Insurance and Interconnectedness in the Financial Services Industry

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component. Of specific interest is the behavior of different sectors of the insurance industry. Life insurers consistently primarily load on the first component in all time periods except the last where they also exhibit sensitivity to the fourth component. The second most consistent subsector is accident & health that loads primarily on the first and fourth components with some sensitivity to the third in the deregulation period. Financial guarantee and property & casualty insurers load on the first component in three of the four sub-‐periods; though not the same three. Financial guarantee insurers also are sensitive to the fourth component while property & casualty insurers are sensitive to the second and third components in two of the sub-‐periods. Examining our results by time periods we find prior to, and in the years immediately following passage of, the Financial Modernization Act all four insurance subsectors were sensitive to the first component; the return on the market. During the housing boom, most insurers continued to load on the first component with the exception of financial guarantee insurers who exhibited a negative relationship in the second component, a proxy for diversification. After the demise of Lehman Brothers and Bear Stearns, property & casualty insurers were more sensitive to the diversification proxy. In summary, principal component analysis documents that insurer types respond differently to various principal components across categories and time. In the latter sample period, financial guarantee and property & casualty insurers have large exposures to the second and third components while life and accident & health firms have greater exposure to the first and fourth components. During the crisis years from 2009 to 2010, life, accident & health, and financial guarantee insurers revealed significant sensitivity to the return on the market while property & casualty insurers were more sensitive to diversification effects. Overall, the evidence points to the need to classify insurers properly in order to get truly meaningful results regarding systemic riskiness. GRANGER CAUSALITY We report the p-‐values for the Linear Granger Causality analysis for the entire period 2004-‐2010 and for the four sub-‐periods. In Table 3 we use raw returns and in Table 4 we use market adjusted returns. Considering all insurance companies, there is a causality relation between insurance companies and other financial companies, post-‐deregulation. Using market adjusted returns, the relation for the overall insurance industry is primarily unidirectional, from other firms to insurance companies, with the exception of the crisis years, 2008-‐ 2010 where there is a bidirectional relationship between insurers and other financial companies. The 16
directionality is reversed in the case of life insurers. Examining by the type of insurance company, financial guarantee insurers, accident & health insurers, and property & casualty insurers exhibit bi-‐ directional relationships in at least one period. In the financial crisis period of 2008-‐2010, there is a bi-‐ directional relationship between other financial companies and financial guarantee and accident & health insurers while property & casualty and life insurers led the rest of the financial industry. Of note, life insurers never lagged but were leaders in all but one period, the period following deregulation. Therefore, the result that Billio, Getmansky, Lo and Pelizzon observe regarding the unilateral and bilateral relations between insurance companies and other financial service firms is likely driven by financial guarantee, property & casualty and life insurers. Summary Using both principal components analysis and Granger causality, we show that insurers have become more interrelated with other financial services firms over time; and, that in order to get to the roots of the correlation, there is a need to classify firms within the insurance sector properly. In addition, we find evidence that hedge funds are most sensitive to the second component; the proxy for diversification. In analyzing the correlation amongst return indexes constructed from monthly returns, principal components analysis supports the claim that financial firms are different and must be classified accordingly. When attempting to determine the causality of this interrelatedness, all specifications of linear and nonlinear Granger causality show significant causal relationships for each insurer type at some time period in our study. However, some insurers are leaders, some are laggards, and some have bi-‐ directional relationships with other financial firms. Upon running these tests on the residual returns, it was found that financial guarantee and life insurers had the greatest number of significant causal relationships during the study period. CONCLUDING REMARKS We extend the analysis of the interconnectedness of the financial service industry with our analysis. By breaking out the insurance companies by type, we get a better idea of the drivers and degree of interconnectedness. 17
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Page 7 and 8: Life insurers **************** Inse
Page 9 and 10: Financial guarantee insurers Financ
Page 11 and 12: • Within the insurance industry,
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Page 15 and 16: are considered significant (Tabachn
Page 17: We graph the first two principal co
Page 21 and 22: REFERENCES Acharya, Viral V., Lasse
Page 23 and 24: TABLE 1 Principal Components Analys
Page 25 and 26: Table 2, Panel B, continued Eigenve
Page 27 and 28: TABLE 3, CONTINUED PANEL E PROPERTY
Page 29 and 30: TABLE 4, CONTINUED PANEL E PROPERTY
Page 31 and 32: FIGURE 2 Proportion of insurance as
Page 33 and 34: FIGURE 4 Principal components analy
Page 35: FIGURE 6 Variation explained by pri

Insurance and Interconnectedness in the Financial Services Industry

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