Insurance and Interconnectedness in the Financial Services Industry

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the insurance companies into life insurers, property and casualty, (referred to as P&C hereafter), accident and health insurers, and other, based on SIC code. 22 However, we specifically identify financial guarantee insurers (XL Capital, MBIA, Radian, Assured Guaranty and AIG) and separate these firms from the P&C category. We draw the data for hedge funds from the Credit Suisse/Tremont Hedge Fund Index. We construct portfolios using market capitalization weighted averages of the individual components for the portfolios. We provide a graphical representation of the monthly returns for each portfolio in Figure 3, providing the value of $1 invested in each portfolio at the beginning of 1994; in panel A we graph the value of $1 for the four primary industry groups, as well as the portfolio of hedge funds, and the S&P500, and in panel B we graph the portfolios based on the type of insurance. We observe the following in Figure 3: **************** Insert Figure 3 here **************** • The returns of these portfolios in panel A diverge after the deregulation in the Financial Services Modernization Act of 1999, with even more divergence in the 2005-‐08 period (Panel A); there is some degree of convergence at the peak of the financial crisis in late 2008, early 2009. • There is more volatility in the brokerage/dealer industry than other financial service industries (Panel A). • The insurance industry returns are most similar to those of the general market, though the insurance portfolio tracks more with real estate and hedge funds during the 2004-‐2008 period (Panel A). 22 We use SIC codes 6310-‐6311 for life insurers, SIC codes 6330-‐6331 for property and casualty insurers and SIC codes 6320-‐6324 for accident, health, hospital and medical service plans. The remaining SIC codes are placed in the “other insurance” category are 6350(1) – surety insurance (including mortgage guaranty), 6360(1) – title insurance, and 6371 – pension, health and welfare firms. We also include SIC codes 6390(9) in the “other insurance” category to capture insurance carriers not otherwise classified and consisting of bank deposit insurance, deposit or share insurance, Federal Deposit Insurance Corporation, Federal Savings and Loan Insurance Corporation, pet health insurance and warranty insurance for autos. 8
• Within the insurance industry, the financial guarantee insurers were most volatile, outperforming the other lines of insurance following deregulation, but experiencing the greatest drop with and following the financial crisis (Panel B). Though we see correlation, convergence, and divergence patterns among these return series in Figure 3, we provide more direct methods to assess the nature of these returns, using principal component analysis and Granger causality. METHODOLOGY The question remains as to the degree of interconnectedness of insurance companies with other financial service companies, and whether the interconnectedness differs among the different types of insurance companies. We use principal components analysis to determine the strength of the relationship between the returns for insurance companies, and also to assess the degree of interconnectedness among insurance companies. The results of this analysis provide information on whether insurance companies in general or by type are systemically important. Whereas principal components analysis is useful in detecting commonality among the returns of the portfolios, there may be a lead-‐lag relationship in returns that would also suggest systemically important relationships. Therefore, in addition to principal components analysis, we also estimate Granger causality among the portfolio returns, focusing on the relation between returns to insurance companies and those of other financial services firms. PRINCIPAL COMPONENTS ANALYSIS In general terms, principal components analysis (PCA) is a nonparametric, linear transformation of a data matrix to a new coordinate system. Using PCA, we extract a structure from a data set that is otherwise not noticeable or obvious. PCA does this by restating the dataset, filtering out noise, and identifying an orthogonal set of components that explain the variation in the data. The resulting components – the principal components – are based on the assumptions of linearity. The first principal component (PCA1) is the direction in which the greatest variance lies. The second component (PCA2) is the direction in which the next largest variance lies, and so on. How many components are used? It depends on the data, but theoretically there are as many components as there 9
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Page 9: Financial guarantee insurers Financ
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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
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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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