Insurance and Interconnectedness in the Financial Services Industry

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and Elton, Gruber, Brown and Urich (1978). The primary difference between what we do and what is done in these other studies is that we are dealing with portfolio correlations and they were dealing with individual security return correlations. As with these studies, we find that the mean of the correlation factor outperforms the market index in explaining returns. A higher value of the pair-‐wise correlation indicates that the returns are changing together, whereas a lower value of the mean pair-‐wise correlation indicates that the returns are less in-‐synch with one another. Therefore, if a principal component is correlated with the mean correlation, this means that the component represents how well the returns on financial service firms move together. Our observation is that the first component represents the market, as well as financial service firms in general (hence, a market and industry component). This implies that there is a role for both the market index and an industry-‐specific influence in explaining returns of financial service firms. PCA2 explains 10-‐26% of the portfolios return variation, with this component explaining more variation in the period just following deregulation. Semaan and Drake (2012) document that the second component may relate to diversification potential, as proxied by the variance of the correlations among the portfolios. Similar to Semaan and Drake, we construct the variance of the correlations by again calculating 24-‐month rolling correlations of portfolio returns for each pair of portfolios; the variance of the correlations is simply the variance of the pair-‐wise correlations. Though this approach may appear simplistic, it results in a variable that explains returns of the financial service portfolios. We can see the relation between the principal components and the variance of the correlations. The pair-‐wise correlation between PCA2 and the variance of the correlation is 0.8906, which is significantly different from zero at the 1 percent level. One interpretation is that the second component is a proxy for diversification opportunities; the greater the variance of correlations, the more the diversification potential, and the lower the variance of correlations, the fewer the diversification opportunities. 27 This interpretation also explains why hedge funds’ return load positively on the second component: when opportunities within the financial industry are broad – as indicated by a high variance of the return correlations – the hedge funds, who are not equity capital constrained by regulators, capitalize on these opportunities. 27 This conclusion is consistent with Bali and Hovakimian (2007), who estimate principal components on a large sample of security returns and find that the second principal components is a proxy for volatility risk. 14
We graph the first two principal components and the possible explanations of what they represent in Figure 5. As you can see, PCA1 tracks the mean correlation, whereas PCA2 tracks well with the variance of the correlations. **************** Insert Figure 5 Here **************** Regarding additional components, the associations are less obvious. For example, factors that affect the general economy, such as industrial production and retail sales, tend to be correlated to PCA3, whereas PCA4 is negatively and significantly correlated with changes in interest rates, and PCA5 is positively related to the level of interest rates. 28,29 PCA AND SPECIFIC INSURANCE LINES We examine specific insurance lines in Figure 6 and Table 2. We illustrate the ability of the components to explain the variation in returns in Figure 5; PCA1 is just as dynamic when classifying insurers according to their lines of business; however, it captures slightly less of the variation, ranging from mid-‐50% to low-‐80%. Further proof of the decrease in explanatory power of PCA1 can be seen when breaking the sample into four distinct time periods, as we show in Table 2 where the first principal component drops to 76% and 62% respectively. The first two principal components explain 75% to 85% of the variation (Table 2). Another point of interest is the shape of Figure 6 when compared to Figure 4. From mid-‐2008 through mid-‐2009, there is a large spike downwards and then back upwards in the explanatory power of PCA1 for the specified sample. Outside of this spike, the two samples have relatively similar shapes for PCA1. **************** Insert Figure 6 Here **************** **************** Insert Table 2 Here **************** As with the initial PCA analysis, from 1994 to 2010, each financial service portfolio loads similarly on the first principal component with the exception of hedge funds that load primarily on the second 28 This, however, requires more study. 29 The identification of the drivers for the third and higher components is left for further research. 15
Page 1 and 2: Insurance and Interconnectedness in
Page 3 and 4: Insurance and Interconnectedness in
Page 5 and 6: elationships with banks and expansi
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: are considered significant (Tabachn
Page 19 and 20: directionality is reversed in the c
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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