Etti G. Baranoff

The Relations Among Asset Risk, Product Risk, and Capital in the Life Insurance Industry

This paper explores the relation between capital and risk in the life insurance industry in the period after the adoption of life risk-based capital (RBC) regulation. To examine this issue, we use a simultaneous-equation partial-adjustment model. Three equations express the interrelations among capital and two measures of risk: product risk and asset risk. The asset-risk measure used in this paper reflects credit or solvency risk as in RBC. Product risk assessment for life insurance products is rationalized by transaction-cost economics - contractual uncertainty. A significant finding is that for life insurers the relation between capital and asset risk is positive. This agrees with prior studies for the property/casualty insurance industry and some banking studies. But the relation between capital and product risk is negative. This is consistent with the hypothesized impact of guarantee funds in other studies. The contrast between the positive relation of capital to asset risk and the negative relation of capital to product risk underscores the importance of distinguishing these two components of risk.

Capital and Risk Revisited: A Structural Equation Model Approach for Life Insurers

The role of risk in the capital structure decision of firms is a vast topic in finance. Commonly, models of the interrelationship between risk and capital enumerate as many risk factors as possible by appropriate proxies, with the goal of detailing their individual effects. In this study of the life insurance industry for 1994 through 2000, we take a broader, holistic view of enterprise risk, identifying two groups of insurer risk factors that arise from the major activities of life insurers: investing and underwriting. We call the group of risk factors associated with investing asset risk, and the group associated with underwriting product risk. After specifying other important determinants of capital structure as controls, we allow all other risk factors to find expression in residual error. Within this framework, our focus is to compare two candidate measures for the role of proxy for asset-related risks. One measure, called regulatory asset risk (RAR), derives from the regulatory tradition of concern with solvency and is related to the C-1 component of risk-based capital. The other measure, called opportunity asset risk (OAR), is motivated by traditional finance concerns with market risk and reflects volatility of returns. Product-related risks are proxied by underwriting exposures in different product lines. We employ structural equation modeling (SEM), which uses longitudinal factor analysis. SEM is an innovative technique for such studies, in dealing effectively with multiple structural equations, autocorrelated panel data, unobserved underlying factors, and other issues that are not simultaneously addressed in other methodologies. We find that RAR and OAR are not equivalent proxies for asset risks. Although overlapping to some extent, each illuminates different aspects of the asset risk-capital interrelationship. In particular, RAR does not seem to affect the capital structure decision of small firms, although OAR does. We interpret this to suggest that small firms as a whole are not as sensitive in their capital decisions to the proxy of regulatory concerns as to the proxy of market opportunity. This contrasts with large insurers, for whom both RAR and OAR have significant effects on capital that comport with the finite risk hypothesis. More detailed analysis suggests that the lack of effect of RAR for small insurers may result from RARs proxying some factors that induce finite risk for part of the small insurer sample, and other factors that favor the excessive risk hypothesis.

Industry Segmentation and Predictor Motifs for Solvency Analysis of the Life/Health Insurance Industry

This paper contributes one principal idea to the methodology of solvency studies for the life insurance industry. The idea is grouping, which is applied in two different ways. First, companies are grouped into industry segments by insurer specialization or by size. Second, predictor variables are grouped into thematically related motifs. The primary benefits of grouping are improved solvency prediction and improved interpretation of predictors. Improved prediction results from industry segmentation; improved interpretation from predictor motifs. The models are developed by the technique of cascaded logistic regression, which forecasts solvency status on the basis of motifs, rather than of individual variables. A key finding is that the segments differ in their significant motifs in anticipated ways. For example, investment motifs are important for solvency in the Life and Annuities segments, but not in the Health segment. A similar pattern characterizes the difference between large and small insurers. The study covers the 1990 through 1992 time period, when there were a historically high number of troubled companies.

A Semiparametric Stochastic Spline Model as a Managerial Tool for Potential Insolvency

This study introduces a flexible nonlinear semiparametric spline model, new to solvency studies, as a tool for managerial discretion and regulatory oversight. The model has a linear component and a nonlinear component that uses stochastic splines. The study focuses on the functional relationship between regressors and the probability of financial distress as an object for managerial action. Leverage plots are provided to analyze the potential effect of decisions to modify firm levels of financial variables. If the true relationship between regressors and the response is not linear, then managerial efforts to rectify deteriorating financial conditions can be misinformed by reliance on a linear solvency model. The leverage plots adjust to the firms position within the industry and its specific levels of various financial variables. A five-regressor semiparametric spline model is shown to yield insights into the behavior of the risk of financial distress probabilities that linear parametric models suppress. The model also classifies and validates well in comparison with recent insolvency studies and as well as parametric logit and probit models on the same data.

Market Discipline in Life Insurance: Insureds' Reaction to Rating Downgrades in the Context of Enterprise Risks

There is serious debate about the effectiveness of the market to discipline regulated industries like banks and insurance. In this paper, we examine empirically the reaction of consumers to changes in the financial ratings of life insurers. We find that downgraded life insurers in the period 1994-2003 experience a decline in demand for life insurance policies in the year after a downgrade. This occurs in spite of mitigation strategies like adjusting premium rates. It occurs in the context of a spectrum of enterprise risk measures and other controls such as financial risk (leverage), product risk (premiums written in various lines), asset risk (a proxy called opportunity asset risk), operational risks (use of derivatives, organizational structure and distribution system), regulatory risk (risk-based capital ratio), and size (total assets). Our findings comport with similar research in banking on the reactions of depositors to changes in the risk taking of banks. Moreover, our model is able to estimate the magnitude of the decline in demand for individual insurers. Using the notion of Granger causality, we are also able to demonstrate that the direction of the relationship flows from ratings downgrade to decline in demand, rather than the reverse. These findings have potentially strong impacts on the issue of appropriateness of risk models that regulators and the industry are developing in parallel: The parallel tracks may need to align with a third rail.

DETERMINANTS IN RISK-FINANCING CHOICES: THE CASE OF WORKERS COMPENSATION FOR PUBLIC SCHOOL DISTRICTS

Previous research into workers compensation risk-financing choice has considered only the alternatives of risk transfer in the form of full insurance and risk retention the form of self-insurance. However, self-insurance is only one of two forms of risk retention. How determinants influence the choice of the other form of risk retention, the large-deductible plan, was not examined in prior research. This study empirically examines all three risk-financing choices for public entities, using a sample of Texas school districts and their workers compensation coverage decisions. It finds that some determinants contribute differently to the two risk-retention choices: severity of loss, cost of insurance, and size of firm influence the choices of these two programs in opposite directions, relative to the choice of full insurance. Also, cost of insurance and frequency of losses were found to influence the choice of full insurance in the opposite direction from that expected. That these findings fail to agree with expectations suggests a need to refine the theory to differentiate between the two risk-retention programs, especially for public entities.

Capital and Risks Interrelationships in the Life and Health Insurance Industries: Theories and Applications

This chapter summarizes the theory and empirics of capital structure for life insurers and health insurers. The large literature on explaining capital structure for nonfinancial firms is not explicitly applicable to insurers because of the differences in the structures, setting, and the premium financing of insurers. Nevertheless, the fundamental capital structure question is carefully adapted from the debt versus equity theories used for nonfinancial firms to the risk versus capital theories in insurance. The switch follows naturally from the customer-based model of insurer financing. The predictions of agency theory, transaction-cost economics, pecking order, debt–equity trade-off, bankruptcy cost, risk subsidy, and other theories are developed and summarized into the “finite risk” and “excessive risk” hypotheses. The interrelationships between the capital and risks of life and health insurers are examined in this light. For the last two decades, insurers operated under the finite risk paradigm, even during the 2008 financial crisis.

Implications of the Capital and Risk Interrelationship of Health Insurers

As financial intermediaries in the health care delivery system, U.S. health insurers will be strongly affected by sweeping legislative reforms adopted in 2010, both in health care and in financial regulation. In this paper, we provide useful context for interpreting these reforms and for understanding the financial and risk behavior of health insurers. In particular, we model the interrelationship among health insurers’ capital, investment risk, and product risk in a context of other risks and controls including loss ratios, which are a core ingredient of the reform. We find that health insurers have acted in a risk-limiting manner in the years preceding the reforms. However, the elasticity of capital with respect to both investment risk and product risk is low, suggesting both that the capital structure of health insurers is relatively insensitive to significant change in investment risk and product risk and that the industry is close to the tipping point between risk-limiting and risk-seeking. Healthcare reforms add substantially to health insurer product risks. Especially with newly mandated minimum loss ratios, it is possible that health insurers will move into an excessive risk regime, thus destabilizing these key intermediaries in the healthcare system.

Variable Annuities with Guaranteed Living Benefits: A Capital Structure Impact for Life Insurers?

Traditional variable annuities build retirement income for annuitants through investments in stocks and bonds. These annuities are variable because their performance depends upon the performance of uncertain financial markets. The risk of poor performance lies solely upon annuitants, rather than upon the life insurers that market the products. The last ten years have seen the dramatic growth of a new type of variable annuity that transfers some of the investment risk to the insurers by offering guarantees. Variable annuities with guarantees bundle a traditional variable annuity with one or more contractual guarantees of (1) increase in annuitant income, (2) increase in accumulation value, or (3) annuitant withdrawal rights – regardless of the actual performance of equity and fixed income markets. For life insurers, the risk of such guarantees is a relatively new dimension of their product risk. In this article we explore how U.S. life insurers’ involvement in variable annuities that offer guaranteed living benefits (VAGLB) impacts their capital structure. To assess the risk of VAGLB guarantees, we introduce a new guarantee risk metric that is proxied by the actuarial deficiency of insurer reserves for the guarantee obligations. We apply standard capital structure models to assess the impact of guarantee risk on capital in the context of other enterprise risks and controls for the years 2006 and 2007 (before the credit crisis of 2008/2009). For life insurers that underwrite VAGLB, we find that a regime of “excessive risk” prevails for VAGLB guarantee risk. That is, capital tends to decrease with increasing VAGLB guarantee risk, ceteris paribus. This unexpected finding may be explained by the elevated use of derivatives among VAGLB underwriters. Life insurers may view their derivative hedging activity as an adequate alternative to capital accumulation for managing the risks of VAGLB.

Risk Management and Insurance after 9/11

Did September 11, 2001, change every thing in the world of risk management and insurance? Seven years after the horrendous event, the answer is “no.” No, because of the impact of the subsequent mega natural catastrophes of 2005 with Hurricanes Katrina, Rita, and Wilma and the current 2008 major credit and housing crisis. As shown in table 10.1, the insured losses of the 2005 hurricanes surpassed the insured losses of 9/11. However, the hurricane losses did not hurt the net worth of insurers as did the losses of 9/11. As can be gleaned from figure 10.1, 2001 was the worst year for the insurance industry. The return on equity dipped into negative territor y. In terms of the combined ratio—the indicator of the well being of the industry (the ratio of losses plus expenses to premiums)—2001 was the worst year with the largest combined ratio, at 115.8, for the property/casualty (P/C) insurance industry. But, this resilient industry recovered quickly, as shown in figure 10.2. By 2006, the industry recovered and the combined ratio declined to a lowest level of 92.5. Figure 10.2 reflects the strong capacity of the P/C insurance industry to endure major setbacks. At the time of writing this chapter, exactly three years after the mismanagement of Hurricane Katrina, Hurricane Gustav is fast approaching to the same region. Despite much improved risk handling and communication by all stakeholders, “the Federal Emergency Management Agency (FEMA)’s ability to marshal its forces quickly is still lagging.”1