Insurance and Interconnectedness in the Financial Services Industry

Insurance and Interconnectedness in the 

Contact Author: 

Faith Roberts Neale, 

UNC Charlotte 

Charlotte, NC 28223 

704.687.7636 

frneale@uncc.edu 

704.687.6987 fax 

Financial Services Industry 

by 

Faith Roberts Neale 

Associate Professor of Risk Management and Insurance 

University of North Carolina at Charlotte 

Pamela Peterson Drake 

J. Gray Ferguson Professor of Finance and Department Head 

James Madison University 

Patrick Schorno 

Doctoral Student 

University of North Carolina at Charlotte 

Pamela Peterson Drake 


Harrisonburg, VA 22807 

540.568.6530 

drakepp@jmu.edu 

Elias Semaan 

Assistant Professor 


August 2012 

Authors’ contact information 

Patrick Schorno 

UNC Charlotte 

845.527.8977 

pschorno@uncc.edu 

Elias Semaan 


Harrisonburg, VA 22807 

semaanej@jmu.edu



ABSTRACT 

In the post-‐mortems of the recent financial crisis, interconnectedness between financial 

institutions has become one primary focus of analysis. We analyze the degree of 

interconnectedness between and among financial industries using correlation and 

principal components analysis and find that interconnectedness among financial firms 

has increased over time and varies by type of financial institution. We also find that the 

interconnectedness can be explained by not only a market factor, but also by the 

correlations among financial institutions’ returns. We find the causality attributed to 

the insurance industry by other studies is likely driven by financial guarantee and life 

insurers.



INTRODUCTION 

The recent subprime crisis that ushered in the recession of 2008 and 2009 was the result of many 

different forces that came together in a perfect storm. A contributor to this crisis was a U.S. housing 

market that grew at unsustainable rates, which swept in borrowers, mortgage originators, dealers, 

government sponsored entities, insurers, and investors. 1,2 Regardless of the causes and the degree of 

contribution by diverse participants, the outcome is clear: the rapid deterioration of the subprime 

mortgage market led to a severe financial crisis in the U.S. 

The buzz-‐word of this financial crisis is systemic risk. Systemic risk is “the risk that an event will 

trigger a loss of economic value or confidence in a substantial segment of the financial system that is 

serious enough to have significant adverse effects on the real economy with a high probability.” 3 The 

financial sector is exposed to systemic risk through: 

• correlation of assets across financial firms leading to a “domino” or contagion effect, and 

• exposure to a common shock that simultaneously and significantly affects a large number of 

financial firms. 4 

All studies of systemic risk in the insurance industry include interconnectedness of the different types of 

financial firms as one of the primary factors of systemic risk. 5 , Billio, Lo, Getmansky and Pelizzon (2010) 

use correlation, principal components, regime switching, and Granger causality to measure 

interconnectedness in the financial services industry. 6 They find that hedge funds, banks, insurers and 

1 

The Subprime Mortgage Market, 2007 Annual Report of the Federal Reserve Bank of San Francisco. 

2 

Sherlund (2010). 

3 

Cummins and Weiss (2011) note this definition of risk is very similar to that in the Group of Ten (2001, p. 126) 

report as well as others such as the Financial Stability Board (2009), Schwarz (2008), Thomson (2009), and 

4 

Helwege (2009). 

5 

In the Geneva Association’s 2011 report the Financial Stability Board (FSB) and International Association of 

Insurance Supervisors (IAIS) criteria focus first on identifying systemic activities and then firms with systemic 

activities are examined. Their criteria include size, interconnectedness, substitutability and timing. 

1

okers have become interrelated over recent years. Further, they find that banks and insurers are 

more important to the interconnectedness of the financial industry than brokers and hedge funds, and 

have an asymmetric effect on the monthly returns of brokers and hedge funds. They argue that by 

insuring financial products, writing credit default swaps, and engaging in derivatives trading and 

investment management, insurers began to compete directly with hedge funds, banks, and 

broker/dealers. Hence, insurers became a part of this interconnected system. Chen, Cummins, 

Viswanathan and Weiss (2012) examine the interconnectedness using linear and nonlinear Granger 

causality tests, incorporating the spread on credit default swaps in their examination of eleven 

insurance companies and twelve banks. They conclude that the dominating influence is that of banks 

7, 8 

affecting insurance companies. 

Cummins and Weiss (2011) argue that the core activities of insurers are not systemically risky, 

though their study excludes traditional monoline insurers such as financial guarantee insurers. Cummins 

and Weiss examine life and health insurers, property and casualty insurers, and commercial banks. They 

note that some sources of risk, especially in the life insurance industry, can spill over into other financial 

sectors. These risks include defaults by reinsurers for those insurers that have large exposures to only a 

few reinsurance counterparties, high leverage, and investments in mortgage-‐backed securities. 

Baluch, Mutenga and Parsons (2011) examine global insurance sector returns and find insurers 

have lower systemic risk than banks but the risk has increased in recent years due, in part, to increasing 

6 

Another approach, which focuses on whether a financial institution is undercapitalized with a marginal expected 

shortfall measure, is used by Acharya, Pedersen, Philippon and Richardson (2010). Huang, Zhou and Zhu (2010) 

used both the expected shortfall measure and the result of the U.S. Supervisory Capital Assessment Program. 

Adrian and Brunnermeier (2011) propose a measure similar to covariance, CoVaR, which they state is a 

“countercyclical, forward looking” measure of systemic risk that incorporates size, leverage and maturity 

mismatch. 

7 

An alternative approach used by Cummins and Weiss (2011) focuses on systemic risk indicators. Cummins and 

Weiss follow the Financial Stability Board (2009) criteria and analyze systemic risk using three primary and four 

contributing factors. The primary factors are size; interconnectedness, and substitutability. Further contributing 

factors include leverage, liquidity, complexity, and government policy and regulation. The IAIS proposes 18 

indicators with interconnectedness a significant factor with a weight of 30-‐40%, Baranoff (August 2012). 

8 

Notably, it has been reported that liquidity and leverage played a significant role in the crisis beginning in 2007. 

As reported by the Republican Commissioners on the Financial Crisis Inquiry Commission in the Financial Crisis 

Primer: Questions and Answers on the Causes of the Financial Crisis, December 15, 2010. A 

2 

9 Baluch, et al. (2011) 

find insurers in the United Kingdom, Asia-‐Pacific and United States property-‐casualty insurers were the least 

affected by the crisis. Life insurers, global composite insurers, global reinsurers and other European insurers were 

the most affected and had the worst performance during the crisis. They also find that certain lines of insurance 

were impacted more by the crisis including financial guarantee, credit and liability insurers as well as those who 

deviated from their core insurance business such as AIG and Swiss Re. 

Faith Neale 8/26/12 8:53 PM 

Deleted:

elationships with banks and expansion into non-‐traditional insurance products. They conclude insurer 

segments are affected differently by the crisis with some segments having little to no impact while 

others are highly affected by the crisis. 9 

Given the recent financial and current economic crisis and resulting push for regulatory reform, 

it is important to reconcile these apparently conflicting results to insure that reform efforts are targeted 

to the appropriate firms and industries. This study provides additional insight into the 

interconnectedness of specified insurance segments on the financial services industry. 

On April 3, 2012, the Financial Stability Oversight Council (FSOC) issued its final rule on supervision 

criteria for nonbank financial companies, which includes insurance companies. These rules set forth 

criteria that will be considered when identifying companies in need of FSOC supervision. We provide in 

Panel A of Figure 1 what is termed the “Analytic Framework for Determination,” which consists of six 

factors; three that relate to the company’s ability to affect the economy as a whole, and three that 

relate to the company’s likelihood of financial distress. To manage this determination, the FSOC laid out 

a three-‐stage process, which we illustrate in Panel B of Figure 1. In this process, the large number of 

nonbank companies is filtered on size and at least one quantitative factor, producing a smaller number 

of nonbank firms. 10 Then, using publicly-‐available and regulator available data, this set of nonbank firms 

is narrowed further on the basis of quantitative factors and its risk profile. The third stage then 

produces a much smaller number of nonbank entities, for which the FSOC will scrutinize further. 

**************** 

Insert Figure 1 Here 

**************** 

Interconnectedness, per the FSOC, includes the amount of derivatives transactions and the 

number of counterparties, the notional amount of credit default swaps outstanding that involve 

obligations of the nonbank entity or its parent company, the proportion of counterparty’s capital that 

the nonbank entity is counterparty to and the extent a nonbank entity’s assets are financed by a small 

number of financial entities. Therefore, interconnectedness is defined with respect to specific types of 

transactions, which may not capture the dynamic nature of financial innovations. 

9 

Baluch, et al. (2011) find insurers in the United Kingdom, Asia-‐Pacific and United States property-‐casualty insurers 

were the least affected by the crisis. Life insurers, global composite insurers, global reinsurers and other European 

insurers were the most affected and had the worst performance during the crisis. They also find that certain lines 

of insurance were impacted more by the crisis including financial guarantee, credit and liability insurers as well as 

those who deviated from their core insurance business such as AIG and Swiss Re. 

10 

The size criteria is $50 billion in assets, which would capture half of the publicly-‐traded life insurance companies 

in 2011, but few accident and health and property and casualty firms. This size criteria would have only identified 

one or two of the financial guarantee insurers, depending on the year. 

3

The purpose of this paper is to examine the insurance industry’s contribution to a primary 

component of systemic risk inherent in the financial crisis of 2007-‐8. 11 We posit that insurers with 

different lines of business have diverse business models, with differing underwriting and investment 

portfolios. We also posit that not all types of insurance companies are interconnected with other 

financial services providers. We hypothesize that insurers specializing in financial guarantee, as well as 

life insurers, have some degree of interconnectedness, and are more interconnected when compared to 

fire, marine, and casualty insurers and accident and health insurers. Our primary focus is on the 

interconnectedness of the insurance industry in the financial system, and to determine if 

interconnectedness is consistent among the different types of insurance companies. Our analysis has 

implications for the identification of systemically important financial firms. 

INSURANCE OPERATIONS BY INDUSTRY 

The bulk of insurance operations fall in three main industries and one sub-‐group: property-‐casualty, life, 

accident-‐health, and financial guarantee insurance. Financial guarantee insurers are generally classified 

within the property-‐casualty industry and were some of the first financial firms to be significantly 

affected by the unfolding crisis. 12 

We illustrate the proportion of the insurance industry assets by line of business in 2011 in Figure 

2. The life and accident-‐health firms have by far the largest share, with over $5.4 trillion in assets. 13 The 

property-‐casualty industry without financial guarantee included is the next largest with almost $1.6 

trillion in assets. The financial guarantee insurers, which we have broken out from the property and 

casualty insurers, is the smallest with a total asset value of $37 billion. Each of these firm types is 

unique in both underwriting and investment operations. These distinct operations lead to different risk 

profiles with diverse effects, and may also lead to differing degrees of interconnectedness with non-‐ 

insurers in the financial services industry. 

11 

Helwege (2009) provides a summary of the financial crisis and its causes but leaves the degree of contribution of 

systemic risk by these participants to future research. 

12 

On January 18, 2008, AMBAC became the first FGI downgraded from AAA as a result of the subprime mortgage 

market decline. The Federal Reserve Bank of New York agreed to lend AIG, a property-‐casualty insurer, $85 billion 

on September 16, 2008. November 17, 2008, three life insurance companies requested TARP funding. These 

insurers are Genworth Financial, Hartford Financial Services Group and Lincoln National. 

13 

The proportion of industry assets by type has been steady over time, with almost identical shares in 2001 as in 

2011. 

4

Life insurers 

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**************** 

Life and accident-‐health insurers provide financial protection from the effects of unexpected mortality 

and morbidity losses. Typical lines of insurance include life insurance, annuities and accident & health 

for individuals and groups. Life insurers also produce deposit-‐type contracts including annuities certain 

and guaranteed investment contracts (GICs). 14 As a result, a significant portion of life insurer operations 

involve long periods of premium accumulation and/or claim payout and large amounts of invested 

assets of varying duration. It is not uncommon for premiums to build for 20 or more years until a 

mortality event occurs, and payouts then may be made over a long period of time. Hence, life insurance 

involves a long-‐tail liability. 

Due to the size and duration of invested assets required in long tail lines, life insurers are more 

exposed to fluctuations in the financial markets. Further, their product is sensitive to the economy; life 

insurers experience a drop in insurance during recessions. Other than the effects on its investments and 

the indirect effect on sales of policies, insurance firms are not directly linked to the subprime crisis and, 

hence should have little interconnectedness with other financial service firms. 15 , 16 

More specifically related to the financial crisis are life insurers use of insurance-‐linked securities 

(ILS) that were most often “wrapped” by financial guarantor firms. 17 When the financial guarantee firms 

were downgraded, this ushered in problems associated with the structured finance securities that 

backed the ILS, and the inability to continue to issue additional ILS during the credit crunch in 2008. 

According to Swiss Re, there was over $6 billion life insurance ILS issued in 2007, compared to the $100 

million in 2008. 18 Baluch, Mutenga and Parsons (2011) find life insurers experienced a greater loss of 

14 

Deposit-‐type services offered by life insurers also include dividend and coupon accumulations, lottery payouts, 

structured settlements, and premium funds. Cummins and Venard (2007). 

15 

As pointed out by Fitch Ratings, most life insurers have low exposure to the subprime crisis, especially because 

their investments were limited to the better-‐quality tranches in securitizations [“No Subprime Crisis for U.S. Life 

Insurers, Fitch Says,” Seeking Alpha, August 20, 2007]. 

16 

In contrast, health insurance is renewed annually and a policy must be in effect at the time of treatment for 

claims to be paid. Generally, claims are filed very close to the time services are rendered leading to a relatively 

short claims payout period from the time premiums are received. As a result, insurers with primary operations in 

accident-‐health lines must be analyzed separately from insurers operating primary in life insurance. 

17 

These ILS are referred to as “XXX transactions” because of the reference to the NAIC model regulations. 

18 

The issuance has risen slowly, with over 400 million life insurance ILS issued in 2011. 

5

valuation of assets during the financial crisis than non-‐life insurers and the decrease in value was similar 

to that experienced by banks. 

Property-‐casualty insurers 

In general, property and casualty insurance is protection against the loss or damage to property from 

perils, liability from injuries or damage to property, business interruption losses, and losses due to 

accident or illness. The companies in the property-‐casualty industry provide a diverse set of insurance 

products. 

The property side of the business consists primarily of first-‐party property coverage such as 

insurance for fire, ocean and inland marine, auto physical damage, farm and homeowners, commercial, 

crop, and flood insurance. It is very similar to accident-‐health in that the policies are renewed annually, 

the policy must be in effect at the time of the loss, and there is a relatively short payout time between 

receipt of premium and payment of claims. Generally, damage to property is reported quickly and is 

relatively easy to adjust. 

Liability insurance provides coverage to the insured for their negligence causing injury and/or 

damage to third parties. Liability claims can take much longer to resolve particularly if liability is 

questionable and damages are large or complex. There may be a long period of time between payment 

of premium and payment of claim. For example, medical malpractice claims take, on average, between 

4 to 6 years to resolve. 

There are at least three potential paths for interconnectedness to other financial service firms. 

First, as with other insurers, the insurer may be affected by the revaluation of the investment portfolio 

as market values fell, especially those of mortgage-‐backed securities. Second, the potential claims for 

errors and omissions (E&O) insurance, as well as directors’ and officers’ insurance, increased with the 

exposure of possible claims related to disclosures and valuation of assets. Third, the recessionary 

environment that followed the crisis may be accompanied by more claims of losses, as well as a drop in 

insurance premiums due to devaluations of insured homes. Unlike life insurers, however, property and 

casualty insurers did not shift any of it risks through securitization. 

Many have argued that property and casualty insurers are not systemically important. Though 

there is some degree of interconnectedness, this type of insurance company is more reactive to the 

general economy, rather than proactive. 

6

Financial guarantee insurers 

Financial guarantee insurers (FGIs), also known as monoline insurers, provide credit enhancement for a 

variety of financial products. These credit enhancements are in the form of insurance on public finance, 

guaranteed investment contracts, structured finance, and credit default swaps. 19 FGIs operate over a 

long horizon; in their municipal financial guarantee insurance operations they may receive a one-‐time 

premium payment to insure a bond with duration of 20, 30, or even 40 years. Financial guarantee 

insurers may also manage the proceeds from the bond sale for the municipality, providing a guarantee 

of a set amount of interest (guaranteed investment contract). The majority of financial guarantee 

insurers also insured structured finance, primarily the higher rated tranches of collateralized debt 

obligations (CDOs), and some financial guarantee insurers issued credit default swaps (CDS). 20 

DATA 

The purpose of our analysis is to determine whether insurance companies, in general, are significantly 

interconnected with other financial industries or, in the alternative, interconnectedness is specific to 

certain lines or firms within the insurance sector. In the first part of the analysis, we use principal 

components to examine whether there are common factors that explain returns to financial firms. The 

second step we take is to use Granger causality to examine the potential for autocorrelation among 

monthly returns. Specifically, we focus on the relationship between returns to insurance companies and 

those of other financial institutions. 

We draw returns from the Center for Research in Security Prices (CRSP) Monthly Stock File for 

the entire financial sector. We classify the sample based on SIC codes into banks, brokers, insurers, and 

financial holding companies, hereafter referred to as FHCs. 21 Within the insurance industry, we divide 

19 After the financial crisis, these insurers are not permitted to participate in credit default swaps. 

20 The value of a FGI is derived from its AAA rating. The business model of a FGI leverages this rating by “lending” 

this rating to the structurer or owner of an insured financial product in exchange for a protection payment or 

premium. In essence, the FGI promised to make payments in the event of default of the underlying assets of the 

protected financial product. In an effort to preserve the AAA rating needed for their business model, FGIs 

employed no-‐loss or remote loss underwriting guidelines. For a comprehensive analysis of FGIs see Drake and 

Neale (2011). 

21 We use the following breakdown: into banking (6000-‐6199), brokerage (6200-‐6299), insurance (6300-‐6399), and 

financial holding companies (6710-‐6712). 

7

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

are input variables. However, there are stopping rules based on the percentage of total variation 

explained by components. 

We decompose the covariance matrix of monthly portfolio returns using principal components 

analysis. For example, if the first K principal components explain most of the variability in returns, the 

model is:

! 

!

We provide the percentage explained and eigenvectors in Table 1, and provide the proportions 

of returns explained by the principal components in Figure 4. As you can see, the first principal 

component explains approximately 70% of the variability among financial institutions from 1994 to 

2010. More specifically, the first principal component (PCA1) is dynamic, capturing from 60% to 80% of 

return variation in the different periods. These results are consistent with those observed by Merville 

and Xu (2002). In Figure 4 you can see that the explanatory power of PCA1 peaked around the Long-‐ 

Term Capital Management crisis in August 1998, and subsequently decreased until it began rising again 

from late-‐2002 through mid-‐2005. Finally, we can see in Figure 4 that throughout the financial crisis of 

2007-‐2009, the first and second principal components explains more than 80% of the return variation 

and is persistent over time for each of the portfolios. Our results are consistent with those of Billio, 

Getmansky, Lo and Pelizzon, who observe that the first component’s explanatory power is higher during 

periods of market stress. 

These results are also consistent with those of Kritzman, Li, Page and Rigobon (2010), who refer 

to the variation explained by a specific number of factors as the absorption ratio. The absorption ratio, 

they contend, is a measure of the fragility of a system, and hence systemic risk. Related to this is the 

observation by Billio, Getmansky, Lo and Pelizzon that correlations among returns of securities increase 

in periods of market stress. Our observation that two principal components explain stock returns is 

consistent with other studies, including Lo and Wang (2010): 

Absorption 

Absorption 

ratio: 

Time frame 

ratio: PCA1 PCA1 and PCA2 

Entire period 70.84% 86.63% 

1994 through 1999 75.88% 89.35% 

2000 through 2004 59.88% 86.26% 

2005 through 2008 73.09% 87.46% 

2009 through 2010 80.49% 90.95% 

We provide the factor loadings of the components in Panel B of Table 1. The factor loadings 

indicate the importance of the principal component in explaining the portfolio’s returns; the higher the 

loading, the more important the component. It is commonly accepted that loadings of 0.32 and above 

12

are considered significant (Tabachnick and Fidell, 1996.) 26 Using this criterion, we find the factor 

loadings on the overall sample in Panel B of Table 1 indicate that each financial service portfolio loads 

similarly on the first principal component with the exception for hedge funds, which loads less on the 

first component, yet more heavily on the second component. Brokers load heavily on the third 

component in all periods while FHCs load on the fourth component in all but one of the time periods. 

Insurers are notably sensitive to both the third and fourth principal components. In other words, 

insurers are affected by the same factors that affect other financial service firms, but there are other 

factors that affect insurers that do not affect brokers, banks, FHCs, and hedge funds. 

Interpreting the components 

The observation that there are factors that affect the returns on financial service firms is interesting, but 

not helpful in understanding the drivers of returns. Our understanding of asset pricing leads us to 

believe that the first and most important factor in explaining returns is the return on the market. This is 

consistent with what we observe in terms of the proportion of variation explained and the factor 

loadings. Correlating the return on the S&P500 index with the principal components provides 

supporting evidence: 

Returns on the S&P 500 

p-‐value for test 

of correlation 

Correlation Ho: ρ= 0 

PCA1 0.8812 0.0000 

PCA2 0.0867 0.2174 

PCA3 -‐0.1321 0.0597 

PCA4 0.0901 0.1999 

A stronger correlation, and consistent with evidence by Semaan and Drake (2012), is the correlation 

between PCA1 and the mean of the pair-‐wise correlation of the portfolio returns, 0.9599, which is 

different from zero at the one percent level for PCA1. 

We calculate the mean of the pair-‐wise correlations by first calculating 24-‐month rolling 

correlations of portfolio returns for each pair of portfolios (e.g., brokers and banks); hence, we are able 

to calculate correlation means from December 1996 through December 2010. The mean of the 

correlations is similar to the overall mean model for asset pricing discussed by Elton and Gruber (1973) 

26 

In section 13.6.5, Interpretation of Factors. Loadings greater than 0.71 are equivalent to 50% of the variance 

overlapping; 0.63 equals 40% overlap; 0.55 equals 30%; 0.45 equals 20% and 0.32 is equivalent to 10% of the 

variance overlapping (Comrey and Lee, 1992.) 

13

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. 

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**************** 

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. 

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**************** 

**************** 

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

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

As with other studies, we observe that most financial service companies are affected by a 

market or industry factor, which is generally the largest principal component when analyzing returns to 

financial service firms. As with other studies, we also find that there is a second factor, and we propose 

an explanation for this factor; we posit that this factor represents the diversification potential of the 

financial service industry, as proxied by the variance of the correlations among the financial service 

portfolios. We also find that insurance companies are related to third and fourth principal components, 

which is consistent with other studies. 

We also examine the possibility of feedback, either unidirectional or bidirectional, among the 

types of financial services companies. We use tests of Granger causality to examine the relation among 

the current, lagged, and lead monthly returns. We find that the causality attributed to the insurance 

industry by other studies is most likely driven primarily by financial guarantee and life insurers. 

18

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20

TABLE 1 

Principal Components Analysis of Monthly Return Portfolios for Brokers, Banks, Financial Holding 

Companies, Hedge Funds, Insurers 

PANEL A EIGENVALUES 

PANEL B EIGENVECTORS 

Percentage Variation Explained by Principal Component 

Sample Period PCA1 PCA2 PCA3 PCA4 

1994 through 2010 70.84% 15.80% 5.93% 5.01% 

1994 through 1999 75.88% 13.47% 6.10% 3.93% 

2000 through 2004 59.88% 26.38% 5.63% 5.39% 

2005 through 2008 73.09% 14.37% 7.06% 3.85% 

2009 through 2010 80.49% 10.46% 5.93% 2.14% 

Eigenvectors corresponding to principal 

components 

Sample Period Portfolio PCA1 PCA2 PCA3 PCA4 

1994 through 2010 Brokers 0.4660 0.1491 -‐0.7919 0.2960 

Banks 0.4919 -‐0.2688 -‐0.0310 -‐0.1304 

FHCs 0.4806 -‐0.1708 0.1045 -‐0.7214 

Hedge Funds 0.3111 0.8911 0.3090 -‐0.0303 

Insurers 0.4616 -‐0.2868 0.5153 0.6116 

1994 through 1999 Brokers 0.4526 -‐0.0248 -‐0.7840 0.4223 

Banks 0.4846 -‐0.2412 0.0303 -‐0.5361 

FHCs 0.4977 -‐0.1190 0.0349 -‐0.4069 

Hedge Funds 0.3303 0.9262 0.1611 -‐0.0082 

Insurers 0.4511 -‐0.2630 0.5977 0.6071 

2000 through 2004 Brokers 0.4290 0.4654 -‐0.6932 -‐0.2987 

Banks 0.5469 -‐0.0848 0.0752 0.0008 

FHCs 0.5141 -‐0.1658 0.5291 -‐0.5154 

Hedge Funds 0.0781 0.8232 0.4719 0.3056 

Insurers 0.4965 -‐0.2665 -‐0.1059 0.7428 


Banks 0.4530 -‐0.4783 -‐0.2071 0.5021 

FHCs 0.4364 -‐0.3106 0.8032 -‐0.1164 

Hedge Funds 0.3635 0.8160 0.2383 0.2463 

Insurers 0.4807 0.0226 -‐0.2954 -‐0.7881 

2009 through 2010 Brokers 0.4673 -‐0.1143 -‐0.4478 -‐0.6832 

Banks 0.4788 -‐0.2515 0.1880 -‐0.1309 

FHCs 0.4624 -‐0.1101 -‐0.5173 0.7079 

Hedge Funds 0.3708 0.9190 0.1288 -‐0.0014 

Insurers 0.4485 -‐0.2588 0.6927 0.1228 

Note: PCA indicates a principal component, indicates as PCA1, PCA2, PCA3 and PCA4, in order of importance in 

explaining the variation of monthly portfolio returns. 

21

TABLE 2 

Principal components analysis of monthly portfolio returns portfolio for brokers, banks, FHCs, 

hedge funds, financial guarantee insurers, life insurance, accident and health insurers, property 

and casualty insurers, and other insurers 

PANEL A EIGENVALUES 

PANEL B EIGENVECTORS 

Percentage Variation Explained by Principal Component 

Sample Period PCA1 PCA2 PCA3 PCA4 

1994 through 2010 63.48% 11.28% 8.46% 5.39% 

1994 through 1999 69.48% 9.69% 6.32% 5.04% 

2000 through 2004 62.32% 16.52% 6.51% 4.55% 

2005 through 2008 66.17% 9.29% 7.9% 5.13% 

2009 through 2010 76.11% 8.53% 5.78% 3.90% 


components 



Banks 0.3791 -‐0.0499 -‐0.2940 -‐0.1159 

Financial holding companies 0.3630 0.0487 -‐0.3570 -‐0.1245 

Hedge funds 0.2053 0.8249 0.3288 0.1641 

Financial guarantee insurers 0.3356 -‐0.1888 -‐0.3517 0.3731 

Life insurers 0.3660 -‐0.0413 0.1072 -‐0.1051 

Accident & health insurers 0.3282 -‐0.1963 0.5777 -‐0.3111 

Property & casualty insurers 0.3256 -‐0.3480 0.3120 -‐0.1413 

Other insurers 0.3365 -‐0.0678 0.1680 0.7264 


Banks 0.3689 -‐0.0317 -‐0.1922 -‐0.3572 


Hedge funds 0.2345 0.7885 -‐0.0555 0.4356 

Financial guarantee insurers 0.3478 -‐0.2593 0.2619 -‐0.0353 

Life insurers 0.3704 -‐0.0282 -‐0.0335 0.0589 

Accident & health insurers 0.3162 -‐0.2844 -‐0.1180 0.6807 

Property & casualty insurers 0.3366 -‐0.3698 0.1604 0.1623 

Other insurers 0.2925 0.2672 0.7634 -‐0.2167 


Banks 0.3847 0.0974 -‐0.2641 0.0726 

Financial holding companies 0.3648 0.0046 -‐0.2667 0.5293 

Hedge funds 0.0066 0.7445 0.4720 0.0013 

Financial guarantee insurers 0.3741 -‐0.1356 -‐0.0155 -‐0.4780 

Life insurers 0.3954 0.0202 0.0604 0.0095 

Accident & health insurers 0.3276 -‐0.2208 0.4883 0.5430 

Property & casualty insurers 0.3664 -‐0.1950 -‐0.1018 -‐0.2865 

Other insurers 0.3522 0.0154 0.4467 -‐0.3300 

22

Table 2, Panel B, continued 


components 


2005 through 2008 Brokers 0.3753 0.1234 0.0471 -‐0.3208 

Banks 0.3471 -‐0.3834 -‐0.0797 -‐0.4877 

Financial holding companies 0.3195 -‐0.3600 0.4450 -‐0.0757 

Hedge funds 0.2606 0.6086 0.5709 -‐0.1420 

Financial guarantee insurers 0.2964 -‐0.4555 0.2775 0.5207 

Life insurers 0.3743 0.1993 -‐0.1246 0.0071 

Accident & health insurers 0.3282 0.2916 -‐0.1619 0.5595 

Property & casualty insurers 0.3277 0.0642 -‐0.5360 -‐0.1473 

Other insurers 0.3544 -‐0.0372 -‐0.2478 0.1654 


Banks 0.3713 -‐0.0815 -‐0.0504 0.0079 


Hedge funds 0.2583 0.7077 0.3107 0.5382 

Financial guarantee insurers 0.3410 -‐0.1404 -‐0.3504 -‐0.0670 

Life insurers 0.3429 0.1694 0.2540 -‐0.5558 

Accident & health insurers 0.3416 -‐0.0166 0.4694 -‐0.3098 

Property & casualty insurers 0.3029 -‐0.5351 0.4069 0.2602 

Other insurers 0.3348 -‐0.3181 -‐0.1112 0.3997 

Note: PCA indicates a principal component, indicates as PCA1, PCA2, PCA3 and PCA4, in order of importance in 

explaining the variation of monthly portfolio returns. 

23

TABLE 3 

Granger causality tests for all insurance companies and by type of insurance company, with 

tests of lead and lag relationships between raw returns on non-insurance companies and those 

of insurance companies 

Table values are p-‐values, with statistically significant Wald tests p-‐values indicated in bold and 

italics based on one-‐period lead or lag relationships. 

PANEL A ALL INSURANCE COMPANIES 

Insurance Companies Insurance Company as 

Period 

as Leader 

Laggard 

1994 through 2010 0.5937 0.1239 

1994 through 1999 0.6965 0.5485 

2000 through 2004 0.0010 0.0073 

2004 through 2008 0.0122 0.0114 

2008 through 2010 0.0784 0.0010 

PANEL B FINANCIAL GUARANTEE INSURERS 

PANEL C LIFE INSURERS 

Financial Guarantee Financial Guarantee 

Period 

Insurers as Leader Insurers as Laggard 

1994 through 2010 0.0002 0.0001 

1994 through 1999 0.3503 0.2648 

2000 through 2004 0.0184 0.0289 

2005 through 2008 0.0000 0.0000 

2009 through 2010 0.0566 0.0000 

Period Life Insurers as Leader Life Insurers as Laggard 

1994 through 2010 0.0017 0.0523 

1994 through 1999 0.6097 0.4021 

2000 through 2004 0.0871 0.2742 

2005 through 2008 0.0000 0.4358 

2009 through 2010 0.0107 0.6857 

PANEL D ACCIDENT/HEALTH INSURERS 

Accident/Health Accident/Health 

Period 


1994 through 2010 0.5899 0.1506 

1994 through 1999 0.8729 0.9455 

2000 through 2004 0.2783 0.1700 

2005 through 2008 0.0341 0.1051 

2009 through 2010 0.1125 0.0000 

24

TABLE 3, CONTINUED 

PANEL E PROPERTY AND CASUALTY 

Property and Casualty Property and Casualty 

Period 


1994 through 2010 0.4007 0.3733 

1994 through 1999 0.8675 0.5811 

2000 through 2004 0.0003 0.0061 

2005 through 2008 0.0019 0.6959 

2009 through 2010 0.0371 0.0000 

PANEL F OTHER INSURERS 

Other Insurers as Other Insurers as 

Period 

Leader 

Laggard 

1994 through 2010 0.2506 0.0071 

1994 through 1999 0.3242 0.1407 

2000 through 2004 0.3798 0.1768 

2005 through 2008 0.1049 0.4579 

2009 through 2010 0.6159 0.0000 

25

TABLE 4 

Granger causality tests for all insurance companies and by type of insurance company, with 

tests of lead and lag relationships between market adjusted returns on non-insurance 

companies and those of insurance companies 

Table values are p-‐values, with statistically significant Wald tests p-‐values indicated in bold and 

italics based on one-‐period lead or lag relationships. 

PANEL A ALL INSURANCE COMPANIES 

Insurance Companies as Insurance Company as 

Period 

Leader 

Laggard 

1994 through 2010 0.6673 0.0415 

1994 through 1999 0.2244 0.7991 

2000 through 2004 0.2944 0.0089 

2005 through 2008 0.0878 0.0012 

2009 through 2010 0.0314 0.0005 

PANEL B FINANCIAL GUARANTEE INSURERS 

PANEL C LIFE INSURERS 

Financial Guarantee Financial Guarantee 

Period 


1994 through 2010 0.0002 0.0005 

1994 through 1999 0.1451 0.2521 

2000 through 2004 0.4505 0.0030 

2005 through 2008 0.0000 0.0000 

2009 through 2010 0.0000 0.0288 

Period Life Insurers as Leader Life Insurers as Laggard 

1994 through 2010 0.0007 0.0785 

1994 through 1999 0.0151 0.1291 

2000 through 2004 0.3780 0.2166 

2005 through 2008 0.0000 0.5206 

2009 through 2010 0.0000 0.7235 

PANEL D ACCIDENT/HEALTH INSURERS 

Accident/Health Accident/Health Insurers 

Period 

Insurers as Leader 

as Laggard 

1994 through 2010 0.3766 0.1138 

1994 through 1999 0.5449 0.6462 

2000 through 2004 0.2592 0.1142 

2005 through 2008 0.3194 0.1005 

2009 through 2010 0.0128 0.0000 

26

TABLE 4, CONTINUED 

PANEL E PROPERTY AND CASUALTY 

Property and Casualty Property and Casualty 

Period 


1994 through 2010 0.3164 0.3818 

1994 through 1999 0.5487 0.8377 

2000 through 2004 0.0393 0.0131 

2005 through 2008 0.2904 0.3386 

2009 through 2010 0.0321 0.0867 

PANEL F OTHER INSURERS 

Period Other Insurers as Leader Other Insurers as Laggard 

1994 through 2010 0.7081 0.0156 

1994 through 1999 0.5703 0.0507 

2000 through 2004 0.6125 0.2311 

2005 through 2008 0.1219 0.5669 

2009 through 2010 0.7176 0.0457 

27

FIGURE 1 

Financial Stability Oversight Council Framework 

PANEL A ANALYTIC FRAMEWORK FOR DETERMINATION 

Influence on the 

broader economy 

Vulnerability to 

financial distress 

• Size 

• Interconnectedness 

• Subsytutability 

PANEL B THREE-‐STAGE PROCESS FOR IDENTIFYING NON-‐BANK COMPANIES 

Stage 1 

• Quanytayve 

thresholds 

• Size and one of: 

• Interconnectedness 

• Leverage 

• Liquidity risk 

• Maturity mismatch 

• Leverage 

• Liquidity risk 

• Maturity mismatch 

Stage 2 

• Risk profile 

• Analyyc framework 

determinayon factors 

Stage 3 

• Noyfy companies 

• Collect addiyonal 

informayon 

• Resolvability 

• Opaqueness 

• Complexity 

• Regulatory scruyny 

28

FIGURE 2 

Proportion of insurance assets by type of insurer, 2011 

22.2% 

Source: SNL Financial, Inc. 

0.5% 

2.5% 

74.8% 

Health insurers 

Life insurers 

Property & casualty insurers 

Financial guarantee insurers 

29

FIGURE 3 

Compounded monthly returns for financial services portfolios 

PANEL A COMPOUNDED RETURNS BASED ON TYPE OF FINANCIAL SERVICE 

Value 

of $1 

$14 

$12 

$10 

$8 

$6 

$4 

$2 

$0 

Jan-‐94 

Jan-‐95 

Jan-‐96 

Jan-‐97 

PANEL B COMPOUNDED RETURNS BASED ON TYPE OF INSURANCE 

Value 

of $1 

$8 

$7 

$6 

$5 

$4 

$3 

$2 

$1 

$0 

Brokerage Banking 

Insurance Real estate 

Hedge funds S&P 500 

Jan-‐98 

Jan-‐99 

Jan-‐00 

Jan-‐01 

Jan-‐02 

Jan-‐03 

Month 

Bond insurance Life insurance 

Accident and health insurance Property and casualty insurance 

Others AIG 

Jan-‐94 

Sep-‐94 

May-‐95 

Jan-‐96 

Sep-‐96 

May-‐97 

Jan-‐98 

Sep-‐98 

May-‐99 

Jan-‐00 

Sep-‐00 

May-‐01 

Jan-‐02 

Sep-‐02 

May-‐03 

Jan-‐04 

Sep-‐04 

May-‐05 

Jan-‐06 

Sep-‐06 

May-‐07 

Jan-‐08 

Sep-‐08 

May-‐09 

Jan-‐10 

Sep-‐10 

Month 

Jan-‐04 

Jan-‐05 

Jan-‐06 

Jan-‐07 

Jan-‐08 

Jan-‐09 

Jan-‐10 

30

FIGURE 4 

Principal components analysis of the monthly return indexes for the overall sample of brokers, 

banks, insurers, FHCs, and hedge funds from December 1996 to December 2010 

Illustrated are the 36-‐month rolling-‐window eigenvalues for principal components PCA1 

through PCA5. 

VariaOon explained 

100% 

90% 

80% 

70% 

60% 

50% 

40% 

30% 

20% 

10% 

0% 

Dec-‐96 

Aug-‐97 

Apr-‐98 

Dec-‐98 

PCA1 PCA2 PCA3 PCA4 PCA5 

Aug-‐99 

Apr-‐00 

Dec-‐00 

Aug-‐01 

Apr-‐02 

Dec-‐02 

Aug-‐03 

Apr-‐04 

Month 

Dec-‐04 

Aug-‐05 

Apr-‐06 

Dec-‐06 

Aug-‐07 

Apr-‐08 

Dec-‐08 

Aug-‐09 

Apr-‐10 

Dec-‐10 

31

FIGURE 5 

The first two components and their relationship with pairwise correlations among portfolios 

Panel A PCA1 and the mean of portfolio correlations 

Component or correlaOon 

1.0 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0.0 

Dec-‐96 

Jul-‐97 

Feb-‐98 

Sep-‐98 

Apr-‐99 

Nov-‐99 

Jun-‐00 

Jan-‐01 

Aug-‐01 

Mar-‐02 

Oct-‐02 

May-‐03 

Panel B PCA2 and the variance of correlations 

Component or correlaOon 

0.4 

0.3 

0.3 

0.2 

0.2 

0.1 

0.1 

0.0 

Dec-‐96 

Jul-‐97 

Feb-‐98 

Sep-‐98 

Apr-‐99 

Nov-‐99 

Jun-‐00 

Jan-‐01 

Aug-‐01 

Mar-‐02 

Oct-‐02 

Dec-‐03 

Jul-‐04 

Month 

May-‐03 

Dec-‐03 

Jul-‐04 

Month 

PCA1 Mean of correlayons 

Feb-‐05 

Sep-‐05 

Feb-‐05 

Sep-‐05 

Apr-‐06 

Nov-‐06 

Apr-‐06 

Nov-‐06 

Jun-‐07 

Jan-‐08 

Jun-‐07 

Jan-‐08 

Aug-‐08 

Mar-‐09 

Aug-‐08 

Mar-‐09 

Oct-‐09 

May-‐10 

PCA2 Variance of correlayons 

Oct-‐09 

Dec-‐10 

May-‐10 

Dec-‐10 

32

FIGURE 6 

Variation explained by principal components for the sample of brokers, banks, FHCs, hedge 

funds, and insurance companies, with insurance companies separated by type, December 1996 

through December 2010 

VariaOon explained 

100% 

90% 

80% 

70% 

60% 

50% 

40% 

30% 

20% 

10% 

0% 

Dec-‐96 

Jul-‐97 

PCA1 PCA2 PCA3 PCA4 PCA5 PCA6 PCA7 PCA8 PCA9 

Feb-‐98 

Sep-‐98 

Apr-‐99 

Nov-‐99 

Jun-‐00 

Jan-‐01 

Aug-‐01 

Mar-‐02 

Oct-‐02 

May-‐03 

Dec-‐03 

Month 

Jul-‐04 

Feb-‐05 

Sep-‐05 

Apr-‐06 

Nov-‐06 

Jun-‐07 

Jan-‐08 

Aug-‐08 

Mar-‐09 

Oct-‐09 

May-‐10 

33 

Dec-‐10

Insurance and Interconnectedness in the Financial Services Industry

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