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...this study 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 RAR's proxying some factors that induce finite risk for part of the small insurer sample, and other factors that favor the excessive risk hylpothesis.
INTRODUCTION
The context of this article is the capital structure strategy of U.S. life insurers during the 1990s. Capital structure decisions are made within the framework of a panoply of enterprise risks. For life insurers, there are two major categories of enterprise risks aligned with the two principal activities of life insurers: investing and underwriting. Our study provides a simplified but appropriate framework for imbedding the capital structure decision of life insurers into the spectrum of their investing and underwriting risks. We do not essay a detailed enumeration of various enterprise risks, but keep the categories of risks at a general level. Within this framework, our purpose is to compare two different approaches that purport to measure aspects of investment risk. One measurement approach derives from the traditional regulatory concern with avoiding insolvency. The other approach is oriented toward the risks of optimizing investment opportunities. Each has its proponents in the literature.
For the regulatory-associated approach, we use a measure called regulatory asset risk (RAR) that is closely related to the C-1 risk of the life-risk-based capital law. For the investment-opportunity-related approach, we introduce a volatility-of-returns-based measure called opportunity asset risk (OAR). We view RAR and OAR neither as potential competitors nor as complementary, but as somewhat overlapping. We expect them to provide different insights into the capital structure decision. We compare RAR and OAR in the same manner as in a controlled scientific experiment: by swapping one for the other in a model in which everything else remains the same--the same response variables, the same predictors, the same data. Thus the differences in model results can be attributed to the swap. We believe this is the first such comparison of proposed proxies in capital structure studies.
To provide a model for our framework, we use structural equation modeling (SEM). Introduced into capital structure studies by Titman and Wessels (1988), but subsequently largely neglected, SEM deals appropriately with several methodological issues for our study that are not dealt with simultaneously by other models. In particular, SEM provides for multiple equations to describe insurer behavior, for autocorrelated panel data, and for unobservable latent factors that underlie measured proxies. In addition, as noted by Titman and Wessels, the factor feature of SEM mitigates measurement issues arising from the use of imperfect proxies.
Our major findings are striking and present possible policy and managerial implications: RAR has no effect on the capital structure of small insurers, although it does on large insurers. By contrast, OAR affects the capital structure of both large and small insurers, but has more effect on large insurers. There are also temporal differences, before and during the late bull market.
Insurers are engaged in two major activities: investing and underwriting. Risks from those activities are represented in the balance sheet by assets (investing) and liabilities (underwriting). On the asset side, insurers maintain substantial portfolios generated from premiums collected and capital raised. These portfolios are invested in bonds, stocks, mortgages, real estate, and other assets of varying risks. (1) On the liability side, risk arises from the nature and volume of products sold. Annuity contracts represent different liability risks than do health and accident policies.
In empirical studies, risks are complex constructs that cannot be measured directly and thus are always proxied. Our focus in this article is on two composite proxy measures of asset risk, distinguished by their orientation. One originates from a regulatory tradition oriented toward insolvency risk, that is, the objective of minimizing the risk of failure or ruin from investing activities. We call this measure RAR. To calculate RAR, we approximate the C-1 component of life risk-based capital. Such measures have been used in capital structure studies by Baranoff and Sager (2002 and 2003) and Shrieves and Dahl (1992) for banks. The other proxy originates from an orientation toward maximizing firm value and reflects the risks of investing to optimize asset returns. We call this risk measure the OAR. Although OAR per se is an innovation of this study, a somewhat similar measure was used by the capital structure study of Cummins and Sommer (1996). OAR measures volatility of an insurer's potential investment returns. Actual returns are not used (and are not available). Instead, returns are calculated as though portfolio components were invested in related investment indices. OAR is motivated by the use of returns volatility to calibrate investment risk in portfolio theory. (2) Given the orientations of RAR and OAR, one might interpret RAR as theoretically intended to reflect downside risks of investing and OAR as intended to reflect opportunity for both gain and loss. But as we shall see, there is considerable empirical overlap.
RAR and/or OAR potentially represent the category of asset risks for life insurers. As potential representatives for the category of product risks, we measure the extent of insurer involvement with annuity and health products, representing relatively less risk (annuities) and relatively more risk (health). These measures were used in some form in Baranoff and Sager (2002, 2003). It could be argued that loss ratios might better represent liabilities-side risks, but loss ratios are not available to us.
To represent elements of the capital structure decision that are not among the primary risks for life insurers, we include the most important controls from previous studies: size, organizational form, and group membership. Size is used both as an explicit control and as a stratum. We stratify the life insurance industry by size to examine potential qualitative differences between large and small insurers that may have significant policy and managerial effects (see Baranoff, Sager, and Witt, 1999). We also compare the years 1994 through 1996 with 1998 through 2000 in order to gauge the potential effects of the great bull market of the late 1990s.
For capital structure, we examine the ratio of book capital to total invested assets. It would be better to use the market value of equity. But since most life insurers are not publicly traded, market value is not available.
An enumeration of the spectrum of enterprise risks to which life insurers are subject would be a long list. Moreover, such risks are not neatly distinct but overlap in ways difficult to depict. The empirical topology of risk is even messier. Any observed risk measure most likely embodies significant components from a variety of enterprise risks. Therefore, any proxy for a portion of the risk spectrum could be viewed as a mixture of underlying theoretical risks, some of which may not be directly measurable (see Titman and Wessels, 1988). By taking a factor view of risk, we mitigate some of these measurement issues.
Moreover, our concern in this article is not with mapping the geography of risk per se, but with the relationship of risk to capital structure. Some risks that occupy large territories in risk space may not be especially important for capital structure. (3) Our focus is: Given whatever overall role that risks play in capital structure decisions, how do OAR and RAR compare and contrast as proxies for asset risks? If our model failed to represent regions of risk space that are important for capital structure decisions, then it would be appropriate to criticize our findings as potentially misleading on account of the distorting effects of missing variables. A priori, we do not know what proportion of the capital effects of various asset risks can be captured by OAR and RAR. Neither do we know what proportion of the capital effects of various product risks can be captured by our measures. A posteriori, however, it seems that our framework captures sufficient explanatory power so that the scope for distortion by unenumerated risks is greatly reduced.
The theoretical backdrop for our analysis is the set of capital structure theories that were summarized by Harris and Raviv (1991) in general and more particularly by Cummins and Sommer (1996) for the property and casualty insurance industries and Baranoff and Sager (2002, 2003) for the life and health insurance industries. Harris and Raviv surveyed the body of the theoretical and empirical capital structure studies that include agency theory, asymmetric information, and product/input theories.
As explained later in the hypothesis section, the prior studies of insurers' capital/risk relationship for both the property and casualty and the life and health insurance industries suggest that the industry as a whole operates within the finite risk rather than the excessive risk paradigm. Cummins and Sommer (1996) for the property and casualty insurance industry and Baranoff and Sager (2002, 2003) for the life and health insurance industry showed a positive interrelationship between various risks and capital, in line with the finite risk hypothesis. Their studies were industry-wide, without segmentation. Cummins and Sommer used a combined asset and product risk proxy measure that is based on financial market returns and loss ratios. Baranoff and Sager unbundled the risk measures into asset risk and product risk. For asset risk, they used only...
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