RELATIONSHIPS AND UNDERWRITER SPREADS IN THE

EUROBOND FLOATING RATE NOTE MARKET +

Michael G Kollo

University of New South Wales

and

Ian G. Sharpe*

University of New South Wales

Journal of Economic Literature Classification Codes: G15 and G24.

Key words: Banking relationships, Underwriter spreads, Eurobonds

* Corresponding author: Professor Ian Sharpe

School of Banking and Finance,

University of New South Wales,

Sydney, NSW 2052, Australia.

Tel: 61-2-93855856,

Email: I.Sharpe@unsw.edu.au

+ The authors would like to thank Vince Hooper, Suk-Joong Kim, Gabriel Noti, Ronan

Powell, LiAnne Woo and participants at the 15th Australasian Finance and Banking

Conference 2002 and at research workshops at the University of Melbourne, University

of Technology- Sydney, and the University of New South Wales for helpful comments on

earlier drafts of the paper. The Australian Research Council provided financial support

for the project.

RELATIONSHIPS AND UNDERWRITER SPREADS IN THE

EUROBOND FLOATING RATE NOTE MARKET

ABSTRACT

We examine the determinants of underwriter spreads on Eurobond floating rate notes

issued by financial firms over the 1992-2002 period. Focusing on the role of the

issuer/underwriter relationship we find that there is a small though statistically significant

discount for issuers who switch underwriters and that the discount is not related to the

reputation of the lead underwriter. There is also strong evidence of currency clienteles in

underwriter spreads with spreads significantly higher on pound and Euro denominated

issues than USD issues. Spreads are also higher for non-investment grade issues and,

within investment grade issues, increase as quality declines.

RELATIONSHIPS AND UNDERWRITER SPREADS IN THE

EUROBOND FLOATING RATE MARKET

INTRODUCTION

The Eurobond market provides an important source of funding for financial

institutions, corporations, governments and supra-national agencies. Its growth has been

facilitated by the activities of domestic and supra-national financial firms in both

European and international markets as well as increasing globalisation, restrictive legal

environments within domestic markets, illiquid domestic debt markets, and barriers of

entry into domestic markets (Giddy (1995, p. 129)). An important component of the

market is the floating rate note, a medium term bond with an interest rate tied to a short-

term base rate, usually the London inter-bank overnight rate (LIBOR). The Eurobond

floating rate note market has experienced rapid growth with new issues increasing from

US$36 billion in 1992 to US$339 billion in 2000.

Despite the importance of Eurobond floating rate notes, little is known of the

determinants of the direct costs of issuance (that is of underwriter fees) in the market. Our

primary objective is to address this shortcoming by examining the determinants of

underwriter spreads on Eurobond floating rate notes issued by financial firms. Moreover,

we focus on business relationships between issuers and underwriters, and the effects of

switching underwriters and related ‘graduation effects’, on underwriter spreads.1

A study of underwriter spreads in the Eurobond floating rate note market is of

interest for several reasons. Relative to U.S. bond markets, the Eurobond market is a

market for high quality bearer bonds of relatively well-known, reputable issuers. The

1 ‘Graduation effects’ relate to when the issuer switches to a higher (graduates up) or a lower (graduates

down) reputation underwriter (Krigman et al (2001)).

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bonds are placed and underwritten by leading international banks with expertise in the

Eurobond market. Moreover, it is a relatively competitive wholesale market with few

barriers to entry and a low level of underwriter concentration.2 Given the stature and

reputation of Eurobond issuers, switching underwriters is likely to involve relatively

small costs for borrowers suggesting that an ongoing issuer/underwriter relationship

could be of less value in the international bond markets than domestic markets.

Second, US empirical studies of the effect of repeat business on underwriter fees

have found mixed results. Consistent with the asymmetric information models, James

(1992) finds evidence that underwriter spreads on U.S. seasoned public securities

offerings are smaller when the issuer uses the same underwriter as in its earlier initial

public offering than if it changes underwriter. However, subsequent studies of

underwriter spreads in U.S. equity and debt markets have generally found that where the

issuer utilises the same underwriter as in prior issues the underwriter spread is

significantly higher than if it switches underwriter (see Nanda and Warther (1998)).3

Third, Eurobond floating rate notes are denominated in a range of currencies that

give rise to foreign currency risk and the possibility of currency clienteles within the

underwriting market. A study of underwriter spreads in the Eurobond floating rate market

provides a unique opportunity to examine how the presence of currency clienteles may

impinge on the setting of underwriter fees.

Finally, prior U.S. studies of underwriting fees in the corporate bond market have

excluded financial firms from their samples. This reflects a concern that the underwriter

and issuer could share a common ownership structure, resulting in an underwriting fee

2 Smith and Walter (1997, p. 270) note that in 1994 in the U.S. “the 10 leading underwriters of corporate bonds and equity together managed approximately 83 per cent of the total market --- (whereas) in the Eurobond market --- the top 10 firms accounted for only 44.5 per cent.” 3 Saunders and Srinivasan (2001) find a similar relationship for merger fees charged by investment banks.

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that is not market determined. While our decision to focus on financial firms issuing into

the floating rate market is based on their dominant role in the market (see Section I), a

study of financial issuers is of interest because financial issuers are likely to have a

superior level of understanding of the workings of financial markets than non-financial

issuers. This suggests a lower level of information asymmetry between issuer and

underwriter when the issuer is a financial firm rather than a non-financial firm.

I THE EUROBOND FLOATING RATE NOTE MARKET

Eurobond floating rate notes are medium term, negotiable and transferable bearer

bonds that are usually listed on a stock exchange (primarily London or Luxembourg) and

rated by Moody’s and Standard & Poors. Most are issued without call provisions or

sinking funds, and they are generally “bought” deals where a small number of

underwriters, who offer the best cost of funds in the currency, purchase the entire issue.

The American fixed-price-offer method is used to price and distribute the issue, with the

issuer and lead underwriter negotiating a price and a “fixed” underwriting discount that is

non-discountable by the other underwriters (see Smith and Walter (1997, p. 263)).

An overview of the Eurobond floating rate note market for 1992-2002 is provided

in Table 1. Average maturity of the notes is typically around 6 years, while the gross

underwriting spread averages 37bps.4 There is evidence of increasing competitive

pressures in the market with the spread falling from around 65bps in 1992-1993 to 25bps

in 2000-2002. Smith and Walter (1997, p. 251) note that the primary issuers in the market

are the large banks, supranational institutions such as the World Bank and the European

Union, government agencies, and industrial corporations and their captive finance

4 Defined as the ratio of the gross fee charged by the underwriter including management fees, sales commissions, and other deal-specific fees charged by the underwriter to the gross proceeds of the issue.

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subsidiaries. However, it is evident from Table 1 that financial firms are the dominant

issuers with 71% of the market by dollar volume and 68% by number of issues.

The currency denomination of issues for the pre- and post- Euro periods is

examined in Table 2. The Euro has had a significant effect on the currency choice of

floating rate note issuers. Pre-Euro, 53% of the market volume was in USD, with a

further 25% in Pound Sterling and Deutchemark. However, post-Euro, 66% of the dollar

volume of floating rate note issues is denominated in Euros with only 22% in USD. An

important issue in our empirical analysis is the effect of the Euro on underwriter spreads.

II. SPECIFICATION OF THE MODEL

The dependent variable in the model, denoted LN(FEE) and referred to as the

underwriter spread, is the natural log of the combined or gross fees charged by the

underwriter for an issue expressed as a percent of the proceeds. It includes management

fees, sales commissions, and any other deal-specific fees charged by the underwriter5.

A. The Issuer/Underwriter Relationship

The effects of banking relationships in the pricing of financial services is

examined by DeAngelo (1981) who models the existence of ‘low-balling’ where

providers of financial services under-price services on initial business in order to earn

quasi rents on repeat transactions. She argues that the optimal pricing technique is one of

‘low-balling’ to win the initial deal, and then increasing fees for repeat transactions.6 In

an underwriting setting, this would result in higher underwriter spreads for repeat

business issuers, and lower fees for issuers that switch underwriters.

5 Livingston and Miller (2000) use a similar definition of the underwriter spread. . We use the gross Underwriter Fee as reported by SDC Platinum. 6 Nanda and Warther (1998) argue that underwriters do not share efficiency gains from repeat transactions, but rather use their monopolistic power to raise underwriter fees for repeat issuers. The argument is akin to the hold-up problem in banking (see Rajan (1992)).

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On the other hand, James (1992) notes that because underwriters invest in

relationship-specific assets that are durable and transaction-specific there is a ‘lock in’

effect that makes it costly to switch suppliers of services because of initial set-up costs.

The underwriter’s pricing strategy on repeat issues with the same issuer is then to extract

the maximum quasi rents by setting a spread such that the issuer is indifferent between

switching and staying with the same underwriter. In the absence of ‘low-balling’, the

existing underwriter will set a fee marginally below that of a competing underwriter who

has to incur the initial set-up costs. Issuers that stay with the same underwriter will then

pay lower underwriter fees than if they switch underwriters.

We use two alternative proxies for these relationship/agency cost influences. First,

we include an indicator variable, denoted SWITCH_UW, for where the issuer switches

underwriter. It takes the value of unity if: (i) the issuer made a Eurobond issue in the

three year period prior to the current issue; and (ii) if the underwriter on the prior issue

has a different parent to that of the underwriter of the current issue.7 Second, we construct

a proxy for the strength of the business relationship between the issuer and underwriter

over the three-year period preceding the issue. The leading underwriters in the Eurobond

market often operate through subsidiaries located in different countries to facilitate the

distribution of securities. Consequently, we define a relationship as one between a

borrowing firm and the consolidated underwriting entity, including the subsidiaries of the

underwriting parent firm. As our data source identifies the underwriter and its parent, we

are able to identify the proportion of a borrower’s Eurobond issues that have been

underwritten by the parent underwriting firm or any of its subsidiaries. Thus where the

issuer has made one or more non-equity linked fixed or floating rate Eurobond issues in

7 In the case of multiple lead underwriters, if any of the lead underwriters from the past issue are part of the lead underwriters for the current issue, SWITCH_UW takes the value of zero.

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the previous three years, the relationship strength proxy, denoted RELATIONSHIP_UW,

is defined as the proportion of those issues (in value terms) that were underwritten by an

underwriter with the same parent as the underwriter of the current issue. Where there

were no issues in the previous three years, the proxy takes the value of zero.8 With the

conflicting predictions of the relationship literature, the expected sign of the coefficient

of the relationship strength and switching underwriter variables is uncertain (though they

should have opposite signs).

We also include an indicator variable, NOT_ISSUED, for firms that have not

issued a non-equity linked Eurobond issue in the prior three-year period. As the level of

information asymmetry is likely to be less for recent issuers than non-issuers, and

information transparent issuers require less marketing and other underwriting services,

we expect a direct relationship between NOT_ISSUED and the underwriter spread.

B. Underwriter Reputation

Chemmanur and Fulghieri (1994) develop a game theoretic model in which an

underwriter provides certification and monitoring services to issuers and is confronted by

a dynamic tradeoff between setting strict standards in evaluating issuers to enhance its

reputation and setting lower standards to gain more business but at a cost to its reputation.

They show that more reputable underwriters provide superior certification and

monitoring services and charge higher fees than less reputable underwriters. Similarly,

Puri (1999) argues that high reputation underwriters charge higher fees to cover the

greater costs incurred in providing superior services than less reputable underwriters.9

8 In the case of multiple underwriters, the issue amount is evenly split between the lead underwriters to calculate relationships for each underwriter in the lead manager group. RELATIONSHIP_UW then takes the value of the average for the lead managers of the issue. Saunders and Srinivasan (2001) construct a similar measure for relationship strength in their model of the determinants of merger fees. 9 The US empirical literature suggests an inverse relationship between underwriter reputation and fees (see Carter and Manaster (1990), Roden and Bassler (1996) and Livingston and Miller (2000)).

6

U.S. studies have used two primary approaches to proxy underwriter reputation:

(i) Carter and Manaster (1990) use a ranking based on the underwriter’s hierarchical

bracket position in new issue tombstone announcements; and (ii) Megginson and Weiss

(1991) use the market share of the underwriter (in dollar terms) over a prior period. As

Megginson and Weiss (1991, p. 890) find a high positive correlation between the market

share proxy and the tombstone bracket ranking, and a tombstone ranking of underwriters

in the Eurobond market is not available, in this study we use the market share approach.

Consistent with the relationship variables, we develop a reputation proxy based on the

non-equity linked Eurobond market share of the consolidated underwriting group. Thus

UW_RANK is defined as the ranking of the underwriting group in the calendar year

preceding the issue.10 It takes the value of 20 for the top ranked underwriter, 19 for the

second ranked underwriter, --- and 0 for underwriters ranked 21 and lower.11 The theory

suggests a direct relationship between underwriter reputation and fees.

C. Currency Clienteles

Kim and Stulz (1988) define clientele effects as the set of preferences through

which investors match cash flows to their preferred tax or income structure. Thus,

investors’ preferences for varied cash flows are expected to influence the demand for

financial instruments. In the Eurobond market, Kim and Stulz (1988) find evidence of

arbitrage opportunities between USD denominated Eurobond issues and domestic bond

issues consistent with a clientele effect across the markets.

Currency clienteles within the Eurobond underwriting market may arise from

currency preferences, regulation of issuers and investors, home currency expertise of

10 In the case of multiple lead underwriters, the market share variable is the average of the market shares of the lead underwriters to the issue. 11 We also used the consolidated underwriting group’s share of the non-equity linked Eurobond market in the twelve months immediately preceding the issue date and an indicator variable for when the underwriter is ranked among the top five underwriters but the results were similar to those of the UW_RANK variable.

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underwriters, and restrictions on underwriting activities. Issuers are often drawn to the

Eurobond market by the prospect of lower borrowing costs and select the currency of

denomination on that basis. Currency swaps are then used to convert the currency

exposure to the preferred currency. This arbitrage activity is facilitated by the choice of

an underwriter who is able to efficiently manage and market the issue in that currency.

Moreover, domestic underwriters may benefit from having a better understanding of the

movements in domestic interest and exchange rates and/or from superior access to

investors wishing to invest in the domestic currency (see McCauley (1999, p. 6)). They

may also be protected from competition from foreign domiciled underwriters by

requirements that domestic banks be among the lead managers.12 Furthermore, prior to

the introduction of the Euro in January 1999, European institutional investors were often

restricted in their ability to invest in securities that were not denominated in their home

currency (Lannoo (1998, p. 318)). Hence currency preferences of institutional investors

are influenced by the currency denomination of their liabilities.

The introduction of the Euro has had a dramatic influence on currency preferences

in the Eurobond floating rate market market. With the home currency of the eleven

founding members of the European currency system becoming the Euro, the demand for

Euro denominated securities by European institutional investors increased significantly.

Moreover, the supply of Euro denominated issues increased as many European issuers

were able to issue into a more liquid Eurobond market in their home currency than was

previously possible. These shifts in the demand and supply for Eurobonds are also likely

to have enhanced the competitive position of European underwriters vis-à-vis U.S.

12 For example, lead managers of German mark denominated Eurobonds were required to be domiciled in Germany, though they could be German subsidiaries of non-German banks (see Fisher (1988, p. 134)). Regulations of this type forced non-domestic underwriters to choose between establishing German subsidiaries with the increased costs of generating the necessary marketing and research capability to operate effectively in the market or alternatively neglecting that sector of the Eurobond market.

8

underwriters. For these reasons we differentiate between pre- and post-Euro currency

clienteles in the analysis of underwriter spreads within the floating rate note market.

Currency clienteles are proxied by a set of indicator variables for the main

currencies. Prior to the Euro we identify five primary currencies, the US dollar, German

mark, British pound, Japanese yen, and a miscellaneous currency category. These are

denoted PRE_USD, PRE_DM, PRE_POUND, PRE_YEN, and PRE_MISC_CUR with

each taking the value of unity if the Eurobond is issued prior to the introduction of the

Euro and is denominated in that currency and zero otherwise. In the period since the

introduction of the Euro the primary currencies are the US dollar, Euro, British pound,

Japanese yen and a miscellaneous category, denoted POST_USD, POST_EURO,

POST_POUND, POST_YEN and POST_MISC_CUR respectively. To avoid singularity

the PRE_USD and POST_USD variables are excluded from the regressions.

D. Default Risk

Livingston and Miller (2000) note that the underwriter spread is the underwriter’s

compensation for absorbing risk in the distribution of securities. An important component

of that risk in bond markets is the risk of default, though the nature of the Eurobond

market is such that issuers generally carry a high credit profile to overcome the

information asymmetry barriers of issuing into the market. We proxy the borrower’s

default risk by indicator variables for Moody’s letter ratings,13 denoted MOODY_AAA,

MOODY_AA, MOODY_A, MOODY_BAA, MOODY_BA and MOODY_B. As the

AAA letter rating category is excluded from the regression to avoid singularity, the

coefficients of the five letter rating variables are expected to be positive and increasing in

magnitude as credit quality declines. To account for issues that are not rated we include

13 Jewell and Livingston (1998, p. 192) note that discussions with individuals in the ratings industry suggested that letter ratings are more important than sub-ratings in determining bond yields.

9

an indicator variable, denoted NOT_RATED, for issues that are not rated and zero

otherwise. Its coefficient has an uncertain sign.

While interest rate risk increases with the maturity of fixed rate bonds, the market

based adjustment of the coupon payable on a floating rate note largely eliminates interest

rate risk. However, a longer maturity floating rate bond could have a higher risk of

default as the issuer is obligated to maintain the variable coupon payments and repayment

of principal for a longer period (see Merton (1974)). Consequently we include the natural

logarithm of the maturity of the floating rate note, denoted LN(MATURITY), in the

model and expect it to be directly related to underwriter spreads.14

E. Control Variables

We also include a number of controls for issue size, for if the issuer and

underwriter have the same parent, and the year of issue. For scale related influences on

underwriter fees we include the log of issue size, denoted LN(ISSUE_SIZE), and in the

presence of scale economies expect an inverse relationship (see Nanda and Warther

(1998)). When the issuer and underwriter are part of the same organization and have a

common parent, the underwriting fee could reflect an element of transfer pricing.

Consequently we include an indicator variable, ISSUER_IS_UW, that takes the value of

unity if the issuer’s parent is the same as the underwriter’s parent, and zero otherwise.

The year of issue indicator variables are denoted DYEARt, and D1992 is excluded from

the regression to avoid singularity. With increasing competition in the market, and

declining underwriter spreads, the coefficients of the DYEARt indicator variables are

expected to be negative and to increase in absolute value over time.

14 Livingston and Zhou (2002, p. 16) note that the relationship between a bond’s maturity and the underwriting spread may not be linear. Moreover, using the natural log of maturity overcomes the skewness in the maturity variable.

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II. DATA

The data for the study, drawn from the SDC Database, is a sample of Eurobond

floating rate notes issued by financial firms over the 1992-2002 period.15 Some 6536

floating rate notes issued by financial firms between 1992-2002 were initially identified.

However, 1762 observations were excluded because of incomplete data relating to the

dependent and independent variables.16 This left a sample of 4773 Eurobond floating rate

note issues of 776 financial firms.

Table 3 provides summary statistics for the sample. The mean underwriter spread

is 29bps, with a median of 18bps, suggesting skewness in the data. The average issue size

is US$201m in constant 1995 dollars, and the average maturity is 7.5 years. The notes are

of high quality vis-à-vis U.S. domestic bond market studies with only 0.6% of the issues

below investment grade.17 The average market share for lead underwriters is 4% which is

suggestive of a more competitive market for underwriting services than the 11% mean

market share reported in the Roden and Mullineaux (2002) U.S. bond market study.

With respect to the issuer/underwriter relationship, approximately 74% of the

issuers had issued a non-equity linked Eurobond in the prior three year period. Moreover

the incidence of switching underwriters among this group is relatively high with 69% of

prior issuers switching underwriters. This contrasts with the findings in Krigman et al

(2001) that, in the mid-1990s, 30% of U.S. firms completing a seasoned equity offering

within three years of their initial public offer switched lead underwriter.

15 To be classified as a U.S. firm we require SDC to classify the domicile of the borrower, and if a subsidiary that of its parent, to be the U.S.. Financial firms are classified as firms with a SIC classification code beginning with ‘6’. 16 The incomplete data related to the underwriter fee in 1408 observations, ratings information on 200 observations, the maturity for 153 observations, and issue size for one observation. A further observation was excluded from the sample as SDC reported an implausible underwriter spread of 25%. This left a sample of 4773 Eurobond floating rate note issues of 776 financial firms. 17 Jewell and Livingston (1998) and Roden and Mullineaux (2002) report 22% and 18% of their bond samples as being below investment grade.

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III. RESULTS

As the underwriter fee variable is skewed we use the natural log of the

underwriter spread, denoted LN(FEE), as the dependent variable. In Table 4, REGs 1 and

2 differ in their treatment of the issuer/underwriter relationship. In REG 1 relationship

strength is proxied by the switching underwriter indicator variable, SWITCH_UW, while

in REG 2 it is proxied by RELATIONSHIP_UW, the proportion of the issuer’s Eurobond

issues over the prior three years that were underwritten by the current underwriter.

A potential problem in OLS estimates involving the dichotomous switching

underwriter indicator variable, SWITCH_UW, is the possibility of selection bias. The

specification of the sample selection is known as the “treatment effects model” and

applies when the indicator of the presence or otherwise of the sample selection is itself a

determinant of underwriter spreads (see Green (1995, pp.642-643) and Saunders and

Srinivasan (2001)). In our model, underwriter spreads are determined by the costs and

benefits of switching underwriters. As these costs and benefits are not directly

observable, they are proxied in REG 1 by the observed indicator variable, SWITCH_UW.

If the observed underwriter switching behaviour is not randomly sampled from the

population of the costs and benefits of switching, then serious biases may result.

The solution to the selection problem is to use Heckman’s (1979) two-stage

maximum likelihood estimation technique (with the LIMDEP software). The first stage

involves a probit model with the SWITCH_UW variable as dependent variable and a set

of independent variables reflecting the benefits/costs of switching underwriters. 18 The

estimate of the probability of switching underwriters from the probit regression, the

inverse Mills ratio or ‘LAMBDA’, then replaces the SWITCH_UW variable in the

18 These include the maturity, size, year, the credit risk and domicile of the issuer, the reputation and domicile of the underwriter, the strength of the relationship between issuer and underwriter, and indicator variables for where the issuer and underwriter have a common parent and common nationalities.

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second stage maximum likelihood estimation of the spread equation. Heckman’s

estimator of the treatment effects model with underwriter switching are reported in REG

1 while OLS is used for the estimates with RELATIONSHIP_UW in REG 2.

A. The Issuer/Underwriter Relationship and Underwriter Reputation

The regression coefficients of the issuer/underwriter relationship proxies in REGs

1 and 2, SWITCH_UW and RELATIONSHIP_UW, are statistically significant and, as

expected, opposite in sign. Where there is a strong issuer/underwriter relationship, or

where the issuer does not switch underwriters, the underwriter charges a significantly

higher spread. These results mirror those of Nanda and Warther (1998) and Saunders and

Srinivasan (2001) for U.S. underwriting and merger fees respectively and are consistent

with DeAngelo’s (1981) model of ‘low-balling’ where providers of financial services

under-price their services on initial business in order to earn quasi rents on subsequent

business. Alternatively, Nanda and Warther (1998) explain the result in terms of

underwriters not sharing efficiency gains from repeat transactions, but rather using their

monopolistic power to raise fees for repeat issuers.19 There is little support for James’

(1992) model in which underwriters invest in relationship-specific assets that, because of

high initial set-up costs, lead underwriters to charge higher fees on new business.

REGs 3, 4 and 5 in Table 4 examine whether the sensitivity of underwriter

spreads to switching underwriters depends on the reputation of the underwriter. In Reg 3

we introduce an interactive term involving the inverse Mills ratio, LAMBDA, and the

underwriter’s league table ranking, UW_RANK. However, its coefficient is not

significant. Regs 4 and 5 then explore the hypothesis that the switching discount depends

on whether the switching is to a more or less reputable underwriter. In REG 4 we include

the interactive term between LAMBDA and the difference of underwriter ranking 19 The argument is akin to the hold-up problem in banking (see Rajan (1992)).

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between the current underwriter and that on the prior issue, ∆UW_RANK. Again the

interactive variable is insignificant. Finally in Reg 5 we create indicator variables for

whether the switching involves a movement from a top tier to a non-top tier underwriter,

denoted FROM_TOP_TIER, from a non-top tier to a top tier underwriter, denoted

TO_TOP_TIER, or between underwriters in the same category, SAME_TIER. Each is

included interactively with LAMBDA, but to avoid singularity the switching within the

same category is excluded from the regression. While the coefficient of the switching

LAMBDA term remains negative and statistically significant, the interactive terms are

insignificant. Thus our results from Regs 3, 4 and 5 suggest that the underwriter

switching discount is unrelated to underwriter reputation. That is, there is no evidence of

a graduation effect in underwriter fees associated with switching to more or less reputable

underwriters. This contrasts with Saunders and Srinivasan (2001) who find, in merger

fees, that the switching discount is greater when switching to a less reputable underwriter.

It is also noteworthy that the underwriter reputation proxy, UW_RANK, when

introduced individually or interactively with the underwriter switching variables in Table

4 does not attain statistical significance in any of the regressions. Unlike Livingston and

Miller’s (2000) U.S. bond market study, we find little evidence that underwriter

reputation is a significant factor in the setting of underwriter fees in the Eurobond

floating rate note market. This is consistent with the Eurobond market being dominated

by high quality borrowers who place less value on underwriter reputation in the

marketing of their issues.

B. Currency Clienteles

The results in Table 4 are consistent with the existence of currency clienteles in

the Eurobond underwriting market, both pre and post the introduction of the Euro. Pre-

Euro, underwriter spreads on POUND, YEN and DM denominated issues had

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significantly lower underwriter spreads, while miscellaneous currencies had significantly

higher spreads, than USD denominated issues. Post-Euro, underwriter spreads on USD

denominated issues have been significantly higher than on POUND or EURO issues, but

lower than those in YEN or the miscellaneous currencies. The advent of the Euro appears

to have provided (the mostly) European issuers of EURO denominated floating rate notes

the lower level of underwriting fees that were previously only available to issuers of

POUND denominated notes.

C. Default Risk

Consistent with higher risk issues involving greater risk for the underwriter and

therefore requiring greater marketing effort, we find a direct relationship between credit

risk and underwriter fees. The rating indicator variables are significantly positive and

increase in magnitude as credit quality declines. While U.S. domestic bond market

studies also find that underwriter fees are higher on higher risk issues, this effect is

typically observed only for non-investment grade issues (see Livingston and Zhou (2002,

p. 25)). For Eurobond floating rate notes, however, spreads on the investment grades AA,

A and BAA are significantly higher than on AAA rated issues. Moreover, the coefficient

of the indicator variable for unrated issues is significantly positive and comparable in

magnitude to the coefficient of A and BAA rated issues. This suggests that the unrated

issues are generally of investment grade.

The log of the note’s maturity also has a highly significant positive coefficient

consistent with underwriter spreads increasing with maturity. As the floating rate feature

largely eliminates interest rate risk, the relationship between maturity and underwriting

spread may reflect either investor preferences for shorter maturity securities and/or

greater default risk at longer maturities.

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D. Controls

The scale variable, the natural log of issue size, has a significant negative

coefficient in all forms of the model consistent with the presence of scale economies in

the underwriting of floating rate notes. Recent studies of underwriter fees on US domestic

debt issues have found mixed evidence of scale economies in underwriter spreads with

Jewell and Livingston (1998), and Livingston and Zhou (2002) not finding significant

scale effects while Livingston and Miller (2000) find significant scale economies.

The regression controls for when the issuer and underwriter share the same parent

by including the indicator variable, ISSUER_IS_UW. It has a positive and statistically

significant coefficient, suggesting the possibility of transfer pricing or alternatively the

existence of internal controls limiting access of the borrowing subsidiary to more

competitive sources of underwriting services. Finally, the year indicator coefficients

show a gradual and statistically significant decline in underwriter fees. This may reflect

increased liquidity and/or competition in the Eurobond underwriting market.

E. Economic Significance

To evaluate the economic significance of the regression coefficients, we consider

a base case of a AAA rated US dollar floating rate note of $201m issued in 1992 where

the issuer switches underwriter, the underwriter has a mean rank of 10.5 and the issuer

and underwriter do not share the same parent. For the base case the model predicts an

underwriter spread of 29bps. Relative to the base case, a firm that has not issued in the

Eurobond market in the prior three-year period pays a 4bps higher underwriter spread

while a repeat issuer pays a 2bps higher spread. For an average issue size of US$201m a

2bps switching discount implies a fee saving of only US$40,000. Thus although the

existence of an ongoing issuer/underwriter relationship has a statistically significant

effect on underwriter spreads, the economic effect is small.

16

The currency denomination and credit rating have a greater economic impact on

underwriter spreads than the relationship proxies. Prior to the Euro and relative to a USD

issue, POUND issues have 16bps lower spreads (a saving of US$320,000 or 55% of the

base case underwriter fee), YEN issues have 6bps lower spreads, and DM issues have 2

bps lower spreads. Similar results are found post-Euro with POUND and EURO issues

having 18 and 16bps lower spreads respectively than USD issues. With respect to credit

rating, an investment grade BAA rated bond will have a 16bps higher spread than AAA

rated issues, an increase of US$320,000 relative to the base fee for a AAA rated firm.

When the issuer is not investment grade the spread is 66 and 85bps higher for BA and B

rated firms than for AAA issues. These impacts on spreads are both statistically and

economically significant.

For the maturity variable we examine the difference in underwriter spread for

maturities corresponding to the 25th and 75th percentiles of the maturity distribution, that

is 3 and 10 years respectively. The underwriter spread on the 10 year floating rate note is

approximately 18bps higher than for the 3 year note, suggesting that maturity has a

significant economic impact on the spread.

With respect to the controls we focus on two effects, scale economies and the

time trend. For the former we examine the difference in spread for an issue size

corresponding to the 25th percentile or US$53m and the 75th percentile or US$264m.

While the scale economy effect is highly statistically significant, its effect is relatively

small in economic terms. Thus a typical issue by the larger firm will have an underwriter

spread of only 2.5bps lower than that of the smaller firm. For the time trend, relative to

the base 1992 issue, a 2002 floating rate note issue has an 18bps lower spread or a saving

of US$380,000. Much of this saving, 13 bps, occurred prior to 1999 with only 5bps

occurring after the introduction of the Euro.

17

IV. SUMMARY

This paper examines the determinants of underwriter spreads on a sample of

Eurobond floating rate notes of financial firms over the 1992-2002 period. There are four

main findings. First, we find issuers in the Eurobond market frequently switch

underwriters with 69% of issuers using a different lead underwriter than in their prior

issue. Moreover, issuers that switch underwriters receive a statistically significant

discount in underwriter spread. While this discount is statistically significant, it is small

in economic terms (only 2bps). This is not surprising given that issuers in the Eurobond

market are generally large well-known international firms with small underwriter

switching costs.

Second, we find little evidence of graduation effects in underwriter spreads. That

is, the switching discount in underwriter spreads is independent of the underwriter’s

reputation in the current and prior issue. This conclusion differs from Saunders and

Srinivasan (2001) who find that lower reputation underwriters provide significantly larger

discounts on merger fees than top tier banks.

Third, we find evidence of significant currency clienteles in underwriter spreads,

with USD denominated issues generally attracting higher underwriter fees than other

major currencies. The currency clientele effects are different pre and post the introduction

of the Euro in January 1999. In the post-Euro period, underwriter spreads are

significantly less on EURO and POUND denominated issues than on USD and YEN

issues. These clientele effects are both economically and statistically significant.

Fourth, we find an economically and statistically significant relationship between

credit risk and underwriting fees, with higher rated issues and shorter maturity notes

attracting lower underwriting fees. As in U.S. bond market studies, underwriter spreads

for below investment grade issues are significantly higher than for investment grade

18

issues. An important difference in the pricing of default risk in underwriter spreads,

however, is within investment grade issues. Whereas the US domestic literature observes

little difference in underwriting fees within investment grade issues, the underwriting fees

increase at each bond letter rating in the Eurobond floating rate note market.

There are several possible extensions of the research reported in this paper. First,

while we document a relatively high level of switching underwriters in the Eurobond

floating rate note market, there has been no research into the determinants of that

switching behaviour. Although Krigman et al (2001) examine underwriter switching for

U.S. seasoned equity issues, a study of underwriter switching in international debt

markets in the presence of underwriting currency clienteles would be of particular

interest. Second, with Eurobond floating rate note underwriter spreads declining over the

1992-2002 period, a study of underwriter concentration in the market may shed some

light on the determinants of the declining spreads. Finally, with maturity and currency

denomination being significant determinants of underwriter spreads on Eurobond floating

rate notes, future studies may focus on how issuers choose maturity and currency

denomination in this market.

19

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21

Table 1. Market Size, Maturity and Gross Underwriting Spread in the Eurobond Floating Rate Note Market (1992 - 2002) Total Floating Rate Note Market Financial Issuers

Year Number of Issues

Proceeds (USD $mil)

Mean Maturity

Mean Gross Underwriting

Spread

Number of Issues

Proceeds (USD $mil)

Mean Maturity

Mean Gross Underwriting

Spread 1992 268 $36,482 6.758 0.639% 141 $18,414 7.18 0.772%1993

479 $63,185 7.034 0.682% 251 $34,892 8.30 0.776%1994 639 $97,104 6.548 0.585% 391 $61,374 7.65 0.645%1995 617 $59,315 6.349 0.454% 389 $43,489 6.93 0.484%1996 910 $104,376 6.533 0.340% 614 $79,987 6.96 0.339%1997 1072 $138,471 7.290 0.392% 733 $102,484 7.57 0.423%1998 863 $127,550 9.075 0.438% 627 $99,006 9.85 0.435%1999 1042 $291,480 7.894 0.365% 748 $207,341 8.24 0.368%2000 1325 $339,142 6.479 0.224% 975 $240,867 7.00 0.226%2001 1407 $300,687 6.381 0.289% 1016 $219,184 6.78 0.323%2002 965 $185,147 3.602 0.247% 651 $123,153 3.26 0.247%Total 9587 $1,742,938 6.690 0.372% 6536 $1,230,191 7.156 0.385%

Note: This table shows the market size, the mean maturity and mean gross underwriter spread in the Eurobond floating rate note market (excluding equity-linked issues) for 1992-2002. The data is also disaggregated for financial issuers, defined as issuers with ultimate parents with an SIC code starting with ‘6’. All information is sourced from SDC Platinum.

22

Table 2. Currency of Denomination in the Eurobond Floating Rate Note Market (1992-2002)

Pre-Euro (1992 - 1998) Post-Euro (1999-2002)

Currency of Denomination

$USDm Total

Volume in Market

Market Share by Volume

$USDm Volume by Financial Issuers1

Currency of Denomination

$USDm Total

Volume in Market

Market Share by Volume

$USDm Volume by Financial Issuers1

U.S. Dollar $330,570.3 52.77% $236,353.4 Euro $737,547.4 66.06% $506,574.6Pound Sterling $85,809.2 13.70% $74,377.2 U.S. Dollar $248,076.4 22.22% $187,244.2Deutchemark $72,202.9 11.53% $45,312.5 Pound Sterling $96,860.6 8.68% $77,373.4Japanese Yen $53,755.0 8.58% $32,246.0 Japanese Yen $23,568.0 2.11% $12,065.2ECU $23,784.8 3.80% $16,502.0 Hong Kong Dollar $7,108.7 0.64% $5,956.4Italian Lira $23,539.4 3.76% $9,083.3 Czech Koruna $789.4 0.07% $249.6French Franks $23,017.7 3.67% $16,040.1 Australian Dollar $619.5 0.06% $501.8Portuguese Escudo $4,029.8 0.64% $2,844.5 Canadian Dollar $496.0 0.04% $152.9Hong Kong Dollar $2,939.6 0.47% $2,137.5 Swedish Krona $293.0 0.03% $18.6Canadian Dollar $2,396.3 0.38% $1,394.0 Norwegian Krone $269.7 0.02% $5.6Dutch Guilder $999.0 0.16% $449.1 Slovak Koruna $138.5 0.01% $0.0Australian Dollar $983.5 0.16% $792.6 Slovak Crown $134.3 0.01% $0.0Irish Punt $640.7 0.10% $640.7 Swiss Franc $123.4 0.01% $18.4Belgian Franc $539.9 0.09% $539.9 South African Rand $115.8 0.01% $115.8Finnish Markka $401.1 0.06% $401.1 Polish Zloty $85.7 0.01% $85.7Danish Krone $271.2 0.04% $271.2 New Zealand Dollar $76.3 0.01% $76.3Swedish Krona $205.0 0.03% $110.9 Greek Drachma $67.5 0.01% $67.5Taiwanese Dollar $106.2 0.02% $0.0 Danish Krone $57.1 0.01% $39.5Singapore Dollar $102.0 0.02% $0.0 Singapore Dollar $28.3 0.00% $0.0New Zealand Dollar $79.1 0.01% $79.1 Other $0.0 0.00% $0.0Other $109.8 0.02% $70.8 Note: This table shows the top twenty currencies of denomination for floating rate notes (excluding equity-linked issues) in the Eurobond market ranked by $USD dollar volume pre and post the introduction of the Euro. The data is also disaggregated for financial issuers, defined as issuers with ultimate parents with an SIC code starting with ‘6’. All information is sourced from SDC Platinum.

23

Table 3. Model Summary Statistics Mean Median Std. Dev Min Max Dependent Variable

FEE 0.288 0.180 0.366 0.004 5.000 LN(FEE) -1.672 -1.715 0.886 -5.522 1.609

Banking Relationship RELATIONSHIP_UW 0.231 0.045 0.340 0.000 1.000 SWITCH_UW 0.513 1.000 0.500 0.000 1.000 NOT_ISSUED 0.257 0.000 0.437 0.000 1.000

Underwriter Reputation UW_RANK 10.52 5.50 6.48 0.00 20.00 UW_TOP_TIER 0.28 0.00 0.45 0.00 1.00

Currency of Denomination USD 0.351 0.000 0.477 0.000 1.000 PRE_DM 0.046 0.000 0.209 0.000 1.000 PRE_MISC_CUR 0.056 0.000 0.230 0.000 1.000 PRE_POUND 0.065 0.000 0.246 0.000 1.000 PRE_YEN 0.085 0.000 0.278 0.000 1.000 POST_EURO 0.311 0.000 0.463 0.000 1.000 POST_MISC_CUR 0.008 0.000 0.089 0.000 1.000 POST_POUND 0.067 0.000 0.249 0.000 1.000 POST_YEN 0.012 0.000 0.111 0.000 1.000

Default Risk MOODY_AAA 0.181 0.000 0.385 0.000 1.000 MOODY_AA 0.304 0.000 0.460 0.000 1.000 MOODY_A 0.297 0.000 0.457 0.000 1.000 MOODY_BAA 0.044 0.000 0.206 0.000 1.000 MOODY_BA 0.005 0.000 0.072 0.000 1.000 MOODY_B 0.001 0.000 0.029 0.000 1.000 NOT_RATED 0.167 0.000 0.373 0.000 1.000 MATURITY 7.508 5.041 8.343 0.589 59.036 LN(MATURITY) 1.591 1.618 0.894 -0.529 4.078

Controls ISSUER_IS_UW 0.086 0.000 0.280 0.000 1.000 ISSUE_SIZE 201.0 149.6 218.9 0.273 2384.5

Note: The Table shows summary statistics for the N=4773 sample. FEE is the gross underwriter spread, RELATIONSHIP_UW is a measure of relationship strength between issuer and underwriter, SWITCH_UW is an indicator variable for issuers that switch underwriters, NOT_ISSUED is an indicator variable for infrequent issuers in the market, UW_RANK and UW_TOP_TIER are measures of underwriter reputation, ∆(UW_RANK) is the difference in league ranking of the current and prior underwriters, PRE_DM, PRE_POUND, PRE_YEN, PRE_MISC_CUR, POST_EURO, POST_POUND, POST_YEN and POST_MISC_CUR are indicator variables for the currency of denomination for issues pre and post the introduction of the Euro, MOODY_AAA, MOODY_AA, MOODY_A, MOODY_BAA, MOODY_BA, MOODY_B and NOT_RATED are indicator variables for the Moody’s rating, MATURITY is the time to maturity. ISSUER_IS_UW is an indicator variable for when the issuer’s parent is the same as the underwriter’s parent, and ISSUE_SIZE is the real issue size. Source: SDC Platinum.

24

Table 4. Regression Estimates of the Determinants of Underwriter Spreads in the Eurobond Floating Rate Note Market. REG 1

Heckman’s Two Stage Least Squares

REG 2 Ordinary Least

Squares

REG 3 Heckman’s Two Stage

Least Squares

REG 4 Heckman’s Two Stage

Least Squares

REG 5 Heckman’s Two Stage

Least Squares INDEPENDENT VARIABLES Coeff. T-stat Coeff. T-stat Coeff. T-stat Coeff. T-stat Coeff. T-stat

INTERCEPT -1.750 (-16.57)*** -1.807 (-17.01)*** -1.751 (-16.59)*** -1.750 (-16.57)*** -1.753 (-16.58)***Banking Relationship

LAMBDA (λ) -0.054 (-3.92)*** -0.041

(-1.67)* -0.058 (-4.01)*** -0.060 (-3.97)***

RELATIONSHIP_UW 0.203 (5.39)***LAMBDA*UW_RANK -0.001 (-0.64)LAMBDA*D(UW_RANK) 0.002 (0.92)LAMBDA*FROM_TOP_TIER 0.027 (0.74)LAMBDA*TO_TOP_TIER

0.041 (0.77)

NOT_ISSUED 0.062 (2.57)** 0.122 (5.26)*** 0.061 (2.56)** 0.066 (2.71)*** 0.065 (2.68)***Underwriter Reputation

UW_RANK -0.001 (-0.54) -0.002

(-1.11) -0.001 (-0.55) -0.001 (-0.63) -0.001 (-0.51)Currency Clienteles

PRE_DM -0.083 (-1.85)* -0.071 (-1.57) -0.084 (-1.87)* -0.083 (-1.84)* -0.082 (-1.82)*PRE_MISC_CUR

0.093 (2.25)** 0.085 (2.05)** 0.092 (2.22)** 0.093 (2.24)** 0.094 (2.27)**

PRE_POUND

-0.835 (-9.04)*** -0.802 (-8.65)*** -0.835 (-9.04)*** -0.834 (-9.03)*** -0.834 (-9.03)***PRE_YEN -0.245 (-5.78)*** -0.223 (-5.23)*** -0.245 (-5.78)*** -0.245 (-5.77)*** -0.244 (-5.76)***POST_EURO -0.845 (-9.29)*** -0.814 (-8.92)*** -0.845 (-9.29)*** -0.844 (-9.28)*** -0.846 (-9.29)***POST_MISC_CUR

0.423 (4.82)*** 0.402 (4.57)*** 0.421 (4.79)*** 0.424 (4.82)*** 0.426 (4.84)***

POST_POUND

-1.036 (-10.84)*** -1.004 (-10.48)*** -1.037 (-10.85)*** -1.035 (-10.82)*** -1.036 (-10.84)***POST_YEN 0.054 (1.73)* 0.056 (1.78)* 0.054 (1.71)* 0.055 (1.74)* 0.055 (1.75)*

Credit Risk MOODY_AA 0.060 (2.04)** 0.052 (1.75)* 0.061 (2.05)** 0.061 (2.05)** 0.060 (2.04)**MOODY_A 0.149 (5.13)*** 0.134 (4.57)*** 0.150 (5.14)*** 0.151 (5.16)*** 0.149 (5.11)***MOODY_BAA 0.449 (8.98)*** 0.444 (8.88)*** 0.449 (8.99)*** 0.450 (9.00)*** 0.447 (8.94)***MOODY_BA 1.197 (9.15)*** 1.198 (9.16)*** 1.196 (9.15)*** 1.197 (9.15)*** 1.197 (9.15)***MOODY_B 1.378 (4.27)*** 1.369 (4.24)*** 1.386 (4.29)*** 1.364 (4.22)*** 1.365 (4.22)***NOT_RATED 0.291 (8.78)*** 0.286 (8.63)*** 0.291 (8.79)*** 0.291 (8.79)*** 0.290 (8.76)***LN(MATURITY) 0.498 (41.01)*** 0.492 (40.51)*** 0.498 (41.05)*** 0.497 (40.98)*** 0.498 (41.01)***

25

26

Table 4 (Continued.) REG 1

Heckman’s Two Stage Least Square

REG 2 Ordinary Least

Squares

REG 3 Heckman’s Two Stage

Least Square

REG 4 Heckman’s Two Stage

Least Square

REG 5 Heckman’s Two Stage

Least Square INDEPENDENT VARIABLES Coeff. T-stat Coeff. T-stat Coeff. T-stat Coeff. T-stat Coeff. T-stat

Controls ISSUER_IS_UW

0.131 (3.84)*** 0.109 (3.16)*** 0.130 (3.81)*** 0.131 (3.83)*** 0.132 (3.86)***LN(ISSUE_SIZE)

-0.054 (-6.20)***

-0.051 (-5.84)***

-0.054 (-6.17)***

-0.054 (-6.18)***

-0.055 (-6.23)***

D1993 0.131 (1.35) 0.136 (1.40) 0.131 (1.35) 0.131 (1.34) 0.131 (1.35)D1994 -0.233 (-2.46)** -0.226 (-2.39)** -0.233 (-2.46)** -0.234 (-2.47)** -0.232 (-2.46)**D1995 -0.444 (-4.79)*** -0.436 (-4.70)*** -0.444 (-4.79)*** -0.444 (-4.79)*** -0.444 (-4.78)***D1996 -0.562 (-6.25)*** -0.553 (-6.16)*** -0.562 (-6.26)*** -0.562 (-6.25)*** -0.562 (-6.25)***D1997 -0.546 (-6.09)*** -0.536 (-5.97)*** -0.547 (-6.10)*** -0.546 (-6.08)*** -0.544 (-6.06)***D1998 -0.593 (-6.59)*** -0.560 (-6.23)*** -0.594 (-6.61)*** -0.591 (-6.57)*** -0.591 (-6.57)***D1999 -0.629 (-6.87)*** -0.604 (-6.58)*** -0.629 (-6.87)*** -0.628 (-6.86)*** -0.629 (-6.86)***D2000 -0.845 (-9.29)*** -0.814 (-8.92)*** -0.845 (-9.29)*** -0.844 (-9.28)*** -0.846 (-9.29)***D2001 -0.835 (-9.04)*** -0.802 (-8.65)*** -0.835 (-9.04)*** -0.834 (-9.03)*** -0.834 (-9.03)***D2002 -1.036 (-10.84)*** -1.004 (-10.48)*** -1.037 (-10.85)*** -1.035 (-10.82)*** -1.036 (-10.84)***

Summary Statistics Adjusted R2 47.55% 47.70% 47.54% 47.55% 47.54%No. Obs 4773 4773 4773 4773 4773

Note: This table shows OLS and Heckman’s two stage regression results for the sample of all non-equity linked floating rate notes issued between 1992 and 2002 in the Eurobond market. The dependent variable is LN(FEE), the natural log of the gross underwriter spread. LAMBDA (λ) is the inverse Mills ratio estimated using Heckman’s two stage least squares with SWITCH_UW, the indicator variable for issuers that switch underwriters, as the treatments effects variable. RELATIONSHIP_UW is a measure of the relationship strength between issuer and underwriter, NOT_ISSUED is an indicator variable for infrequent issuers, UW_RANK is the underwriter’s reputation ranking in the Eurobond market, ∆(UW_RANK) is the difference in league ranking of the current and previous underwriter, FROM_TOP_TIER and TO_TOP_TIER are indicator variables for where the issuer has switched from a more to a less and from a less to a more reputable underwriter respectively, PRE_DM, PRE_POUND, PRE_YEN, PRE_MISC_CUR, POST_EURO, POST_POUND, POST_YEN and POST_MISC_CUR are indicator variables for the currency of denomination for issues pre and post the introduction of the Euro, MOODY_AA, MOODY_A, MOODY_BAA, MOODY_BA, MOODY_B and NOT_RATED are indicator variables for the Moody’s rating, LN(MATURITY) is the natural log of the time to maturity, ISSUER_IS_UW is an indicator variable for when the issuer and underwriter have the same parent, LN(ISSUE_SIZE) is the natural log of real issue size, and DYEARt are the indicator variables for the year of the issue. The t-values are given in parentheses and ‘***’,’**’,’*’ denote significance at the 99%, 95% and 90% confidence intervals respectively. Source: SDC Platinum.