Many studies suggest the ability of the NAIC Risk-Based Capital ratio (RBC ratio) to predict insurer insolvency. Based on the US life insurer (insurer) data for the period of 2005 to 2008, this study finds explanatory variables that have a statistically significant relationship with the RBC ratio. Advantages of panel data over cross-sectional and time series data analysis are exploited to make valid inference on coefficients of the explanatory variables. Testing for unobserved insurer and time effects and for dependence between these effects and the explanatory variables indicates the appropriateness of the fixed insurer and time effects model. Based on the ordinary least squares estimates, it is found that insurers' size, capital-to-asset ratio, and return on capital have a statistically significant relationship with the RBC ratio. Additionally, health product, annuity product, opportunity, and regulatory risks of insurers are related to the RBC ratio. Accounting for heteroscedasticity and autocorrelation for a given insurer yields the same coefficient estimates, but increased standard errors. / text

This study focused on identifying factors that significantly affect the potentially insolvent status of life insurers. The potentially insolvent status is indicated based on insurer’s Risk-based capital ratio (RBC ratio) reported in the National Association of Insurance Commissioners (NAIC) database of life insurers’ annual statements. A logistic regression analysis is performed to explore the relationship between the RBC insolvent indicator and a set of explanatory variables including insurer’s size, capital, governance structure, membership in a group of affiliated companies, and various risk measures during the 2006-2008 period. The results suggest that the probability of potential insolvency for an individual insurer is significantly affected by its size, capital-to-asset ratio, returns on capital, health product risk and proportion of products reinsured. It could be also possibly affected by the insurer’s regulatory asset risk. However, the results indicate that the probability is not significant related to the insurer’s annuity product risk, opportunity asset risk, governance structure and its membership in a group of affiliated companies. On average, by holding all other explanatory variables constant, every 1% increase in total assets will result in a decrease of 0.19 to 0.36% on the odds of potentially insolvent rates; every 0.01 unit increase in capital-to-asset ratio will result in a decrease of a multiplicative factor of 0.951 to 0.956 on the odds; every 0.01 unit increase in return on capital will result in a decrease of a multiplicative factor of 0.984 to 0.985 on the odds; every 0.01 unit increase in health product risk will result in an increase of a multiplicative factor of 1.021 to 1.031 on the odds; and every 0.01 unit increase in proportion of products reinsured will result in an increase of a multiplicative factor of 1.015 to 1.026 on the odds. The assumptions of independency and absence of harmful multicolliearity are both valid for this logistic model, suggesting that the model is adequate and the conclusion is warranted. Although the potentially insolvent indicator, instead of the real insolvent indicator is used, this model could still be useful to identify the significant factors which affect life insurers’ potentially insolvent status. / text