. The analysis over time shows that insurers which initially prioritize profitability over growth are more likely to reach a state of “profitable growth” than vice versa. Our results emphasize the existing results on underwriting discipline and show that all three dimensions must be considered simultaneously and in a multi-period context to fully evaluate firm performance.
We analyze the efficiency of non-life insurance companies in four of the fastest-growing markets in the world-the BRIC (Brazil, Russia, India, China) countries. An innovative feature of this paper is its incorporation of uncontrollable variables in the efficiency analysis using multi-stage data envelopment analysis (DEA). This approach captures cross-country differences, such as the political and economic environment, and allows distinguishing between managerial inefficiency and inefficiency due to environmental conditions. We find that the environment affects the efficiency of non-life insurers operating in the BRIC countries. Furthermore, in a regression of firm characteristics on efficiency scores we identify four drives of efficiency: Size, profitability, solvency, and ownership form. The results further our understanding of the insurance industry in the BRIC countries in regard to its efficiency and the environment in which it operates.
This article provides an overview and comparison of risk-based capital (RBC) requirements as they currently exist in the United States, the European Union, Switzerland, and New Zealand. These four systems are representative of different ways capital standards are implemented around the globe. The United States uses a static factor model; Switzerland considers dynamic cash-flow-based approaches; New Zealand integrates private rating agencies into its supervisory process. Other differences between these three countries include the use of different risk measures, the use of internal models, and varying consideration of operational risk and catastrophe risk. Regulators in the European Union are being influenced by all three of these approaches as they finalize the design of their new regulatory framework Solvency II. We integrate the current version of this approach in our analysis.
We compare cliquet-style interest rate guarantees used in German participating life insurance contracts across different economic environments. These guarantees are proportional to the average market interest rate at contract inception and typically set at 60% of the 10-year rolling average of government bond yields. Currently, however, in the face of prolonged low interest rates and stricter solvency regulation, the continued viability of this type of product is in question. A discussion of alternative guarantee designs is thus highly relevant. To this end, we perform a comparative analysis of contracts sold in different interest rate environments with regard to the guarantee value and show that the current practice of proportional guarantees leads to higher guarantee values the lower the market interest rate. We also observe an increased interest rate sensitivity. Additionally, alternative product designs that mitigate the interest rate dependency of the guarantee value are illustrated and assessed from the policyholder perspective.
We compare the regulatory environment for the maximum technical interest rate of life insurance contracts in four European countries and the United States. In Germany, Austria and Switzerland, the maximum rate is driven by a long-term rolling average of government bond yields and is adjusted by the regulator. In the U.S., corporate bond yields are used and the regulator is not directly involved in setting the maximum rate. The regime implemented in the United Kingdom is unique: instead of a rules-based "one-size-fits-all" approach, the maximum rate is determined by a company-specific principle-based method. We provide a comparative analysis of the different systems and conduct a numerical analysis to investigate how the maximum rate will develop under predefined interest rate scenarios. The discussion is highly relevant in light of Solvency II, a regime that may fundamentally change regulation of the maximum technical interest rate.
Das Fehlverhalten Einzelner kann im Finanzdienstleistungssektor ganze Unternehmen zu Fall bringen, wie etwa das Beispiel der Barings-Bank zeigt. Die Diskussionen um QIS 5 und Solvency II zeigen, dass auch eine qualifizierte Abbildung solcher Risiken valide und praktikabel möglich ist. Wichtig ist auf jeden Fall die Erkenntnis, dass eine Unterschätzung und Vernachlässigung von Risiken aus der Geschäftstätigkeit ernste Konsequenzen haben kann.
In this paper, we first discuss the characteristics and major benefits of the Swiss risk-based capital standards for insurance companies (Swiss Solvency Test), introduced in 2006. As the insurance industry is one of the largest institutional investors in Switzerland, changes to its asset and liability management as a result of the new regulatory framework could have striking economic effects. Thus, we further examine significant market implications for the Swiss economy due to possible changes in the asset and liability management of Swiss insurance companies. We investigate resulting effects on the Swiss capital market, focusing on bond, real estate, stock, foreign exchange markets, and the situation in case of a capital market crisis. Furthermore, we analyze potential consequences to corporate financing and product design. Most of the considered consequences result from the transition of past (in principle not risk-based) supervision to risk-based supervision and can thus be generalized to other supervision systems, in particular Solvency II.
The aim of this paper is to develop an alternative approach for assessing an insurer's solvency as a proposal for a standard model for Solvency II. Instead of deriving minimum capital requirements-as is done in solvency regulation-our model provides company-specific minimum standards for risk and return of investment performance, given the distribution structure of liabilities and a predefined safety level. The idea behind this approach is that in a situation of weak solvency, an insurer's asset allocation can be adjusted much more easily in the short term than can, for example, claims cost distributions, operating expenses, or equity capital. Hence, instead of using separate models for capital regulation and solvency regulation-as is typically done in most insurance markets-our single model will reduce the complexity and costs for insurers as well as for regulators. In this paper, we first develop the model framework and second test its applicability using data from a German non-life insurer.
