Convergence and risk-return linkages across financial service firms

Journal of Banking & Finance 31 (2007) 1167–1190

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Convergence and risk-return linkagesacross financial service firms

Elyas Elyasiani a,*, Iqbal Mansur b,1, Michael S. Pagano c,2

a Fox School of Business and Management, Temple University, PA 19122, United Statesb School of Business Administration, Widener University, Chester, PA 19013, United States

c Villanova School of Business, Villanova University, Villanova, PA 19085, United States

Available online 1 December 2006

Abstract

We examine the risk and return linkages across US commercial banks, securities firms, and lifeinsurance companies during the 1991–2001 period. After controlling for changes in the broader stockmarket, interest rates, and foreign currency values, we find that return and risk interdependenciesacross these financial firms are significant and size-varying; larger institutions display stronger vol-atility transmission linkages, while smaller ones exhibit more prominent return-related linkages. Thetighter link in risk among large financial institutions (FIs) suggests stronger convergence, employ-ment of common models of risk measurement and risk management, and more intense inter-industrycompetition, particularly between large banks and large securities firms, compared to smaller insti-tutions. Lack of risk spillover among smaller FIs confirms the intuition that they typically assumemore localized and idiosyncratic risk. The co-movement of stock returns among smaller FIs has beenhelped by the effects of locally based factors, such as economic conditions and state regulations, onall such institutions, and a less diversified product set. Differences in spillover patterns between largeand smaller institutions have implications on investment choices and mergers and acquisitions in theindustry. Introduction of the Gramm-Leach-Bliley Act (1999) has had dissimilar effects on the risk-iness of large versus smaller life insurance and securities firms, and an insignificant effect on commer-cial banks.� 2006 Elsevier B.V. All rights reserved.

0378-4266/$ - see front matter � 2006 Elsevier B.V. All rights reserved.

doi:10.1016/j.jbankfin.2006.10.006

* Corresponding author. Tel.: +1 215 204 5881; fax: +1 215 204 1697.E-mail addresses: [email protected] (E. Elyasiani), [email protected] (I. Mansur), Michael.Pagano@

villanova.edu (M.S. Pagano).1 Tel.: +1 610 499 4321; fax: +1 610 499 4614.2 Tel.: +1 610 519 4389; fax: +1 610 519 6881.

mailto:[email protected]

mailto:[email protected]

mailto:Michael.Pagano@ villanova.edu

mailto:Michael.Pagano@ villanova.edu

1168 E. Elyasiani et al. / Journal of Banking & Finance 31 (2007) 1167–1190

JEL classification: G20; G21; G12; C32

Keywords: Banks; Investment banks; Insurance companies; Convergence; Spillover; GARCH

1. Introduction

In the US financial services industry, the trends towards consolidation, conglomeration,and globalization have accelerated over the last two decades. These trends, aided by majortechnological innovations and regulatory changes, have led to convergence of differenttypes of financial institutions (FIs), and the increasing dominance of large FIs in the finan-cial services industry.3 Scale and scope economies, one-stop shopping convenience,strength to withstand crises and foreign competition, increased diversification opportuni-ties, and more vigorous inter-industry competition are cited among the benefits of thesephenomena. These trends have also attracted serious scrutiny from regulators andresearchers because the sheer size of these ‘‘mega’’ firms has led to greater concerns overheightened market power, increased systemic risk, stronger moral hazard incentives, andthe rising costs of the ‘‘too-big-to-fail’’ doctrine.4

The overall costs and benefits of the trends noted above depend critically on whether ornot the riskiness and returns of the large FIs in different industries have become so closelycorrelated that adverse conditions within a subset of firms can quickly and strongly ‘‘spillover’’ to other firms within the financial services industry, bringing about a system-wideproblem.5 Thus, it is important to examine the following questions: (1) are the risks andreturns of banks, brokerage firms, and insurance companies tightly linked?, (2) does the

3 De Nicolo et al. (2004) provide a detailed discussion of these three trends and their effects on the riskiness ofthe world’s 500 largest financial firms. Consolidation refers to the steadily declining number of competitors withina market through merger. Conglomeration describes financial firms’ expansion of their product offerings to takeadvantage of synergies. Globalization refers to increased interconnection among financial markets and firmsacross national borders as a result of declining barriers for bank competition worldwide, faster and cheapertransfer of funds, and international harmonization of bank regulations (e.g., the Basle Accord). Convergencerefers to the increased similarity of products across different types of FIs and a blurring of their distinctions. Jonesand Nguyen (2005) report that the top 100 US banking organizations accounted for 81% of all US banking assetsand 72.5% of domestic deposits in 2003.

4 For example, Marcus (1984) shows theoretically, and Keeley (1990) confirms empirically, that banks haveincentives to pursue dichotomous capital strategies; over-leveraging the bank when the value of the bank’spotentially mispriced deposit insurance ‘‘put option’’ outweighs the incentive to under-leverage the firm in orderto protect the bank’s charter value, and vice versa. In addition, Boyd and De Nicolo (2005) develop a theoreticalmodel that demonstrates how a more concentrated banking market can increase bank risk-taking by creatingmore severe moral hazard problems. For example, borrowers become riskier, as a bank (due to its greater marketpower in a concentrated market) charges a higher interest rate, and thus creates a Stiglitz and Weiss (1981) moralhazard problem. Demsetz and Strahan (1997) document that larger banks are better diversified than smallerbanks but they use this advantage to increase their financial leverage and to engage in riskier lending activities.

5 Forbes and Rigobon (2002) distinguish between contagion and interdependence as follows: ‘‘it is onlycontagion if cross-market co-movement increases significantly after the shock. Otherwise, a continued high levelof market correlation suggests prevalence of strong linkages between the two economies in all states of the world.This paper uses the term interdependence to refer to this situation’’. Alternatively, Bekaert et al. (2005) describecontagion as the ‘‘correlation [between markets or firms] over and above what one would expect from economicfundamentals.’’ De Nicolo and Kwast (2002) and Lang and Stulz (1992) review the interdependencies amongbanking and corporate firms, respectively.

E. Elyasiani et al. / Journal of Banking & Finance 31 (2007) 1167–1190 1169

size of the FI affect the magnitudes of these risk-return linkages? and (3) has the Gramm-Leach-Bliley Act (GLBA), designed to modernize the industry, significantly affected FIs’risk and return patterns?

We use a multivariate GARCH model to investigate the inter-industry transmission ofchanges in the level and volatility of stock returns. While there have been numerous stud-ies of inter-market interdependence and the more extreme case of contagion, our analysisis the first to focus on return and risk linkages across the three key areas of commercialbanking, brokerage/securities, and life insurance. Our estimation is carried out for largeand small FIs separately in order to determine how these linkages differ between the megafirms and smaller financial service firms, and how consolidation in these industries canalter the spillover patterns.6 The strength of the interdependence, or the spillover effect,can also help determine the degree of convergence and the intensity of competitionbetween the financial services firms in different industries, with stronger spillover effectsindicating closer convergence and more intense competition. Lastly, we examine the effectsof passage of GLBA in 1999 on the risk and return levels of large and smaller firms in oursample.

Our main findings are that large banks, brokers, and insurance companies exhibit muchlarger volatility spillovers than their smaller counterparts, while the converse is true forspillover of stock returns. These results provide evidence of convergence across FIs of dif-ferent types as well as effective inter-industry competition, particularly between largebanks and securities firms. The predominantly strong risk linkages among the latter insti-tutions may be due to common sources of risk, and adoption of similar models of riskassessment and risk management by the three industries. Our finding of stronger co-move-ment among stock returns of smaller FIs in different industries, relative to their largercounterparts, is most likely due to the stronger effects of locally based factors such as eco-nomic conditions and state-specific regulations on all such institutions, and a less diversi-fied product set.7,8

We also find that the introduction of GLBA had no effect on the stock returns and risksof banks of any size, but it did have size-sensitive and FI-type-specific effects on the vol-atilities of securities firms and insurance companies.9 The rest of the paper is organized asfollows. Section 2 provides a brief review of the relevant literature, Section 3 discusses themodel and methodology, and Section 4 describes the data. Section 5 presents the empiricalresults and Section 6 concludes.

6 Selection criteria for identifying Large FIs are described in Section 4. These criteria yielded a set of large FIswith an average equity market capitalization of $19.8 billion during 1991–2001, more than 20 times larger thanthe 1991–2001 average market capitalization of our smaller FI category ($0.8 billion).

7 We do not provide formal tests of these potential causes of the observed risk and return linkages because suchan investigation is beyond the scope of our paper. We would like to thank an anonymous reviewer for suggestingthat additional tests pertaining to possible causal factors for these co-movements can be a fruitful avenue forfuture research.

8 Regulatory constraints are typically more binding for smaller financial firms; larger institutions can moreeffectively circumvent the regulation using their access to large money markets, substitution within their well-diversified product basket, and shifting operations to international markets as needed.

9 This finding is similar to Hendershott et al. (2002) who report positive short-term abnormal returns forinsurance companies and investment banks but insignificant effects for commercial bank returns after severalGLBA legislative announcements during May–November, 1999.


2. Relevant literature

Studies of risk and return spillover initially focused on industrial firms (e.g., Lang andStulz, 1992). The central result of these studies is that intra-industry contagion effects canbe significant and that contagion events can typically be characterized as information-based, rather than panic-based. In the financial services sector, the analysis of intra-indus-try effects has been primarily limited to contagion within the banking industry (e.g.,Aharony and Swary, 1983, 1996; Brunner and Simms, 1987; Weigand et al., 1999),although some studies related to the insurance industry have also been undertaken (Fennand Cole, 1994; Brewer and Jackson, 2002). Studies of contagion in the banking and insur-ance industries reinforce the evidence in favor of information-based, rather than panic-based (or pure) contagion.

Research on international interdependencies of security returns is extensive but nostudy has focused directly on the convergence of financial institutions across major finan-cial product lines. For example, there has been research on integration within the bankingindustry in Europe, integration of the European retail lending markets (Dermine, 1996;Kleimeier and Sander, 2000), and convergence of European corporate financing flows(Schure et al., 2004; Murinde et al., 2004). However, these studies focus on subsets ofthe three main forms of FIs (banks, brokers, or insurers) and do not perform tests of con-vergence across FI types. Overall, these studies suggest that there has been some degree ofintegration across European FIs but it is mostly concentrated within specific sub-regionsand particular financial products.

Similar to the literature on European FIs, contagion studies are generally limited to oneindustry. Brewer and Jackson (2002) is an exception in that it investigates inter-industrycontagion between US banks and life insurance companies and finds strong evidence ofboth intra- and inter-industry information-based contagion. Our analysis extends thescope of the Brewer and Jackson (2002) study by including a third set of FIs (securitiesfirms) and by performing a time series analysis of interdependencies within a GARCH sys-tem in a longer and more recent period. The GARCH framework is more elaborate thanthe basic event study model used by Brewer and Jackson, in that it allows for time varyingvolatility, volatility interdependence across financial industries, and testing of hypothesesrelated to cross equation restrictions.

The study of cross-industry interdependence is of importance and interest for severalreasons. First, the findings have major implications for risk management by bank man-agers, investors in financial markets, and regulators working to prevent inter-industryfinancial crises. Second, academics and policy makers concur that financial firms are‘‘special’’ and ‘‘distinct’’ from other industries due to their influential role in the trans-mission of monetary policy, administration of the payment mechanism, and sectoralcredit allocation function (Saunders and Cornett, 2006). Therefore, FIs are likely to dis-play responses to received shocks which are dissimilar to those of other industries. Inparticular, there is a common perception that spillover is more likely to occur amongfinancial firms, than non-financial companies, and that it spreads more quickly to theoverall economy (Kaufman, 1994). Third, the inter-relations among financial institu-tions, and their relationship with financial markets, have been changing at a rapid pacedue to deregulation, financial innovation, and technological change. Hence, these rela-tionships need to be re-examined (Allen and Gale, 2000 and Allen and Santomero,1997).


The effects of size on systemic risk and the strength of spillover effects are also of inter-est because a ‘‘financial meltdown’’ generally starts with problems plaguing the largestinstitutions and because the character of a spillover differs between FIs of different sizes.The increasing trend of consolidation observed in the financial services industries gives thisissue added importance. It is noteworthy that, from a theoretical perspective, firm size caneither reduce or increase a firm’s risk. Risk reduction can be achieved because increasedsize allows FIs to diversify more extensively and to benefit from economies of scale inmanagement of risk. In contrast, forces increasing FI risk include increased moral hazardincentives and/or agency problems associated with managing a more complex institution.Boyd and De Nicolo (2005) provide a theoretical explanation based on moral hazard andcontracting theory for why bank risk-taking can actually increase as banking assetsbecome more concentrated within fewer banks. Thus, Boyd and De Nicolo’s analysis sug-gests that the consolidation of the US banking industry might lead to increased risk at theindividual bank and system levels.

The net effect of these competing forces on systemic risk is an empirical question thathas created a burgeoning literature. In a review of banking industry consolidation, Bergeret al. (1999) report that the ever-growing size of banks has increased market power in someareas, improved profit efficiency, created more diversified FIs, heightened systemic risks,and broadened the government safety net. Demsetz and Strahan (1995) and Demsetzand Strahan (1997) show that there are significant differences in the diversification andfinancial leverage strategies of large and small banks. Larger banks are better diversified(geographically and product-wise) but also more highly leveraged and less liquid. As aresult, larger FIs tend to have a greater systematic risk (market beta) than smaller banks,although their overall (total) risk (the sum of systematic and idiosyncratic risks), is not sig-nificantly different from the latter.10 Thus, while the overall level of a bank’s total risk maynot be affected directly by firm size, the composition of the bank’s risk is clearly influencedby the firm’s type of investments, diversification opportunities, and financial leverage deci-sions, all of which are typically influenced by the size of the bank.

DeYoung and Roland (2001) find that differences in a bank’s product mix (lending andfee-based services) can lead to different levels of earnings volatility. Contrary to conven-tional wisdom in banking, the authors find that banks with a larger percentage of fee ser-vices (particularly firms that cross the 40%-of-revenues threshold) have greater earningsvariability due to higher degrees of financial and operating leverage, as well as more vol-atile revenues. Since these firms are typically larger banks, DeYoung and Roland (2001)support the notion that variation in the operating leverage and product mix of these largerfirms is most likely to be greater than those found for smaller institutions. Consistent withDeYoung and Roland (2001), Stiroh (2004) also finds size-based differences in the riskinessof US commercial banks. Based on these findings, a threshold effect related to the level offee-based income may explain the degree to which cost structures, technology investments,and earning volatility vary across FIs and to what extent the spillover of risk and returnacross large FIs may differ from those of the smaller FIs within the banking, brokerage,and insurance industries.

10 Allen and Jagtiani (1997) estimate a two-factor return generating model and also find that the systematic riskof generally large, publicly traded commercial banks and insurance companies is substantial (market betas greaterthan 1.0) and has increased during 1974–1994.


Demsetz and Strahan (1997) also argue in favor of the dissimilarity of spillover betweenlarge FIs of different industries and their smaller counterparts. According to these authors,larger banks typically have greater exposure to systematic risk and commensurately loweridiosyncratic risk, compared to smaller banks, with idiosyncratic risk being typicallyrelated to local factors. Since local conditions of a small FI are likely to be uncorrelatedwith those of another small FI, idiosyncratic risks of these small FIs will be uncorrelatedand, consequently, significant risk-related linkages should not be expected between thesesmaller institutions.

In sum, the empirical literature related to the financial services industry on risk andreturn interdependencies suggests that these interdependencies are present in certain situa-tions within the US banking industry. The co-movements of risk and return among highlylevered, more-diversified large FIs are likely to be different from those observed for smallerFIs, which typically operate with less financial leverage, focus on a specific region and cli-ent base, have a narrower range of products, and are influenced more heavily by idiosyn-cratic risk factors. The spillover of risk and return across commercial banks, brokers, andlife insurance companies, and the effect of firm size on the strength of the spillover, havebeen largely unexplored. We turn to these questions in the following sections.11,12

3. Methodology and model

The multivariate GARCH methodology is employed here to model the FIs’ stockreturn and risk spillover effects.13 Song (1994), Flannery et al. (1997), Elyasiani and Man-sur (1998), and Brewer et al. (forthcoming) model the time-varying risk premium of finan-cial institutions using univariate GARCH models. Elyasiani and Mansur (2003) utilize abivariate GARCH methodology to determine the return interdependence and volatilitytransmission among the major US, German, and Japanese banks. A multivariate GARCHmethodology is also used by Elyasiani and Mansur (2004) to test the relative sensitivitiesof bank stock returns to short-term and long-term interest rates. Our approach is mostclosely aligned to the latter study, where we attempt to determine the return interdepen-dence and risk transmission across banks, securities firms, and insurance companies usinga multivariate GARCH methodology.

We employ an eight-equation System-GARCH model (Eqs. (1)–(8) below) to describethe stock return (Ri) and conditional stock return volatility (hii) behavior of three maincategories of FIs (i = 1,2,3); banks (BNK), securities/brokerage firms (BKF), and lifeinsurance companies (LICs). Eqs. (1), (3), and (5) in this model, presented as extendedmarket models, describe the return generating processes and are functions of the marketreturn (RM), an interest rate index (the change in the level of interest rate, DI), a foreign

11 FI categories can be extended to include mutual and hedge funds as well. However, such an extension isdifficult to manage in a multivariate GARCH framework. As noted in De Nicolo et al. (2004), banks, securitiesfirms, and life insurance companies are the largest and most prominent FIs.12 Similar to our analysis of large versus small FI behavior, a host of studies examines the ability of small banks

to provide competitive products in an industry witnessing an increasing trend of consolidation (e.g., Goldbergand White, 1998; DeYoung et al., 1999 and Cole et al., 2004).13 Various methods have been used to model the time varying second moment, e.g., ARCH (Engle, 1982),

GARCH (Bollerslev, 1986), stochastic volatility (Anderson, 1994), ICSS-GARCH (Inclan and Tiao, 1994),Markov switching (Hamilton, 1988, 1989), Switching GARCH (Klaassen, 2002). See Poon and Granger (2003),Andersen et al. (2003), and McQueen and Vorkink (2004) for a detailed discussion.


exchange index (the percentage change in the foreign exchange index, FX),14 the returnspillover effects across different FI categories (Rj and Rk, where j,k 5 i), and a GLBA bin-ary variable (D1).15 The cross-return terms allow for explicit testing of the extent of returninterdependence among FIs. The GLBA binary (D1), takes the value of unity after the pas-sage of GLBA by the Senate on 11/4/1999, and zero before that.

The volatility equations (Eqs. (2), (4), and (6)) extend the traditional GARCH specifi-cation by including the risk spillover effects (hjj,t�1 and hkk,t�1,j,k5j) across different FIs,and the GLBA binary variable (D1). This specification of volatility equations permits usto test the prevalence of risk spillover among FIs.

R1;t ¼ b10 þ b11RMt þ b12DI t�1 þ b13FX t�1 þ b14R2;t�1 þ b15R3;t�1 þ b16D1 þ e1;t; ð1Þh11;t ¼ m10 þ a11h11;t�1 þ k11e

21;t�1 þ u11h22;t�1 þ u12h33;t�1 þ u11D1; ð2Þ


22;t�1 þ u21h11;t�1 þ u22h33;t�1 þ u21D1; ð4Þ


23;t�1 þ u31h11;t�1 þ u32h22;t�1 þ u31D1; ð6Þ

ei;tjX0t�1 � Nð0; hi;tÞ; ð7Þhij;t ¼ qijhi;thj;tð�1 < qij < 1Þ; i 6¼ j: ð8Þ

In the above specification index i (i = 1,2, and 3) represents BNK, BKF and LIC firms,respectively, t is a time index, ei denotes the error term with properties described byEq. (7), and X0t�1 is the information set. The conditional variance–co-variance relationship,specified by Eq. (8), is a constant correlation model which permits the variances to changebut requires the correlation (qij) between the series to be constant.16 The value of qij needsto be estimated along with the model parameters. The coefficients aii and kii in Eqs. (2), (4)and (6) must satisfy stationarity conditions such that (aii > 0), (kii > 0), and (aii + kii) < 1.The sum (aii + kii) serves as a measure of shock persistence in the respective industry.

14 The inclusion of a foreign exchange rate variable (FX) as an explanatory variable for LICs is not common.However, FX is an important factor to consider since FX risk arises for LICs, (i) when premiums are remitted inone currency but benefits are denominated in another; (ii) by investing in foreign securities; and (iii) by reinsuringforeign insurance risks through domestic operation. A review of large LIC 10-K reports suggests sizable FX risk.For example, in 2005 AIG generated $8.25B of its operating income from foreign sources, compared to $3.59Bfrom domestic operations (10-K report, p. 44). As of December 31, 2004, Lincoln National had $1.03B in foreigncurrency denominated investments and foreign currency swaps (10-K, p. 99). Similarly, in 2004, Metlife heldforeign currency derivatives of $4.72B (notional) to hedge the FX risk associated with foreign bonds and loans(10-K, p. F35). Mange (2000) develops mathematical models and provides illustrative measures of the FX rateeffect on LIC products under several scenarios. His findings suggest that incremental FX risk may not be verysignificant in the context of shorter term products but is dominant in relation to longer term products such aswhole life and annuities.15 The absolute values of correlation coefficients for RMt, DIt�1, and FXt�1 are between 0.01 and 0.04. In

addition, the variance inflation factors (VIF) for these variables are close to 1.0 and are, therefore, well below thecritical value of 10.0. As such, the presence of considerable multicollinearity can be ruled out.16 Other specifications such as VECH-GARCH model proposed by Bollerslev et al., 1988) fail to ensure that the

conditional variance–co-variance matrix of returns are positive semi-definite. The constant correlation GARCHmodel suggested by Bollerslev (1990) is an alternative specification to ensure a positive semi-definite conditionalvariance co-variance matrix. For a discussion of the constant correlation GARCH model, see Kroner and Sultan(1993) and Park and Switzer (1995).


Our model follows Campbell and Hamao (1992). In Campbell and Hamao’s model,excess returns are described as the sum of expected returns, and K factor realizations mul-tiplied by their respective factor loadings. The expected return is, in turn, determined asthe summation of the products of ‘‘the market price of risk’’ for each factor, and therespective betas, where the former is described by a set of information variables, laggedone period. Upon substitution, the model describes returns as a function of one concurrentobservable factor and a set of lagged information variables

Ri;;tþ1 ¼ bi1R1;tþ1 þ RainX nt þ ei;tþ1: ð9Þ

Campbell and Hamao use the world index as the concurrent index for the US and Japa-nese stock returns, and dividend/price ratio, relative short-term interest rates, and long-short yield spread as the lagged information variables. In the current study, the concurrentfactor is the market index, and the information variables include the interest rate andexchange rate. This approach is also consistent with Bailey and Chung (1995) andBadrinath et al. (1995).17

Empirical analysis of the spillover issues sheds light on the transmission mechanism ofrisk and return across financial industries. Specifically, the results found here identify thedirection and the strength of the spillover effect, indicating whether a unidirectional or abidirectional causality mechanism is in effect. Unidirectional spillover demonstrates a lead-ership-followership pattern across the three industries considered and can help determinethe predictability of the followers’ stock returns. These results can also help determinewhether the asymmetric predictability of equity returns found for industrial firms canbe generalized to financial firms (e.g., see Fleming et al., 1998; for an analysis of non-finan-cial firms).

Finally, this analysis has implications for FI diversification strategies and the formula-tion of an effective regulatory policy. For example, if risk transmission is found to be uni-directional from banks to brokerage firms and LICs, rather than in the opposite direction,then legislators and investors should take this asymmetric risk spillover into account whendevising new regulations and making investment decisions, respectively. Similarly, ifreturn interdependence is found to be dominant, it can have a far-reaching impact onthe investment decisions of investors interested in making new investments in the USfinancial intermediaries, as well as decisions related to the proper allocation of economicresources to this sector of the US economy.

4. Data

Daily returns for the S&P 500 index (the market index), as well as stock returns on allcommercial banks, all securities/brokerage firms, and all LICs traded on the NYSE,Amex, and Nasdaq, and the year-end equity market capitalization data are obtained fromthe Center for Research in Security Prices (CRSP) database. The sample period runs fromJanuary 2, 1991 to December 31, 2001. We employ the standard industry classification

17 Bailey and Chung use short-term peso-dollar yield spread, free-market premium for dollar, yield spreadbetween Mexican and US government dollar debt, and dividend yield on the IPC index return as laggedinformation variables. Badrinath et al. (1995) show that current returns of small firms are correlated with thelagged returns of larger firms. Elyasiani and Mansur (2003) incorporate the three risk factors used in the currentstudy and adopt a lagged structure.


(SIC) codes of 6021–6022 for our commercial bank groups, and SICs 6211 and 6311 toidentify companies for the BKF and LIC groups, respectively. Nine equally-weighted port-folios, three for each of the three types of FIs, are constructed and used in the estimationof various models. The nine portfolios are: All Bank, All Brokerage, All LICs, LargeBank, Large Brokerage, Large LICs, Smaller Bank, Smaller Brokerage, and Smaller LICs.

To minimize the problem of survivorship bias, the data set for each day includes allthose firms whose stocks are actively traded on that particular day. It follows that, dueto factors such as mergers and acquisitions, spin-offs and IPOs, the sample membershipvaries over time. This procedure allows the use of all available data for each tradingday and, thus, enables us to maximize the sample size in each portfolio.18

The 10-year US Treasury constant-maturity bond yield is used as the long-term interestrate and is obtained from the Federal Reserve Bank of St. Louis website. We use a long-term rate in the analysis because Akella and Chen (1990), Bae (1990), Browne et al. (1999),and Elyasiani and Mansur (2003, 2004), among others, have found that long-term interestrates exert a stronger influence on LICs and commercial banks, than short-term rates. Thetrade-weighted US Dollar index is used in construction of the foreign exchange index andis also obtained from the Federal Reserve Bank of St. Louis website.

4.1. Formation of portfolios of the large financial institutions

Year-end equity market capitalization for the years 1991–2001 were used to rank allinstitutions in order to determine whether they qualify to be a part of the Large FI port-folios. To be included in the Large Bank portfolio, the institutions had to meet the follow-ing criteria: a market capitalization of greater than $10 billion in 2001, and a minimumcapitalization of $2 billion for at least 10 out of the 11 years in the sample. Nine institu-tions exceeded the minimum market capitalization requirement in each year. J.P. Morganand US Bancorp failed this requirement in 1991 but they were, nonetheless, included in thedata set because they are well recognized as major banking institutions and also becausethey exhibited exceptional growth during the sample period. Therefore, a total of 11 insti-tutions form the Large Bank portfolio.19

For the Large Brokerage firm portfolio, the selection criteria include a minimum mar-ket capitalization of $1 billion in 2001 and $1 billion for 9 out of the 10 remaining yearswithin our sample period. All five of the institutions included (A.G. Edwards, BearStearns, Charles Schwab, Merrill Lynch, and Morgan Stanley Dean Witter) exceededthe minimum capitalization requirement in each year, except for Charles Schwab in1992, which came very close at $0.986 billion. Similarly for the Large LIC sample, the min-imum capitalization requirement was $2 billion in year 2001 and $1 billion in other years.Five institutions (Aegon, American International Group, American National, Jefferson

18 For All Bank, All Brokerage, and All LIC portfolios, the sample membership was between 144–404, 17–49,and 17–37 firms, respectively. Eleven, five, and eight firms formed the Large Bank, Large Brokerage and LargeLIC portfolios, respectively. The remainder in each category formed the Smaller FI portfolios.19 Although the criteria described here are somewhat arbitrary, they enable us to focus on the largest 5–10

financial firms within each of the three types of FIs. In 1991, J.P. Morgan had a market capitalization of $1.92billion and US Bancorp $1.88 billion, not far from the minimum $2 billion requirement. The 11 banks included inthe portfolio are: Bank of America, Bank of New York, Citigroup, FleetBoston Financial, J.P. Morgan, Keycorp,PNC Financial, State Street Corp., US Bancorp, Wachovia, and Wells Fargo.

$0

$10,000,000

$20,000,000

$30,000,000

$40,000,000

$50,000,000

$60,000,000

$70,000,000

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

Banks

Brokerage Firms

Life Insurance Cos.

Fig. 1. Average equity market capitalization for large FIs: (1991–2001).


Pilot, and Lincoln National) met both of the above criteria for each year. Three additionalLICs were also included because of their size even though their stock was not publiclytraded for the entire duration of our sample. They are Manulife Financial (started 9/28/1999), Met Life (started 4/6/2000) and Sun Life (started 3/27/2000). Each of these institu-tions clearly exceeded the minimum capitalization requirement from the very beginning oftheir publicly traded status in 1999–2000. Therefore, eight institutions formed the LargeLIC portfolio during the latter part of our sample (i.e., five LICs were used for 1991–1998, six in 1999, and eight for 2000–2001). Our selection criteria yielded Large FIs thatwere substantially larger in terms of market capitalization than the FIs included in theSmaller FI categories (on average, $19.8 versus $0.8 billion during 1991–2001). Fig. 1shows the average size of the three types of Large FIs for 1991–2001 and illustrates therapid growth in market capitalization for these firms.

5. Empirical results

5.1. Descriptive statistics

Table 1 presents the descriptive statistics for the portfolio return series. The resultsshow that the average daily returns of FIs typically range between 10 and 18 basis points,except for the large LICs that earned 6 basis points per day.20 In contrast to the riskiness

20 The daily mean returns reported in Table 1 appear to be relatively high. This is partially due to the substantialreturn volatility and large bank ‘‘merger mania’’ during the late 1990s ‘‘internet bubble’’ period. Equality ofmeans between large and small firms is tested. It is notable that only LICs exhibit a statistically significant size-based difference in returns.

Table 1Summary statistics of bank, brokerage, and LIC stock returns

All institutions Large institutions Small institutions

BNK BFK LIC BNK BFK LIC BNK BFK LIC

No. of observations 2869 2869 2869 2869 2869 2869 2869 2869 2869Mean 0.00101 0.00171 0.00115 0.00103 0.00137 0.00057 0.00100 0.00178 0.00124Standard Deviation 0.00494 0.01612 0.00965 0.01495 0.02037 0.00722 0.00475 0.01768 0.01139Minimum �0.03626 �0.11720 �0.05135 �0.07353 �0.1171 �0.05950 �0.03564 �0.11721 �0.05433Maximum 0.03330 0.22988 0.06196 0.09766 0.12716 0.05285 0.03126 0.27010 0.07166Median 0.00109 0.00086 0.00065 0.00016 0.00033 0.00020 0.00105 0.00053 0.00076Skewness �0.415*** 1.745*** 0.222*** 0.273*** 0.209*** �0.268*** �0.456*** 2.304*** 0.439***

Kurtosis 5.823*** 22.259*** 2.549*** 3.619*** 3.222*** 7.572*** 5.868*** 28.876*** 2.720***

J–B (MSL) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Q(5) 21.876*** 11.794*** 7.129** 3.729 383.442*** 6.183** 23.404*** 12.029*** 7.469**

Q(10) 27.759*** 25.327*** 9.540 10.862 385.583*** 10.381 30.001*** 28.057*** 8.999Q(20) 55.470*** 41.788*** 30.422** 29.937** 404.081*** 19.016 61.232*** 42.920*** 34.908***

Q2(5) 14.602*** 248.405*** 19.813*** 2.944 6.813** 37.412*** 17.584*** 621.866*** 11.479***

Q2(10) 40.660*** 249.926*** 29.444*** 10.530 12.832* 72.511*** 45.340*** 660.985*** 18.441**

Q2(20) 78.875*** 268.574*** 46.413*** 39.893*** 35.930*** 91.033*** 85.243*** 671.141*** 38.196***

BNK, BKF, and LIC denote Bank, Brokerage Firm, and Life Insurance Company portfolios, respectively. J–B is the Jarque–Bera joint normality test statistic. MSLrefers to marginal significance level. Q(n) and Q2(n) are the Ljung–Box test for the 5th, 10th, and 20th order serial correlation in return and squared return series. Thecritical values for 5, 10, and 20 degrees of freedom are 11.07, 18.30, and 31.41 at the 5% level, respectively.***, **, and * represent significance at the 1%, 5%, and 10% levels, respectively.

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(standard deviation of returns) of large brokerage firms and insurers, which is similar totheir smaller counterparts (about 0.02 for brokerage firms and 0.01 for insurers), the risk-iness of large banks is substantially higher than those of the smaller ones (0.015 versus0.005). This finding is in contrast to Demsetz and Strahan (1997) who report similar levelsof total risk for small and large banks. It is noteworthy, however, that unlike Demsetz andStrahan, our sample includes the internet bubble period and also coincides with the late-1990s wave of large bank mergers, the latter of which may have led to higher return vol-atility for large banks relative to the smaller banks in the sample.

For all portfolios, the unconditional distributions of returns are non-normal as evi-denced by significant skewness and kurtosis, as well as significant Jarque and Bera(1981) statistics. The significant kurtosis values indicate that the distributions of all returnseries are leptokurtic. The Ljung and Box (1978) test statistics for the return and thesquared return series reject the null hypothesis of no autocorrelation and no ARCH effect,respectively, for all nine portfolios. These findings suggest that a GARCH-type process isindeed appropriate for modeling FI stock returns.

To identify whether the time series of portfolio returns, interest rates, and exchangerates are stationary, a number of tests are performed. The test procedures include the Aug-mented Dickey-Fuller (ADF) test (Dickey and Fuller, 1979, 1981), and the Phillips-Perrontest (Perron, 1988; Phillips and Perron, 1988). The findings (available on request) indicatethat all stock return series follow an I(0) process, and thus are considered stationary. Thelong-term interest rate and exchange rate series follow an I(1) process, but the first differ-ence of the interest rate (DI) and the percentage change in the exchange rate series (FX)both revert to an I(0) process. Thus, the transformed versions of interest and exchangerates are used in our analysis.

5.2. Multivariate GARCH results

5.2.1. Systematic risk

Estimation of the model (Eqs. (1)–(8)) produces nine sets of coefficients. Table 2, PanelsA–C, report the results for the Large FI, Smaller FI, and All FI portfolios, respectively.21

For the Large FIs, the figures reported in Panel A show that the market betas for banks(BNK) and brokerage firms (BKF) are greater than 1.0 while the market beta for largeLICs is significantly lower than 1.0. In addition, all market beta estimates are highly sig-nificant. In comparison to the market betas for their smaller counterparts, reported inPanel B, the systematic risk exposures of the large banks and brokers are significantlyhigher, whereas the betas of the large and small LICs are quite similar. The finding thatlarge banks take on greater market risk than their smaller counterparts is consistent withDemsetz and Strahan (1995) and Demsetz and Strahan (1997).

The larger beta value of the large banks is possibly due to their assumption of greatercredit risk, higher financial leverage, more extensive engagement in risky off balance sheetactivities (e.g., trading and derivative positions), and the more aggressive attitudes of their

21 Intra-industry spillover of risk and return between large and small firms is also analyzed for all three FIcategories. The results (available on request) show the presence of bi-directional return spillover but no volatilityspillover, with the exception of large LICs affecting small LIC volatility. The GLBA coefficients are positive andsignificant only for large institution portfolios. In addition, all ARCH and GARCH coefficients are significantand their sums are less than unity, as required by stability conditions.

Table 2

Spillover of risks and returns among different sized financial institutions

Coefficients Panel A: Larger Panel B: Smaller Panel C: All

BNK (i = 1) BKF (i = 2) LIC (i = 3) BNK (i = 1) BKF (i = 2) LIC (i = 3) BNK (i = 1) BKF (i = 2) LIC (i = 3)

bi0 Intercept 0.050 (3.451)*** 0.044 (1.779)* 0.026 (2.797)*** 0.095 (13.311)*** 0.062 (2.315)*** 0.101 (5.334)*** 0.090 (12.592)*** 0.062 (2.776)*** 0.087 (5.443)***

bi1 Market 1.126 (77.764)*** 1.469 (59.415)*** 0.373 (36.729)*** 0.241 (40.802)*** 0.543 (23.249)*** 0.367 (25.893)*** 0.279 (44.981)*** 0.735 (36.989)*** 0.449 (36.505)***

bi2 IR �0.105 (�0.438) �0.443 (�1.103) �0.397 (�2.448)** �0.185 (�1.796)* �0.282 (�0.751) �0.035 (�0.143) �0.113 (�1.085) �0.355 (�1.370) �0.142 (�0.067)

bi3 FX 1.983 (0.540) �9.252 (�1.597) 3.820 (1.674)* �1.394 (�0.897) �5.523 (�0.933) 5.762 (1.448) �0.642 (�0.402) �7.054 (�1.370) 8.156 (2.397)**

bi4 Cross-return 0.033 (4.113)*** 0.115 (6.278)*** 0.011 (1.087) 0.009 (2.654)*** 0.199 (3.664)*** 0.112 (3.150)*** 0.012 (3.116)*** 0.206 (4.355)*** 0.101 (3.309)***

bi5 Cross-return 0.011 (0.493) 0.001 (0.035) 0.0109 (1.633) 0.027 (5.109)*** 0.047 (2.122)** �0.0002 (�0.019) 0.038 (5.584)*** 0.064 (2.691)*** �0.003 (�0.298)

bi6 Mean GLBA DV 0.027 (0.496) 0.100 (1.316) 0.0509 (1.323) 0.004 (0.292) 0.067 (0.982) �0.031 (�0.972) 0.004 (0.223) 0.051 (0.817) �0.007 (�0.229)

mi0 Intercept �0.017 (�1.503) 0.062 (1.636) 0.005 (1.985)** 0.010 (5.391)*** 0.137 (4.850)*** 0.045 (5.162)*** 0.009 (5.221)*** 0.056 (3.886)*** 0.011 (3.396)***

ai1 GARCH 0.720 (19.395)*** 0.624 (9.923)*** 0.781 (16.846)*** 0.806 (43.530)*** 0.781 (71.202)*** 0.847 (96.175)*** 0.822 (45.323)*** 0.842 (147.245)*** 0.929 (227.800)***

ki1 ARCH 0.131 (9.639)*** 0.063 (4.955)*** 0.029 (15.375)*** 0.124 (10.082)*** 0.156 (26.259)*** 0.118 (24.506)*** 0.103 (9.813)*** 0.111 (31.454)*** 0.059 (35.186)***

ui1 Cross-volatility 0.025 (1.195) �0.101 (�1.838)* 0.022 (3.700)*** 0.0001 (0.695) 0.011 (0.092) 0.011 (0.291) �0.000 (�0.010) 0.009 (0.125) �0.015 (�0.896)

ui2 Cross-volatility 0.322 (1.719)* 1.990 (4.752)*** 0.016 (2.919)*** �0.0002 (�0.328) 0.013 (1.023) 0.0004 (0.426) 0.0001 (0.112) 0.015 (1.207) 0.0002 (0.428)

ui1 Volatility GLBA DV �0.020 (�0.475) �0.352 (�2.843)*** 0.038 (3.431)*** �0.0001 (�0.076) 0.046 (2.013)** �0.026 (�4.414)** 0.003 (2.168)** 0.056 (3.834)*** �0.004 (�2.076)**

(ai1 + ki1) Persistence 0.851 0.687 0.810 0.930 0.937 0.965 0.925 0.953 0.988

LL Log Likelihood �2685.133 �2271.58 �1144.04

Panel D: Model diagnostic statistics

Based on (ei,t/p

hii)

Skewness 0.169*** 0.375*** �0.123*** 0.169*** 0.565*** 0.283*** 0.137*** 0.588*** 0.305***

Kurtosis 1.619*** 1.956*** 2.781*** 1.619*** 3.495*** 1.709*** 1.676*** 3.458*** 1.670***

J–B (MSL) Normality 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Q(5) Serial correlation 216.525*** 10.945*** 24.169*** 216.525*** 42.146*** 106.620*** 204.415*** 35.138*** 100.350***



Based on (ei,t/p

hii)2

Skewness 5.802*** 7.496*** 8.732*** 5.802*** 11.541*** 5.220*** 5.597*** 11.581*** 5.835***

Kurtosis 56.457*** 98.533*** 120.985** 56.457*** 213.885*** 41.473*** 48.684*** 222.034*** 48.052***

J–B (MSL) Normality 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Q2(5) Serial correlation 9.594* 12.108*** 3.611 9.594* 35.677*** 13.225** 10.653* 38.753*** 16.104***

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Table 2 (continued)

Coefficients Panel A: Larger Panel B: Smaller Panel C: All

BNK (i = 1) BKF (i = 2) LIC (i = 3) BNK (i = 1) BKF (i = 2) LIC (i = 3) BNK (i = 1) BKF (i = 2) LIC (i = 3)

Q2(10) Serial correlation 13.278 19.653*** 8.824 13.278 41.141*** 22.543** 15.415 43.486*** 23.218**

Q2(20) Serial correlation 18.262 24.782* 21.302 18.262 95.394*** 25.954 20.209 75.603*** 27.264

q12 Error correlation 0.331 (5.315)*** 0.188 (9.425)*** 0.185 (7.957)***



The models estimated are as follows:

R1;t ¼ b10 þ b11RMt þ b12DI t�1 þ b13FX t�1 þ b14R2;t�1 þ b15R3;t�1 þ b16D1 þ e1;t ; ð1Þh11;t ¼ m10 þ a11h11;t�1 þ k11e

21;t�1 þ u11h22;t�1 þ u12h33;t�1 þ u11D1 ; ð2Þ


22;t�1 þ u21h11;t�1 þ u22h33;t�1 þ u21D1 ; ð4Þ


23;t�1 þ u31h11;t�1 þ u32h22;t�1 þ u31D1 ; ð6Þ

ei;t jX0t�1 � Nð0; hi;tÞ; ð7Þhij;t ¼ qijhi;thj;tð�1 < qij < 1Þ; i 6¼ j: ð8Þ

Asymptotic t-values are in parentheses. i = 1,2, and 3 represent Bank (BNK), Brokerage/Securities Firm (BKF), and Life Insurance Company (LIC) portfolios, respectively. Q(n) and Q2(n) are the Ljung–Box test for the 5th, 10th, and

20th order serial correlation in standardized and squared standardized residuals. J–B is the Jarque–Bera joint normality test statistic. MSL stands for Marginal Significance Level. qij is the estimate of the constant correlation term.***, **, * represent significance at the 1% , 5%, and 10% levels, respectively.

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managers toward risk. LICs do not exhibit size-based variation in market risk partlybecause both large and small LICs typically hold large investments in fixed income secu-rities, due to insurance regulations, and these securities are typically much less volatilethan the overall stock market. In addition, the liabilities of the LICs are generally long-term and, to a large degree, predictable.22 The results for the All FI portfolios, reportedin Panel C, are similar to those shown in Panel B, for the Smaller FIs. This is not surpris-ing because the All FI portfolio is dominated by the much greater number of Smaller FIsand, thus, tests based on this portfolio tend to follow the pattern reported in Panel B. Toconserve space, we simply note this fact and focus the remaining discussion on the resultsfound in Panels A and B, for the Large and Smaller FIs.

The interest rate and foreign exchange betas for the large LICs take the values of �0.39and 3.82, respectively, and are both statistically significant while those for banks and bro-kers generally are not. This result suggests that large banks and brokers are effectivelyhedging their interest rate and exchange rate exposures while large LICs remain exposedto rising domestic interest rates and a weakening of the US dollar. In addition, large bankstend to securitize much of their loans, thereby shifting their interest rate risk to institu-tional investors purchasing the securitized packages. The significant interest rate risk expo-sure of the LICs may be due to the longer duration of their bond investments relative totheir liabilities. Similarly, the foreign exchange rate sensitivity of the LIC portfolio is dueto their foreign currency denominated assets and liabilities as well as any cash flows relatedto their international operations. The only significant interest rate or exchange rate coef-ficient for banks and brokers is the interest rate beta of the smaller banks, which is nega-tive and significant (at the 10% level).23 The smaller FIs seem to be holding positions thatare less than fully hedged with respect to interest rates and exchange rates.

The sum of the ARCH and GARCH parameters (ki1 and ai1, respectively) provides ameasure of persistence of shocks for each set of FIs. As can be seen in Table 2, PanelsA and B, shock persistence for the large FIs varies over the 0.69–0.85 range and is lowerthan that for the smaller FIs (0.93–0.97). Among the three FI categories, the returns ofsmaller brokers exhibit the widest gap in terms of persistence with those of the large bro-kers (0.93 versus 0.68). Although not as substantial as the difference between small andlarge brokers, banks and LICs also show size-based differences in shock persistencebetween smaller and larger firms (0.93 versus 0.85, and 0.97 versus 0.81, respectively). Thisis not surprising given that the stocks of large FIs are typically more liquid than those ofsmaller FIs and thus, are likely to exhibit less persistence.

5.2.2. Return-related spillover effects

Eqs. (1), (3), and (5) enable us to test whether there are return-related spillover effectsacross banks, brokers, and LICs. In Table 2, Panels A, B, C, the parameters bi4 and bi5, inthe mean return equations represent return-related spillover effects. If either of these twoparameters is significantly different from zero, we can conclude that return spillover

22 For example, 78.6% of LIC’s total liabilities in 2003 were financed by Net Policy Reserves and SeparateAccount business, both of which are characterized mainly as long-term liabilities tied to anticipated life insuranceclaims in the distant future (see Saunders and Cornett, 2006, pp. 70–71 for more details).23 It should be noted that the inclusion of the cross-return or spillover-related variables in our model may have

picked up some of the effects attributable to the interest and exchange rate factors. This point reinforces theimportance of accounting for potential spillover effects.

Table 3Results of hypotheses tests related to return and risk spillover

Panel A: Hypotheses Panel B: v2 Values and Degrees of Freedom (DF)

Hypotheses DF All FIs Large FIs Smaller FIs

Return spillover:

H01: No return spillover: b14 = b15 = b24 = b25 = b34 = b35 = 0 6 116.08*** 72.90*** 76.09***

Symmetry of return spillover:

H02: Symmetry of return spillover between BNK and BKF: b14 = b24 1 21.29*** 49.39*** 14.69***

H03: Symmetry of return spillover between BNK and LIC: b15 = b34 1 19.47*** 0.76 14.96***

H04: Symmetry of return spillover between BKF and LIC: b25 = b35 1 5.59** 0.08 3.83*

Volatility spillover:

H05: No volatility spillover: u11 = u12 = u21 = u22 = u31 = u32 = 0 6 2.50 52.54*** 2.10

Symmetry of volatility spillover

H06: Symmetry of risk spillover between BNK and BKF: u11 = u21 1 0.01 1.47 0.01H07: Symmetry of risk spillover between BNK and LIC: u12 = u31 1 0.77 3.31* 0.08H08: Symmetry of risk spillover between BKF and LIC: u22 = u32 1 1.49 22.77*** 1.10

Hypotheses are tested using the following Multivariate GARCH model:


21;t�1 þ u11h22;t�1 þ u12h33;t�1 þ u11D1; ð2Þ


22;t�1 þ u21h11;t�1 þ u22h33;t�1 þ u21D1; ð4Þ


23;t�1 þ u31h11;t�1 þ u32h22;t�1 þ u31D1; ð6Þ

ei;tjX0t�1 � Nð0; hi;tÞ; ð7Þhij;t ¼ qijhi;thj;tð�1 < qij < 1Þ; i 6¼ j: ð8Þ

***, **, * represent significance at the 1%, 5%, and 10% levels, respectively.

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prevails. Panels A and B of Table 3 summarize our eight spillover-related hypotheses andreport the test results, respectively.

Coefficient estimates reported in Table 2, Panel A, demonstrate that return-spillovereffects are FI-type-specific. Banks and brokers do show return interdependence (the bi4

parameters for banks and brokers are positive and significant) while neither shows signif-icant return spillover with the LICs (none of the other four return-related parameters issignificant). The first row of Table 3 reports the hypothesis test result regarding the pres-ence of any return-related spillover across the three FI types (H01). The test statistics rejectthe null of no-spillover across the three FI categories. The figures in Table 3 show that, forLarge FIs, return spillovers are either symmetrical or non-existent, except for the largebank-broker relation (hypothesis H02). The latter test reveals a significant bidirectional,but asymmetric, effect with banks exerting a stronger influence on brokers than vice versa(parameter b24 in Table 2, Panel A, is greater than the b14). The rapid growth in Section 20affiliates of large commercial banks during the 1990s may have increased the interdepen-dence between these two groups of large FIs.

The lack of spillover between the returns of large banks and brokerage firms on the onehand and large LICs on the other is an indication of non-substitutability of bank’s andbroker’s services for those of the LICs. The market for LICs appears to continue to besegregated from those of the large banks and brokerage firms, while the latter seem to haveconverged, with banks having a larger influence on the brokerage firms. Dissimilarity inthe product baskets and regulatory constraints among these FI types are likely to havecontributed to this phenomenon.

In contrast to the large FIs, Table 2, Panel B, reports that five out of the six return-related spillover parameter estimates are positive and statistically significant, with theexception being the cross-return parameter between smaller LICs and brokers. Conse-quently, there is a much higher level of interdependency between the returns of smallerFIs of different categories, compared to their larger counterparts. In addition, the param-eter estimates for the bank cross-returns with brokerage firm and LICs (b24 and b34) aresubstantially larger in magnitude than the other cross-return parameters, suggesting thatsmaller banks are leaders in terms of return-related shocks across smaller FIs. The testresults for Hypotheses H02–H04, in the third column of rows 2–4 of Table 3, report thecross-return relationships across smaller FIs. The test statistics for these hypotheses con-firm that return spillovers between the three FI categories are asymmetric, with the smallerbanks exerting a larger influence on smaller brokers and LICs, than vice versa.

The greater return spillover for smaller FIs, compared to their large counterparts, couldbe attributed to at least three factors. First, there is some substitutability of retail productsand turf competition among smaller banks, securities firms, and LICs which make theirreturns correlated. Common products include saving and lending products and paymentservices (Saunders and Cornett, 2006, p. 26). Second, these firms typically face greatercommon exposure to regional economic conditions, which also tends to strengthen theinterdependence of their returns. Specifically, a regional economic shock is likely to affectsmaller FIs within the region in a similar manner, leading to a greater correlation betweentheir returns, while large FIs are much more geographically diversified and are less affectedby shocks in one particular region. Third, large FIs are better able to circumvent restrictiveregulations and earn differential returns due to their idiosyncratic product innovations andmanagerial skills. This advantage on the part of the large FIs limits the co-movement intheir returns. However, regulatory requirements are more likely to be binding for smaller


FIs because they lack the financial clout (as well as an implicit ‘‘TBTF’’ safety net) that areavailable to larger FIs. Thus, smaller FIs are more likely to be constrained by regulation ina similar way which, in turn, causes their returns to be correlated.24

5.2.3. Risk-related spillover effects

Eqs. (2), (4), and (6) in the model allow us to identify potential risk-related spillovereffects. The parameters, ui1 and ui2, in Table 2, Panels A, B, C, measure the magnitudesof these effects. Similar to the above discussion of return-related spillover, if either of thesetwo parameters is significantly different from zero, we can conclude that there are spillovereffects between the volatilities of the stock returns of banks, brokers, and LICs. Table 3reports the results of hypothesis tests (H05–H08) related to the presence and symmetryof risk spillovers.

As can be seen by a review of the ui1 and ui2 parameter estimates in Table 2, Panels Aand B, the results related to risk spillover across FIs are opposite to those reported for thereturn-related spillover effects. Panel A shows that five out of the six ui1 and ui2 parameterestimates are significant for the large FI sample while none of these parameters is signif-icant for the smaller FIs displayed in Panel B. The only insignificant risk spillover param-eter for the large FIs is related to the transmission of the large brokers’ risk to the largebanks’ risk level, indicating a unidirectional relationship from banks. Interestingly, theonly exception, where both return and risk spillover are in effect and significant, is betweenlarge banks and large brokerage firms. This confirms the presence of a higher level of con-vergence between banks and brokers, relative to those between these institutions and theLICs. Hypothesis H05 is a joint test to identify whether or not the cross-volatility param-eters are significantly different from zero. The test result shows the presence of significantrisk spillover effects across large FIs of different categories, while smaller FIs fail to dem-onstrate similar risk spillover effects, confirming size-based differences in spilloverpatterns.

Of the five parameters that are significant for the large FIs, only one is negative, indi-cating a negative and significant spillover effect from the large banks’ riskiness to largebrokers’ risk. In this case, a decrease in bank risk leads to a higher risk at brokerage firmsbut not the other way around. This result suggests that large banks play a leadership rolein terms of risk transmission to large brokers. The negative sign may be an indication ofrivalry between the two groups; as banks take away business from the investment banks,they improve their profitability and reduce their own risk, while bringing about the reverseeffects on the latter firms. The test results for Hypotheses H06–H08, in Table 3 exhibitasymmetrical risk spillover effects between large LICs and their large banking and broker-age firm counterparts, with large LICs demonstrating leadership.

Overall, the results reported in Tables 2 and 3 indicate that there is strong evidence ofvolatility spillover between large FIs and that large LICs have a stronger influence on largebanking and brokerage firms. The leadership of large LICs in terms of volatility transmis-sion can be due to the fact that LICs historically have had less stock return volatility thanbanks and brokers (as noted earlier in Table 1). Thus, any increase in the riskiness of large

24 In addition, local economic conditions affect demand for loans and other banking products, insuranceproducts, and brokerage services, as well as the supply of factors of production for FIs such as the cost of laborand capital and the price of deposits. Local regulations affect investment opportunities, product variety, and theproduct mix of FIs.


LICs is important ‘‘news’’ which is quickly propagated to large banks and brokeragefirms.

As noted in Carey and Stulz (2005), stronger risk linkages across large FIs could becaused by trends towards consolidation, conglomeration, and globalization in the USfinancial services industry. These trends have led to common sources of risk, as well asadoption of similar models for risk assessment and risk management across the threeindustries. The commonality in risk can come from a set of common features such as sim-ilar capital requirements, correlated derivative positions established to manage similarmarket-related risks, the frequent use of market-based financing vehicles such as the com-mercial paper and federal funds markets, and/or similar credit and liquidity risks amongthe largest FIs.25 In addition, the growing importance of large FI trading operations, mostnotably, the rapidly expanding counterparty exposures to hedge funds and other large FIs,has led to increased counterparty credit risk with a common set of customers.

In contrast, the lack of volatility spillover effects among the smaller FIs confirms theintuition and findings of Demsetz and Strahan (1995) and Demsetz and Strahan (1997)that these institutions typically assume more localized, idiosyncratic risk, possibly dueto less geographic and product diversification. By definition, idiosyncratic risk is specificto a particular firm and, thus, a smaller FI with a sizable amount of this type of risk willhave less in common with the risks of other FIs. Lack of correlation across smaller banks,brokers, and LIC volatilities leads to few, if any, risk spillover effects, though these firmsdo exhibit return-related linkages.

Overall, we can conclude that systemic risk spillover does exist across financial indus-tries but it does not originate from smaller FIs or is it likely to involve these firms. It isthe large FIs that are the conduit for volatility transmission across different financialindustries. The volatility transmission occurs rather quickly; typically over one day. Thus,it is these institutions that the regulators should focus on in order to avoid the possibilityof a financial system meltdown.

5.2.4. Effects of GLBA

The introduction of GLBA may have had significant effects on the level and volatility ofstock returns for both large and small FIs.26 In theory, it can be argued that the magnitudeof the GLBA effects will be different among the three types of FIs because brokerage firmsand LICs are smaller than banks and can challenge them to a lesser extent. It follows that,in the post-GLBA environment, greater inter-industry competition will affect the returnsand risks of the brokers and life insurers more heavily.

The bi6 and ui1 parameters in the model capture the effects of GLBA on the level andvolatility of stock returns, respectively. The coefficient estimates in Panels A–C of Table 2

25 For example, before GLBA, Section 20 affiliates of bank holding companies (BHCs) controlled more than20% of the corporate debt underwriting, exposing the parent BHCs to similar risks as those of securities firms.26 Allen and Jagtinai (2000) investigate the risk effects of combining banking, securities, and insurance activities.

They report that such combinations do reduce total risk but they also increase systematic risk, raising theprobability of a systemic collapse. The rationale for the effect of GLBA is that when brokerage firms andinsurance companies merge with banking companies, the takeover market discipline strengthens, and competitivepressures on all three types of FIs intensify. As noted in Hendershott et al. (2002), improved market discipline andincreased efficiency may also make these FIs more viable in terms of competition with foreign FIs. These factorscan affect both the returns and riskiness of these FIs. For earlier studies of the effect of combining BHCs withsecurities firms and LICs see Boyd and Graham (1988) and Boyd et al. (1993).


indicate that, interestingly, GLBA did not have a significant effect on the level of stockreturns for any of the FIs across all three size-related categories. This major regulatorychange does not appear to have created significantly positive, or negative, return opportu-nities for the US FIs over the horizon examined here.27 The risk effect is dissimilar acrossdifferent FI types; the riskiness of large and small banks was not, while those of brokersand LICs were, significantly affected by GLBA’s passage.

It is noteworthy that large BHCs already had Section 20 subsidiaries so that after theGLBA they became freer to operate them at their optimal scale without any regulatoryobstacles. As the results in Panels A and B of Table 2 show, the volatility of large brokersis lower while the volatility of smaller brokers is higher in the post-GLBA period. Thereverse holds for LICs (Table 2, Panels A and B). The effect of GLBA on the riskinessof these FIs suggests that the introduction of GLBA increased investors’ perceptions ofthe risks associated with smaller brokers and larger LICs, with the reverse holding for lar-ger brokers and smaller LICs. The smaller banks did not operate Section 20 subsidiariesbut they are not likely to engage in considerable securities activities even after the GLBA.Hence, the riskiness of neither large nor small banks is affected.

In the case of brokerage firms, GLBA’s impact indicates that the new regulations maymake it more difficult for smaller brokers to survive in the face of competition from thebigger financial service holding companies equipped with a department store-type of prod-uct variety, package pricing, and advertising network. In contrast, the primacy of stateinsurance regulations appears to benefit smaller LICs because these state-specific regula-tions enable smaller insurers to operate as state-regulated quasi-monopolists. The positiverelation between GLBA’s enactment and large LIC riskiness suggests these insurers arenow more vulnerable to competition from large banks and brokers, as well as entrenched,smaller LICs. In sum, the introduction of GLBA has had its most significant impact on theriskiness of non-banking FIs and these effects vary depending on the size and type of finan-cial institution.

6. Conclusions

We examine the risk and return linkages across US banks, securities firms, and LICsduring 1991–2001. We analyze these linkages for large and small firms separately. We findthat a multivariate GARCH model can adequately account for time-variation in the riskand return patterns and the inter-industry transmission of shocks among these three indus-tries. The main finding is that interdependencies do exist across FI types and that they aresize-sensitive. Larger institutions are found to display stronger risk-related transmission(volatility spillover) while smaller institutions exhibit more pronounced return-relatedlinkages with limited risk spillover. The significance of the spillover effects suggests thatinter-industry competition is in effect among banks, securities firms, and LICs and thatthese institutions are converging. In comparison, single-industry studies of European

27 Some event studies show that GLBA had positive effects on the three industries involved on the day that acompromise was reached between the White House and the Conferees over the Act (10/22/1999). The results hereare based on a comparison of the pre-/post-passage of the Act by the Senate (11/4/1999), close to two weeks afterthe compromise (Hendershott et al., 2002). Carow and Heron (2002) also report differing stock price reactions tothe Act among finance companies, investment banks, commercial banks, thrifts, and insurers. The difference inmethodologies between the above studies and that of ours make a direct comparison of coefficient values difficult.


FIs suggest that there has been some degree of integration within Europe but that it is con-centrated within specific sub-regions and financial products.

The volatility spillover results for smaller FIs are opposite to those for large FIs. Ourresults confirm the intuition, and prior empirical evidence, that smaller FIs typicallyassume more localized idiosyncratic risks, largely limiting the volatility spillover effectsfrom other FIs. Introduction of the Gramm-Leach-Bliley Act is found to have had no sig-nificant effects on commercial banks’ risk and returns, as well as dissimilar and size-sensi-tive effects on the riskiness of securities firms and LICs. These findings suggest that policymakers should take into account the differing impacts of a regulatory change on risk-tak-ing when such a change affects multiple types of financial service providers, and when thereis substantial cross-sectional variation in the size of these institutions.

Future research should explore the role of regional economic and regulatory factors inFI’s risk and return transmissions. Return spillover exhibited by smaller FIs, relative totheir larger counterparts, is most likely due to the stronger effects of local and regionaleconomic conditions. Studies have found that the state and regional level variables areimportant determinants of bank profitability (Samolyk, 1994; Berger et al., 2000). Similaranalysis can be performed for brokerage firms and LICs and linkages can be establishedfor risk and return transmission. In addition, the FI-specific determinants of the relativespeed of convergence across financial institutions would be an interesting avenue forfuture research.

Acknowledgements

This paper was presented at the Eastern Finance Association meeting of 2006 in Phil-adelphia. The authors wish to thank the discussant, Jiang Yang, for helpful comments andsuggestions. Thanks are due also to Wanli Zhao and Yan Hu for data extraction, and twoanonymous referees of the Journal for constructive comments and criticisms which haveimproved the quality of the paper. The first author gratefully acknowledges a summer re-search grant from Temple University.

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Convergence and risk-return linkages across financial service firms

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