New Bank Fees Wachovia

An investigation of underwriting fees for asset-backed securities.

I. Introduction

One of the largest financial markets is the market for asset-backed
securities (
ABS

). Despite the growth and size of the market, no research
has been done, to our knowledge, on
underwriting

 fees in this market.
The topic of underwriting fees has been previously examined in debt
markets. For example,
Livingston
 family of American statesmen, diplomats, and jurists.
Robert R. Livingston (1654–1728)

Robert R. Livingston, 1654–1728, b.
 and Miller (2000) find an
inverse
relationship

 between
underwriter
 n. a company or person which/who underwrites an insurance policy, issue of corporate securities, business, or project. (See: underwrite)


UNDERWRITER, insurances. One who signs a policy of insurance, by which he becomes an insurer.
 prestige (market share) and
underwriting fees charged in corporate debt markets.
Burch

, Nanda and
Warther (2004) find that loyalty (repeat business) leads to lower fees
for common stock offers, but found the opposite holds true for debt
offers. In this paper, we investigate whether the relations between
underwriter prestige and underwriting fees and between loyalty and
underwriting fees that have been found to exist in other debt markets
exist in the ABS market.

The topic of underwriting fees for ABS is of interest for two main
reasons. First, ABS is a relatively new financial market leading to
relatively little prior research being done on it. Second, at least part
of the financial crisis in 2009 has
arguably
  
adj.
1. Open to argument:

2. That can be argued plausibly; defensible in argument:
 been attributed to the
growth in the market of these types of securities. To better understand
ABS, may help better understand potentially one of the influencing
factors of the financial crisis.

ABS are inherently different from other debt instruments since they
are collateralized by specific
receivables

. The growth of the ABS market
can be attributed to two main factors: demand by investors in search of
spread (above “safer” fixed income securities such as
government and corporate debt) and supply by lenders wishing to
offload

 receivables. Using a proprietary database from Bloomberg LP this paper
provides an overview of the ABS market that is more detailed than the
existing literature. Further, using a methodology similar to that used
in Livingston and Miller (2000), this study explores the relationship
between ABS underwriter prestige and underwriting fees. This study
presents two key findings. First, the relation between underwriter
prestige and underwriter fees is found to be positive and statistically
significant, indicating that more prestigious underwriters charge higher
fees. Second, the analysis identifies a positive relation between
underwriter fees and loyalty, indicating that the more an issuer uses
the same underwriter, the higher the fees that are charged.

The rest of this paper is organized as follows. Section II provides
an overview of the market for ABS. Section III provide a literature
review and hypothesis
formulation
 /for·mu·la·tion/ () the act or product of formulating.


American Law Institute Formulation
. Section IV describes the methodology,
Section V describes the results, and Section VI concludes.

II. Overview of the Market for ABS

In this section, we first provide an overview of the market for
ABS, and then take a more detailed look at ABS
collateral
 , something of value given or pledged as security for payment of a loan. Collateral consists usually of financial instruments, such as stocks, bonds, and negotiable paper, rather than physical goods, although
 types,
underwriters, ratings and weighted average life. The source of all data
is the Bloomberg Data License product. All non-private placement U.S.
asset-backed securities issued from
January
 see month.
 1st. 1999 through
December
 see month.
 31, 2006 are included in our analysis. The period selected represent a
period in which the data are richest and most complete, and spans a
number of economic cycles.

Since its inception in 1985 with First Boston’s sale of
lease-backed notes by
Sperry

 Lease Finance Corporation, the U.S. ABS
market has grown considerably (van
Eck
   , Johann 1486-1543.

German Roman Catholic theologian who opposed the reforms of Martin Luther and procured from Rome the papal bull that declared Luther a heretic (1520).

Noun 1.
 1995). In addition to the
sizeable U.S. market, there are markets in
Europe
 , 6th largest continent, c.4,000,000 sq mi (10,360,000 sq km) including adjacent islands (1992 est. pop. 512,000,000).
 and Japan. Growth in
ABS extends to other markets such as
Korea
 , Korean Hanguk or Choson, region and historic country (85,049 sq mi/220,277 sq km), E Asia.
,
Taiwan
 , Portuguese Formosa, officially Republic of China, island nation (2005 est. pop. 22,894,000), 13,885 sq mi (35,961 sq km), in the Pacific Ocean, separated from the mainland of S China by the 100-mi-wide (161-km) Taiwan
, and
Greece
 Gr. Hellas or Ellas, republic (2005 est. pop. 10,668,000), 50,944 sq mi (131,945 sq km), SE Europe. It occupies the southernmost part of the Balkan Peninsula and borders on the Ionian Sea in the west, on the Mediterranean Sea in the south, on
 (
Lester

n. 1. (Meteor.) A dry sirocco in the Madeira Islands.
,
Asaria and van der
Linden
 city (1990 pop. 36,701), Union co., NE N.J., in the New York metropolitan area; inc. 1925. During the first half of the 20th cent.
 2002), (Park,
Han
 , dynasty of China that ruled from 202 B.C. to A.D. 220. Liu Pang, the first Han emperor, had been a farmer, minor village official, and guerrilla fighter under the Ch’in dynasty.
, and
Kim

, 2002) (Pergamalis
2003). Table 1 shows the U.S. asset-backed securities market is large
and growing as compared to the U.S. corporate bond market.

The growth of the market can be attributed to two main factors:
demand by investors in search of spread above “safer” fixed
income securities such as government and corporate debt, and supply by
lenders wishing to off-load receivables. As the market grows, so does
the ever increasing different types of ABS that are created by Wall
Street. Investors in ABS are typically institutional investors of fixed
income securities seeking portfolio
diversification

 and/or higher
yields. Individual investors also indirectly invest them as many bond
funds will hold ABS.

ABS are financial instruments whose cashflows are “backed
by” installment loans or other receivables. An issuer of an ABS
forms a trust that consists of loans, generally
characterized
  
tr.v. character·ized, character·iz·ing, character·iz·es
1. To describe the qualities or peculiarities of:

2.
 by some
common factor; for example,
automobile
 self-propelled vehicle used for travel on land. The term is commonly applied to a four-wheeled vehicle designed to carry two to six passengers and a limited amount of cargo, as contrasted with a truck, which is designed primarily for the transportation of
 loans. From this trust, the
issuer will create a series of classes (
tranches

) of securities that
make up the deal. These classes receive their cashflows from the trust.
The timing of the cashflows to an individual class depend on the
priority of the class within the overall structure and the payment
behavior of the underlying loans. The deal structure and rules
associated with priority of cashflows impacts the payments received by
the ABS investor.

The payment behavior of the underlying loan holders is important as
well. Loan payments are generally differentiated by normal scheduled
payment vs.
prepayments

. Further, prepayments are often differentiated
by either partial
prepayment

 or full prepayments. Scheduled payments are
the expected monthly payment the borrower agrees to pay on a monthly
basis that includes both principal and interest. The borrower generally
has the right to either partial
prepay
  
tr.v. pre·paid, pre·pay·ing, pre·pays
To pay or pay for beforehand.


pre·payment n.
, pay additional principal above
and beyond what is required each month or can full prepay, pay off the
loan in its entirety. In either case, the additional cashflow paid by
the borrower which in turn is paid into the trust will often be
“passed-through” to the investor of the ABS. This means the
ABS expected cashflows and actual cashflows can vary greatly depending
on the prepayment behavior of the borrowers.

Within the market of ABS, there are numerous deal types–overall
characteristic of the loans or collateral that backs a particular ABS
issue. Some of the more common deal types of ABS are home equity loans,
home equity line of credits, automobile loans, and credit card
receivables. Each of these deal types as well as the numerous others
have subtle differences, but the common element is the cashflow paid by
the loan borrower or credit card holder is ultimately used to pay the
ABS investor. Please see Table 2 for details of outstanding and new
issuance of ABS, by various deal types.

In addition to defining an ABS by the deal type, specific classes
that comprise the overall deal structure are often defined by class or

tranche

A class of bonds.
 descriptors. These descriptors are designed to provide the
investor in a specific class a general understanding of the payment
schedule/structure of the particular class they are investing in and how
their bond relates to the overall deal structure. Each deal has
associated rules for how to distribute the cashflow received from the
underlying collateral. As the ABS market developed so did the complexity
of the associated payment rules and in turn various terms/class
descriptions used by the market. In some instances, class payment rules
require the use of multiple descriptors to accurately provide
description of the cashflow payment structure. The prospectus will often
include some of the more market accepted descriptors as part of the
description of the ABS.

One of the distinguishing features of ABS in contrast to other
fixed income securities is the concept of prepayments. Unlike
traditional corporate bonds that generally pay interest during the term
of the bond and then pay the principal at maturity or if a bond is
called, ABS pays principal along with interest throughout the term of
the bond. In addition, the principal component of the cashflow is a
function of the prepayment behavior of underlying collateral which
greatly impacts the overall term of the ABS. Although ABS are
assigned
  
tr.v. as·signed, as·sign·ing, as·signs
1. To set apart for a particular purpose; designate:

2.
 a
maturity date at issue, ABS rarely will remain outstanding until their
legal maturity date. In both
MBS

 and ABS, market participants generally
use another value to measure “term” of an MBS/ABS–weighted
average life (
WAL

WAL Walloon
WAL Weighted Average Life
WAL Wide Angle Lens
WAL Write Ahead Log
WAL WATS Access Line
WAL Watertown Arsenal Laboratories  
) which is defined as time weighted average time of
receipt of principal. When an ABS is issued, it is quite common for the
lead manager of the deal to provide an original WAL value which is
calculated using an assumed average prepayment rate.

Similar to corporate bonds, ABS are often quoted in terms of basis
points spread to a corresponding
benchmark

 security. For example, an ABS
with an original WAL of 5 years will often be priced as a spread to the
U.S. 5 year treasury. Because the market for ABS is typically over the
counter and cashflows are highly dependent on prepayment behavior of the
underlying borrowers, there is much variability in terms of the price or
value of a given ABS. Overall, there is less
price transparency

 in ABS
market as compared to other fixed income markets such as government
bonds, corporate bonds, and municipal bonds.

Much like other fixed income securities, ABS deals are brought to
market by an underwriter who is responsible for structuring the deal and
bringing the deal to market. For the underwriting services, the
underwriter will generally receive a fee that is based on percentage
dollar amount of the individual class amounts. It should be noted that
the underwriting firm will likely also receive compensation not only for
their underwriting services but also in the form of commissions as they
sell the bond into the primary market.

The ABS market experienced significant growth during late
1990’s and early/mid 2000’s. The growth in the ABS market
coincided with the growth in the number of loans provided to subprime
borrowers. Sub-prime borrowers is a term associated with borrowers with
lower credit scores and generally considered more likely to potentially
default on a loan. The increase in loans led to an increasing number of

securitized

Of, related to, or being debt securities that are secured with assets. For example, mortgage purchase bonds are secured by mortgages that have been purchased with the bond issue’s proceeds.
 securities such as ABS. However, beginning in late 2006 the
market for ABS changed as these borrowers began to default on their
loans. As borrowers began to default or became
delinquent
 1) adj. not paid in full amount or on time. 2) n. short for an underage violator of the law as in juvenile delinquent.


DELINQUENT, civil law. He who has been guilty of some crime, offence or failure of duty.
 in payments,
the values of ABS securities
diminished
  
v. di·min·ished, di·min·ish·ing, di·min·ish·es

v.tr.
1.
a. To make smaller or less or to cause to appear so.

b.
 significantly. This decrease in
turn led to an overall
downturn

A decline in security prices or economic activity following a period of rising or stable prices or activity.
 in the ABS issuance. Issuance in 2007
was $759 Billion, nearly $200 Billion less than the $943 Billion issued
in 2006. We next turn to a more detailed overview of ABS collateral
types, underwriters, ratings and weighted average life.

1. Collateral Type

ABS are typically structured by collateral type. Appendix A
provides a table of the deal type classifications used by Bloomberg to

classify
  
tr.v. clas·si·fied, clas·si·fy·ing, clas·si·fies
1. To arrange or organize according to class or category.

2. To designate (a document, for example) as confidential, secret, or top secret.
 ABS deals. These classifications are generally considered
“industry standard” and commonly used not only by Bloomberg,
but by the market in general when classifying particular ABS deals.
There is significant variety in the types of loans/receivables that are
packaged together when structuring an ABS ranging from automobile loans
to receivables from utilities such as electricity companies.

Although there are numerous collateral types and ABS, the dollar
amount issued varies significantly across different collateral types.
Table 3 presents dollar issuance by deal type for all nonprivate placed
U.S. ABS issued between 1999 through 2006. For example, ABS backed by
home equity loans during the period dominates issuance at $2.3
Trillion

 followed by automobile loan ABS with $716 Billion, credit card ABS with
$486 Billion and student loan ABS with $285 Billion. The remainder of
the issuance is largely
fragmented
  
n.
1. A small part broken off or detached.

2. An incomplete or isolated portion; a bit:

3.
 across nearly 30 other
loan/receivable types.

2. Underwriter

Table 4 provides ABS underwriter rankings of the top 20
underwriters ranked by dollar amount underwritten during the period
1999-2006. A few observations can be noted. First, some underwriters
such as
Credit Suisse

 and
Lehman

 were either top underwriter or in the
top 5 underwriters for each during the period 1999-2006. Second, there
are some underwriters that show significant growth in market share
during the period–two notable examples are Barclays and
Countrywide
  
adv. & adj.
Throughout a whole country; nationwide:

Adj. 1.
 that had little or no underwriting activity prior to 2001, but show
substantial and consistent growth year to year for years thereafter.

Table 5 presents the average underwriting fees charged for the same
top 20 underwriters during the period 1999-2006. For example, consider

Lehman Brothers

. During 1999 through 2002, Lehman Brothers charged
higher than average or average fees. However, during the periods of the
most rapid growth in terms of issuance, 2003 through 2006, the fees they
charged were lower than average.

3. Ratings

Credit ratings are often assigned to ABS. Table 6 shows dollar
issuance of ABS by year and credit rating. Over half of the total dollar
issuance during the period 1999-2006 has credit rating of A- or higher
with the majority of it being rated AAA. The remaining issuance is
relatively evenly distributed across the other, lower ratings.

Table 7 shows average
underwriting fee

 by year and credit rating.
ABS rated AAA typically have lower than average underwriting fee during
each individual year, while lower rated securities such as
BBB

A medium grade assigned to a debt obligation by a rating agency to indicate an adequate ability to pay interest and repay principal. However, adverse developments are more likely to impair this ability than would be the case for bonds rated A and above.
 have
higher than average underwriting fees. This suggests that underwriters
charge higher fees for lower rated securities, suggesting relative
difficulty in marketing lower quality securities.

4. Original Weighted Average Life

Table 8 shows ABS by weighted average life and amount issued during
the sample period used in the study. During the sample period, nearly a
third of all issuance had original WAL between 2.5 and 3.5 years and
over 70% of the issuance had original WAL between .5 and 5.5 years.

Table 9 shows ABS by weighed average life and average underwriting
fee. This data does not indicate a relationship between original
weighted average life and average underwriting fees. This contrasts with
the findings of Livingston and Miller (2000) who found a positive
relationship between term/maturity and underwriting fees.

III. Literature review and hypothesis formulation

1. ABS literature

DeMarzo and Duffle (1999) note that issuers of ABS need to evaluate
two costs when determining optimal security design: The opportunity cost
related to holding assets with lower returns than those that could be
sought if the issuer securitized these assets and used the capital
raised to invest in higher-return assets, and the potential negative
impact of including these lower returning assets in the
securitization

 and the potential impact of lower demand for the security. DeMarzo and
Duffie develop a framework for evaluating optimal security design.

Han and Lai (1995) note that securitization has been successful in
markets such as mortgages and asset-backed loans; it has not been as
successful for insurance products. They offer three reasons this has
been the case: 1) it is more costly to
securitize

 
unstable

adj 1. not firm or fixed in one place; likely to move.
2. capable of undergoing spontaneous change. A nuclide in an unstable state is called
radioactive. An atom in an unstable state is called
excited.
 cashflows
from insurance products into fixed income securities, 2) regulations do
not make it
conducive
  
adj.
Tending to cause or bring about; contributive:  See Synonyms at favorable.
 to do so since regulators do not permit to take
the securities assets/liabilities off their balance sheet, and 3)
insurers have other ways to
diversify

To acquire a variety of assets that do not tend to change in value at the same time. To diversify a securities portfolio is to purchase different types of securities in different companies in unrelated industries.
 their portfolio thereby reducing
the need/attractiveness of securitization.

Plantin (2004) develops a model to gain insight into why firms
issue asset securitization deals into separate classes or tranches. He
points out that many ABS structures such as CDOs are split into senior
and junior classes. He suggests that
investment banks

 that sell these
securities generally target different types of investors for each piece:
the senior pieces are generally sold to less sophisticated, retail
institutions, while the junior pieces generally go to more sophisticated
investors who have the knowledge and resources to
analyze

v.
1. To examine methodically by separating into parts and studying their interrelations.

2. To separate a chemical substance into its constituent elements to determine their nature or proportions.

3.
 these
securities.

One the biggest challenges with ABS is the ability to properly
value/assess the risk of the securities. A number of papers focus on
this topic such as Heidari and Wu (2004). Based on the results of a
survey of
market participant

, they compare these ideal attributes to
those of six models of major MBS dealers. They find that five of the six
fall short of meeting the desired attributes. In addition, they find
high correlation among these five suggesting potential
herding

 among MBS
analysts.

Adelson (2003) points out some of the risks associated with ABS/CDO
markets. In addition to risks such as
prepayment risk

, liquidity risk,
he raises another source of risk – model risk. Model risk is the
“risk that a model does not describe reality well enough to produce
reliable results.”
Antonov

 and Raevsky (2003) develop a model for
the modeling of credit risk that can be used for ABS portfolios.

Childs, Ott and Riddiough (1996) develop a model for the pricing of
commercial
mortgage-backed securities

.
CMBS

 are securities backed by
non-residential mortgages, for example, mortgages for businesses,
apartment complexes, etc. One of their findings involves the
relationship between pool size and tranche value. They find that 5 to 10
distinct mortgages are required to realize most of the effects of asset
diversification.

Additional research is related to prepayment and credit risk.
Hetfield and Sabarwal (2004) find that prepayments on subprime loans
increase with loan age. However, they do not find prepayments affected
as much by current market interest rates. Default rates are much more
sensitive to aggregate shocks than are prepayment rates such as
increases in unemployment. They also find significant differences in the
default and prepayment rates faced by different subprime lenders.
Lenders charging the highest interest rates experience the highest
default rates, but also experience somewhat lower prepayment rates. They
believe that there are substantial differences among subprime borrowers,
and that different lenders target different segments of the subprime
market.

Lacour-Little and
Chun

 (1999) explore the relationship between
third party loan originators and prepayment behavior. They point out
those third parties, such as mortgage brokers, have economic incentives
to encourage borrowers to
refinance

 and, accordingly, their actions may
affect asset values. They find that loans originated by third parties
are significantly more likely to prepay. Moreover, third party loans are
about three times as sensitive to
refinancing

 incentives, compared to
retail loans.

Lucas
 , variant of  
,
Goodman

, and Fabozzi (2004) examine how rating agencies
calculate default rates on structured finance securities. They point out
a number of the limitations of the methodologies used by S&P and
Moody’s. They conclude by offering a new calculation which uses as
a base the calculations of S&P and Moody’s but is modified to
address at least some of the weaknesses of the two rating agencies
methodologies.

Ammer

 and
Clinton

1 Town (1990 pop. 12,767), Middlesex co., S Conn., on Long Island Sound; settled 1663, set off from Killingworth and inc. 1838. The school that later became Yale opened here in 1702.
 (2004) examined the impact of credit rating
changes on the pricing of asset-backed securities. Using a sample of
1300 rating changes by Moody’s or S&P, they find that rating
downgrades tend to be accompanied by negative returns and widening
spreads with the average effects being stronger than those for corporate
bond rating changes. This suggests that ABS investors appear to rely
more on rating changes as a source of negative changes than bond
investors. In terms of effects of rating upgrades, the authors found
very little in terms of market reaction to these events.

Additional studies have focused on financial innovation, the
process or decisions involved in developing new financial products such
as ABS.
Silber

 (1983) provides an overview of financial innovation. He
cites three main factors that most commonly lead to the development of
newer financial products such as mortgage-backed securities. The first
is that firms
innovate
  
v. in·no·vat·ed, in·no·vat·ing, in·no·vates

v.tr.
To begin or introduce (something new) for or as if for the first time.

v.intr.
To begin or introduce something new.
 to ”
lessen
  
v. less·ened, less·en·ing, less·ens

v.tr.
1. To make less; reduce.

2. Archaic To make little of; belittle.

v.intr.
To become less; decrease.
 the financial
constraints

 imposed
on firms.” The second is technology. Finally, the third main source
he cites is legislative, which he points out, was the key factor in the
development of the mortgage-backed securities market.

Boot and Thakor (1993) explore the issue of security design and why
firms issue multiple claims on an asset, many classes when securitizing
loans or mortgages rather than a single security. They develop a complex
model that suggests that firms split securities into two types:
information
insensitive
  
adj.
1. Not physically sensitive; numb.

2.
a. Lacking in sensitivity to the feelings or circumstances of others; unfeeling.

b.
 and information sensitive. They argue that
informed traders will focus on the information sensitive securities and
will move the security closer to its fundamental value, thereby
increasing the issuer’s total expected revenue.

2. Underwriting fees for IPOs

Underwriting fees are the fees paid by issuers of financial
securities to financial firms that take on the responsibility of the
marketing of debt or equity to the financial market. A number of studies
have focused on the underwriting fees for IPOs. Chen and
Ritter
  
n. pl. ritter
A knight.


[German, from Middle High German riter, from Middle Dutch ridder, from r
 (2000)
investigated why underwriting fees/spread for U.S. IPOs tend to cluster
towards 7% which is much higher than fees in non-U.S, markets. They
argue that the relative high average spread and clustering are due to
“strategic pricing.” They argue that investment bankers
maintain the higher fees and compete for
IPO

 business on other grounds.
By avoiding competing on the grounds of fees, investment banker’s
year-end bonuses are not in
jeopardy
 in law, condition of a person charged with a crime and thus in danger of punishment. At common law a defendant could be exposed to jeopardy for the same offense only once; exposing a person twice is known as

double jeopardy.
.

Carter and Manaster (1990) examined IPOs and underwriting fees.
They identify a relationship between underwriter prestige (as measured
by relative position an underwriter is listed in the announcement of a
pending public offering) and returns of IPOs. They argue that
underwriter prestige is a signal to investors as to the relative
riskiness of an IPO. They find that prestigious underwriters are
generally associated with IPOs with lower returns / lower risk
offerings.

Hansen
 , Gerhard Henrik Armauer 1746-1845.

Norwegian physician and bacteriologist who discovered (1869) the leprosy bacillus.
 (2001) explores potential reasons why IPOs fees in the U.S.
tend to be 7%. His evidence suggests that
collusion

 between underwriters
is not the source, but rather underwriters compete for business by other
factors such as reputation, placement abilities, and the degree to which
IPOs are
underpriced
  
tr.v. un·der·priced, un·der·pric·ing, un·der·pric·es
1. To price lower than the real, normal, or appropriate value.

2.
. In a similar study, Barondes, Butler, and
Sanger
 city (1990 pop. 16,839), Fresno co., S central Calif., in the San Joaquin Valley; inc. 1911. It is a shipping and processing center for a variety of agricultural products. Manufactures include sheet metal products, wine, machinery, and corrugated boxes.
 (2000) focused on IPOs whose underwriter fees differed (higher or lower)
than the typical seven percent. They examined the relationship between
these deviations and offering prices of the IPOs. They found that the
lower (higher) the fees paid, the lower (higher) the offering price of
the IPO. They offer marketing efforts of these IPOs by the underwriter
is a function of the amount paid to them in terms of fees.

Additional studies have focused on underwriting fees in non-U.S,
markets. Torstila (2001) explored what determines IPO spreads in Europe.
He found IPO spreads by European issues in Europe are significantly
lower than by European issuers in the U.S. In addition, IPOs listed
jointly in U.S. and Europe generally have higher spreads than issues
listed only in Europe.

Bajaj

, Mazumdar, Chen, and
Sarin
 , volatile liquid used as a nerve gas. It boils at 147&degC; but evaporates quickly at room temperature; its vapor is colorless and odorless.
 (2003) examine IPO underwriting
spreads during the period 1980 to 1998. They found that median size of
IPOs have tripled during this time. Further they find that the more
recent IPOs involved riskier firms. They also find that clustering of
IPO fees existed in earlier periods often at higher rates then 7%.

James
 in the Gospel of St. Luke, kinsman of St. Jude. The original does not specify the relationship.


James, rivers, United States

 (1992) explored whether underwriter fees in IPOs are
associated with relationship-specific assets or
setup

 costs. More
specifically, he examined how underwriting fees are affected by
expectations that the firm will issue additional shares in the future.
He finds that underwriter fees are significantly lower in IPOs when
firms issue additional shares and use the same underwriter than when
firms do not continue with the same underwriter or do not issue again.

Carter, Dark, and
Singh

 (1998) explore the relationship between
underwriter reputation and performance of IPO stocks. Using 3 different
measures of underwriter prestige, they find that reputation is
significantly related to initial return of the IPO; there is less

short-run

 
underpricing

The pricing of a new security issue at less than the prevailing price of the same security in the secondary market. Underpricing helps ensure a successful sale.
 with more prestigious firms vs. less prestigious.
In addition, they find that on average the long-run market-adjusted
returns (3-year holding period) are less negative for IPOs underwritten
by more prestigious underwriters. Lastly, of the three measures used to
measure underwriter prestige, they find the Carter-Manaster (1990)
measure serves best to measure underwriter prestige.

Hebb and
MacKinnon

 (2000) examine the IPO valuation comparing those
IPOs underwritten by
non-commercial

 banks versus commercial banks. They
find greater uncertainty in the true value of IPOs underwritten by
commercial banks versus non-commercial banks. They offer as a potential
reason for this uncertainty as the market’s perception of a
potential conflict of interest by the commercial bank. (2)

3. Underwriting fees for debt

Livingston and Miller (2000) examined investment bank reputation
and the underwriting of debt. Using both the Carter and Manaster metric
and percentage of total dollar issued of debt to measure underwriter
prestige, they find that higher prestige firms charge lower underwriting
fees. In addition, offering yields tend to be lower and offering prices
higher for more prestigious underwriters. Livingston and Jewell (1998)
explore the issue of underwriter spread for industrial bonds that have
split ratings. They find that if the bond has an investment grade rating
from both S&P and Moody’s, even if they differ, the
underwriting fees are effectively the same. However, if the bond is
rated below investment grade by one or both of the agencies,
underwriting fees are effected and are generally between the spreads for
the higher rating and lower rating.

Burch, Nanda and Warther (2004) explore the issue of underwriting
relationships and fees charged. They find that loyalty (repeat business)
lead to lower fees for common stock offers, but the opposite for debt
offers. In addition, they find that firms that change to higher quality
(higher reputation using the Carter-Manaster metric) face lower fees for
both equity and debt offerings.

Butler (2007) examined municipal bond underwriting and whether the
choice of using a local underwriter–underwriter with ongoing business
in the same state as the municipal bond–appears to influence
underwriting fees. He found that local underwriters charged lower fees
and bonds were offered with lower yields as compared to non-local
underwriters. Further, he found these benefits existed greater for bonds
with lower credit quality and bonds not rated by the rating agencies.

Santos
 , city (1996 pop. 412,288), São Paulo state, SE Brazil, on the island of São Vicente in the Atlantic just off the mainland.
 and Tsatsaronis (2002) researched the effects of the

introduction of the euro

 on the underwriting of corporate bonds. Their
study identified two results. The first is that with the introduction of
the euro, the average underwriter fee decreased–pre vs. post euro. This

presumably
  
adj.
That can be presumed or taken for granted; reasonable as a supposition:
 was due to the increased competition amongst firms in the
region. Their study also examined when choosing an underwriter who were
they more likely to choose: an underwriter from their home country with
whom they are more likely to have an ongoing relationship or larger
investment houses that have a more global placing capacity for their
bonds. They found firms migrating towards larger international
investment banking houses.

Yasuda

 (2003) explores the issue of underwriting of corporate bonds
and what effect entry of commercial banks has had on the market for
underwriting services. The research focuses on examining two scenarios
of
coexistence
  
intr.v. co·ex·ist·ed, co·ex·ist·ing, co·ex·ists
1. To exist together, at the same time, or in the same place.

2.
 of commercial and investment banks in the market for
underwriting services.

* Both underwriters and investment banks fetch the same price for
the security (there is not differentiation in certification ability)

* Commercial banks fetch a higher price for the security, and
investment houses discount their fees to the level where issuing firms
are
indifferent

 to underwriter (there is a differentiation in
certification ability).

Altinkihc and Hansen (2000) examine underwriting fees for bond
offerings and
SEO

 (seasoned equity offerings) and how fees vary by issue
size. They suggest that these should exhibit a U-shaped curve (fees as a
function of issue size). The logic behind this
assertion

 is that
initially there are certain
fixed costs

n.pl the costs that do not change to meet fluctuations in enrollment or in use of services (e.g., salaries, rent, business license fees, and depreciation).
 with underwriting and therefore
as size increase, the fees will decrease. However, as size increases to
a certain point, the fees begin to rise due to placement cost–the costs
associated with the difficulty the underwriter may experience in trying
to place larger issues. They find evidence of fees curve is U-shaped
both for equities and bond offerings.

Two papers are most notable as they
pertain to

verb , concern, refer to, regard, be part of, belong to, apply to, bear on, befit, be relevant to, be appropriate to, appertain to
 the current study.
The first is that of Butch, Nanda and Warther (2004). They found that
loyalty (repeat business) leads to lower fees for offers for common
stock which is consistent with James (1992). However, they found the
opposite for debt offers. This leads to the question for our study will
underwriting fees for repeat business, subsequent ABS issuance by the
same issuer using the same underwriter, be akin to IPO and lead to lower
underwriting fees or be similar to that of debt markets and lead to
higher underwriting fees.

The second paper most notable for the current study is that of
Livingston and Miller (2000) which examines bank reputation/prestige and
underwriting fees for debt issues. They find that higher prestige firms
charge lower underwriting fees. Our study will use methodology closely
related to that of Livingston and Miller as we ask the question, do more
prestigious underwriter’s
charger

 lower fees for ABS?

4. Hypothesis formulation

Although asset-backed securities and corporate debt share some
commonalities in that they are part of the overall fixed income/debt
market, a number of differences exist. First, by definition,
asset-backed securities and corporate debt differ in that ABS are
collateralized by a package of loans, where corporate debt are
securities issued by corporations. Second, the literature on ABS
suggests that valuing ABS presents some unique challenges that do not
exist for corporate bonds. Lastly, the ABS market has experienced
periods of rapid growth, such as the period 2002-2005 where it nearly
doubled. During the same period, the corporate debt market remained
relatively constant. In this paper we investigate the following: will
the inverse relationship between underwriter prestige and underwriting
fees identified by Livingston and Miller for corporate debt hold true
for the asset-backed securities market? Alternatively, will the rapid
growth in the market instead lead to less prestigious/ smaller
underwriters charging lower fees as a means to gain market share,
leaving top ranking underwriters charging relatively higher fees? In
consideration we accordingly propose the following hypothesis and

alternative hypothesis
 Epidemiology A hypothesis to be adopted if a null hypothesis proves implausible, where exposure is linked to disease. See Hypothesis testing. Cf Null hypothesis.
:

Hypothesis 1: Consistent with that of U.S. corporate bonds and
identified by Livingston and Miller (2000), there exists an inverse
relationship between underwriter prestige and underwriter fees paid by
issuers of asset-backed securities.

Alternative Hypothesis 1: The ABS market has shown rapid growth in
market size between 1999 through 2006 which may attract more
underwriters to the market willing to accept lower underwriting fee and
thereby accepting higher internal costs to capture business. This
suggests that there exists a positive relationship between underwriter
prestige and underwriting fees paid by issuers of asset-backed
securities as newer entrants into the underwriting of these securities
attempt to gain market share.

Prior research in both IPOs and corporate bonds focused on loyalty
of firms using the same underwriter. Burch, Nanda and Warther (2004)
found that loyalty (repeat business) leads to lower fees for common
stock offers that which is consistent with James (1992). However, they
found the opposite for debt offers. Livingston and Miller (2000),
however, using their variable count found an inverse relationship
between loyalty and underwriting fees for corporate bonds counter to the
findings of Burch, Nanda, and Warther (2004). The underwriting process
for ABS is quite similar to that of corporate bonds.

In consideration we accordingly propose the following hypothesis
and alternative hypothesis:

Hypothesis 2: Similar to corporate bonds, there exists an inverse
relationship between loyalty (repeat business) and underwriting fees.
This could be explained by that a underwriters
due diligence

 costs”
may be lower due to repeat business/same issuer.

Alternative Hypothesis 2: Similar to IPOs, there exists a positive
relationship between loyalty (repeat business) and underwriting fees.

IV. Methodology

Our methodology is similar, though not identical, to Livingston and
Miller (2000), who use
OLS

 to explore relation between underwriter
prestige and underwriter spread/fees in their study of nonconvertible
debt. Our definition of Prestige variable is consistent with Livingston
and Miller. Our methodology differs in the following ways. First,
weighted average life is used instead of maturity. Second, we do not
include callability as a variable, as callability is much less of a
factor in pricing of ABS. Third, we include
dummy

 variables for each of
the four largest collateral types in terms of dollar issuance (auto,
credit cards, home equity and student loans). Fourth, we include the
original deal size. Fifth, we include dummy variables for the class

descriptor

 for the ABS. Finally, a sixth variable for short-term loyalty
is included. The following four equations are estimated in our study:

[MATHEMATICAL EXPRESSION NOT
REPRODUCIBLE
  
v. re·pro·duced, re·pro·duc·ing, re·pro·duc·es

v.tr.
1. To produce a counterpart, image, or copy of.

2. Biology To generate (offspring) by sexual or asexual means.
 IN
ASCII
 or  a set of codes used to represent letters, numbers, a few symbols, and control characters. Originally designed for teletype operations, it has found wide application in computers.
] (1)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (2)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (3)

US : [[alpha].sub.0] + [[beta].sub.1] x AAA + [[beta].sub.2] x

Mezzanine
  
n.
1. A partial story between two main stories of a building.

2. The lowest balcony in a theater or the first few rows of that balcony.
 + [[beta].sub.3] x
Subordinated

 + [[beta].sub.4] X DealAmount
+ [[beta].sub.5] x ParAmount + [[beta].sub.6] x Prestige +
[[beta].sub.7] x WAL + [[beta].sub.8] x Count + X’YEAR_DUMMIES +
[epsilon] (4)

US is the underwriter spread (in basis points) which is provided
typically in the prospectus or other documentation provided by
underwriter when ABS is issued. AAA is a
dummy variable

 equal to one if
the ABS is rated AAA by S&P. Underwriting spreads vary and typically
are higher for classes that are more difficult to sell i.e. mezzanine
bonds. Since the credit crisis that began in 2007, credit ratings have
been questioned as to whether they are reliable proxies for risk.
However, during the sample period of this study, credit ratings were
generally accepted as a
proxy

 of risk and therefore we include them in
the study. We expect that higher credit rated securities would have
lower underwriting fees.

Mezzanine and Subordinated are dummy variables representing class
descriptors mezzanine and subordinated. These were included to see if
different class descriptors, which are assigned to help identify
variability in expected cashflows, impacts underwriting fees. Indeed,
class descriptor may represent an alternative proxy of risk. One may
expect that class descriptors associated with higher variability in
cashflows, namely mezzanine and subordinated, are associated with higher
underwriting fees.

Deal Amount is the log of original deal amount. ABS are commonly
issued not individually, but as part of an overall deal structure. We
include this variable to see if any relationship exists between overall
deal size and underwriting fees.

Auto, CreditCard, HomeEquity, and StudentLoan are dummy variables
representing collateral types auto, credit cards, home equity and
student loans. The type of collateral may serve as an alternative proxy
for risk, and also controls for
cross-collateral

 type effects. For
example, deals backed by collateral types that are smaller in terms
overall issuance such as ABS backed by boat loans may be more difficult
to bring to market than deals backed by collateral types with larger
amount of issuance such as auto, credit card, home equity, and student
loans.

ParAmount is the log of size/par amount of the class. Livingston
and Miller (2000) found larger bond issues tend to have higher
underwriting fees. We include this variable to see if this exists within
ABS market as well. Prestige is calculated by taking the market share of
each underwriter during the entire sample period (1999-2006). WAL is the
original weighted average life. It is more appropriate to use weighted
average life as
opposed
  
v. op·posed, op·pos·ing, op·pos·es

v.tr.
1. To be in contention or conflict with:

2.
 to maturity date due to the inherent strong
likelihood the security will not remain outstanding until maturity. It
is quite common in the market to use weighted average life for this
purpose. Similar to the finding in Livingston and Miller (2000) that
longer term bonds tend to have higher underwriting fees, one expect to
see a similar relation between WAL and underwriting fees for ABS.

Loyalty is dummy variable for short term loyalty which is set to 1
if the underwriter used was the same as the one used for prior issuance.
This metric is used by Burch, Nanda, and Warther (2004). Count is the
log of the number of securities of a particular issuer that were
underwritten by the same underwriter during the sample period. A similar
metric was used in Livingston and Miller (2000), and represents another
metric for loyalty. Burch, Nanda and Warther (2004) found no relation
using this metric. Livingston and Miller (2000), however, did find a
relation in terms of lower underwriting fees.

YEAR DUMMIES is a vector of dummy variables for the year of the
given observation. We include these dummies to investigate whether year
of issuance impacts underwriting fees.

Equations 1 and 2 include Loyalty while Equations 3 and 4 include
Count. Equations 1 and 3 include dummy variables for four ABS deal
types, Auto, CreditCard, HomeEquity, and StudentLoan while Equations 2
and 4 do not. Equations 1 through 4 are initially estimated across the
entire pooled cross
sectional
  
adj.
1. Of, relating to, or characteristic of a particular district.

2. Composed of or divided into component sections.

n.
 time series and then estimated separately,
by year. The annual regressions exclude the year dummies. Further, by
the annual regressions Prestige was calculated year by year. For
example, the prestige for 1999 was calculated by using only 1999
issuance. This was then used as a variable when evaluating influence on
fees in the following year.

V. Results

The results for the pooled cross sectional time regressions

estimation

 of Equations 1 through 4 is reported in Table 10. The results
for the annual
regression
 in psychology: see defense mechanism.


regression

In statistics, a process for determining a line or curve that best represents the general trend of a data set.
 estimations for Equations 1 through 4 are
reported in Tables 11 through 14, respectively.

Hypothesis 1 and Alternative Hypothesis 1 focused on the
relationship between underwriting fees and underwriter prestige.
Hypothesis 1 stated that consistent with U.S. corporate bonds and
identified by Livingston and Miller (2000), there exists an inverse
relationship between underwriting fees and underwriter prestige. Due to
the rapid growth in market size during the sample period 1999-2006, we
offered that this may attract more underwriters willing to accept lower
underwriting fees to capture business. We offered Alternative Hypothesis
1, there may exist a positive relationship between underwriting fees and
underwriter prestige. As newer entrants into the underwriting of these
securities attempt to gain market share, they accept lower underwriting
fees than more prestigious underwriters.

To test Hypothesis 1 and Alternative Hypothesis 1, we used one of
the same
metrics
 Managed care A popular term for standards by which the quality of a product, service, or outcome of a particular form of Pt management is evaluated. See TQM.
 used by Livingston and Miller (2000) to determine
prestige–the proportion of market share for an underwriter during the
sample period. In our estimation of all four equations, we found the

coefficient
 /co·ef·fi·cient/ ()
1. an expression of the change or effect produced by variation in certain factors, or of the ratio between two different quantities.

2.
 to be positive. In 3 of the 4 equations, we found prestige
to be statistically significant at .05 level while in one equation we
found it to be statistically significant at .1 level. Of the 28 annual
regressions, 17 of them are positive and statistically significant,
while 2 are negative and statistically significant. Collectively, these
results are supportive of Alternative Hypothesis 1.

Hypothesis 2 and Alternative Hypothesis 2 focused on underwriter
loyalty to see if there was any benefit in terms of lower underwriter
fees for an issuer to use the same underwriter for subsequent issues.
Livingston and Miller (2000) used a measure they called count–defined
as the number of bond underwritten by the same underwriter for a given
issuer of ABS during the sample period. Butch, Nanda, and

Warther (2004) used an alternative measure of loyalty which was a
dummy variable assigned 1 if the underwriter used in current bond also
served as underwriter in previous issuance. We use both metrics in our
estimations. In Equations 1 and 2 we use the Burch, Nanda, and Warther
measure of Loyalty. In both equations we find the coefficient to be
positive and statistically significant at .05 level. In Equations 3 and
4 we use the Livingston and Miller measure of Count and also find the
coefficients to be positive and statistically significant at .05 level.

We also evaluate loyalty year by year. Of the 28 annual
regressions, we found all measures of loyalty to be positive and
statistically significant at the .01 level in all but 6 of the 28
samples. Collectively, these findings suggest there is no benefit, in
fact potentially a
detriment

, to using the same underwriter–the more
loyal, the higher the underwriting fees. This provides evidence in
support of Alternative Hypothesis 2.

Significant coefficients associated with AAA are negative and
statistically significant. The negative coefficients suggest AAA rated
securities have lower underwriting fees vs. non-AAA securities. This
finding is consistent with Livingston and Miller. The coefficients
associated with Subordinated and Mezzanine are both statistically
significant at .05 level across all estimations. Both have positive
coefficients which suggest securities with subordinated or mezzanine
class descriptor have higher underwriting fees vs. non-subordinated and
non-mezzanine securities. This finding is expected since subordinated
and mezzanine securities are lower tier within the overall deal
structure. Since class descriptors are an ABS/CMO concept, this finding
is unique to this particular financial market.

The coefficients associated with DealAmount are positive and
statistically significant at .05 level in all estimations of the pooled
cross sectional time series, suggesting that the larger deal size, the
larger underwriting fees on the individual securities. Since the concept
of an overall deal is unique to ABS/CMO, this finding is unique to this
particular financial market.

Dummy variables for the four largest deal types (auto, credit
cards, home equity and student loans) were tested. With the exception of
CreditCard, the coefficients associated with these variables are
statistically significant with negative coefficients in Equation 3, but
not statistically significant Equation 1. Collectively, this suggests a
question able relationship between deal type and underwriting fees.

The coefficient associated with ParAmount is negative statistically
significant at .05 level in all four pooled cross sectional time series
regressions, a result that holds for most of the annual estimations as
well This suggests that the larger the size of the security, the lower
the underwriting fees charged. This is
inconsistent

 with Livingston and
Miller (2000) who did not find a statistically significant relationship
between par amount (or what they defined as proceeds) and underwriting
fees for U.S. corporate bonds.

The coefficient associated with WAL is statistically significant
with positive coefficients in all four pooled cross sectional time
series regressions, a result that holds for most of the annual
estimations as well. This result is consistent with Livingston and
Miller (2000) that found similar results for U.S. corporate bonds
between maturity and underwriting fees.

VI. Conclusions

The contributions of this study are two-fold. The first is a
contribution to the overall literature on asset-backed securities.
Despite being developed in the early 1980’s and issuance nearly the
same in terms of dollar amount of U.S. corporate bonds, relatively
little research exists for ABS. Much of the existing research
pertaining
  
intr.v. per·tained, per·tain·ing, per·tains
1. To have reference; relate:

2.
 to ABS is related to the issues of financial innovation, optimal
security design, and some of the inherent risks associated with ABS and
valuing ABS. The current study provides a detailed descriptive analysis
of the ABS market.

The second contribution of the study is to the existing literature
on the factors that influence underwriting fees. Underwriting fees have
been researched in a number of financial markets. Most literature
pertains to IPOs (Chen and Ritter 2000; Hansen 2001; James 1992) among
many others. There also exists a fair amount of research on corporate
bonds and underwriting fees (Livingston and Miller (2000), Burch, Nanda,
Warther (2004), Santos and Tsatsaronis (2002), among others. The current
study extends the literature on underwriting fees, exploring them for a
different market–asset-backed securities.

One of the implications of the current study is related to the
positive relationship between underwriter prestige and underwriter fees.
This suggests that issuers of ABS are charged higher overall
underwriting fees for using a more prestigious underwriter. This result
is inconsistent with the evidence by corporate bonds (Livingston and
Miller, 2000) which finds an inverse relationship between underwriter
prestige and underwriting fees. One potential explanation for the
positive relationship found in ABS is that with relatively rapid growth
in terms of issuance during the sample period the ABS market attracted
newer entrants into the underwriting of ABS. These newer entrants
charged lower overall fees as a means to get into the ABS underwriting
business.

The second
implication

 of the current study pertains to underwriter
loyalty and underwriting fees. This suggests that issuers are
penalized
  
tr.v. pe·nal·ized, pe·nal·iz·ing, pe·nal·iz·es
1. To subject to a penalty, especially for infringement of a law or official regulation. See Synonyms at punish.

2.
 by having higher underwriting fees when they are loyal, i.e., use the
same underwriter, in subsequent issues. This result is consistent with
the findings of Burch, Nanda, and Warther (2004) that provide evidence
of a similar relationship between underwriting fees and loyalty for
corporate bonds using their metric of loyalty. However, using a
different metric to measure loyalty–count–Livingston and Miller (2000)
provide evidence that an inverse relationship exists between loyalty and
underwriting fees for corporate bonds. Unlike corporate bonds in which
the issue of loyalty offers inconsistent results, our results using both
metrics of loyalty yielded the same results for ABS–issuers are
penalized by using the same underwriter.

This study’s primary limitation was the years selected for the
study. Although eight years worth of data was used, it’s certainly
possible the results found during this period may not be applicable to
future periods. Also due to recent events beginning in 2007 during the
sub-prime crisis, issuance in ABS has decreased dramatically. This fact
may further influence fees charged for ABS in the future. Another
limitation of both this study as well as earlier studies is sample
selection bias. Underwriting fees in both the ABS and corporate bond
markets are not always disclosed.

The current study opens the door for additional research in a
number of areas. First, the study provides evidence that there are still
additional research opportunities in the area of underwriting fees.
Prior research on underwriting fees focused primarily on corporate bonds
and equities. This paper extended the literature to include U.S.
asset-backed securities. As the financial markets continue to evolve and
newer security types emerge, it lends itself naturally for the
opportunity to research underwriting fees in these markets. For example,
the market for non-U.S. ABS continues to grow. This may provide
additional research opportunities in the area of underwriting fees.

In addition to underwriting fees, this paper extended the current
literature on ABS. As noted, the current literature on ABS is relatively
limited as compared to other financial markets. There are many
additional opportunities to research whether relationships found to
exist in other financial markets exist in ABS market.

Appendix A: Deal Types This following are deal type used by
Bloomberg to classify ABS deals. Source: Bloomberg.

* ABS: backed by various type loans.

* AUTOS: backed by automobile loans

* BOATS: backed by boat loans

* BU.S.INESS: backed by business equipment loans

* CARDS: backed by credit card receivables

*
CBO

: backed by various bonds

*
CDO

: backed by various debt obligations

* CONSUMERS: backed by various consumer/ personal loans

* CREDIT LINK: backed by credit-linked receivables

* EQUIPMENT: backed by equipment leases/loans

* FILM: backed by film/motion picture receivables

* HEALTHCARE: backed by healthcare receivables

* HOME EQTY: backed by home equity loans

* HOME
IMP

: backed by home improvement loans

* MANUFCT HM: backed by manufactured home loans.

* PLANES: backed by
airplane
  or  heavier-than-air vehicle, mechanically driven and fitted with fixed wings that support it in flight through the dynamic action of the air.
 loans

* RE-SEC: backed by other ABS deals

* RV: backed by loans for recreational vehicles

* STUDENTS: backed by student loans

* TAX LIENS: backed by tax liens

* TRADE: backed by trade receivables

* UTILITY: backed by utility receivables

References

Adelson, M. H. 2003. “CDO and ABS Underperformance: A
Correlation Story.” Journal of Fixed Income 13(3): 53.

Altinkihe, O. and R. Hansen. 2000. “Are there Economies of
Scale in Underwriting Fees? Evidence of Rising
External Financing

 Costs.” Review of Financial Studies 13(1): 191-218.

Ammer, J. and N. Clinton. 2004. “Good News is No News? The
Impact of Credit Rating Changes on the Pricing of Asset-backed
Securities.” International Finance Discussion Papers (809).

Antonov, A. and D. Raevasky. 2003. “Effective Solutions of the
Quasi-Multi Period Model for Large Credit and ABS Portfolios.”
Working Paper.

Bajaj, M., S. Mazumdar, A. Chen, and A. Sarin. 2003.
“Competition in IPO Underwriting: Time Series Evidence.”
EFA

 2003 Annual Conference Paper No. 707.

Barondes, R., A. Butler, and G. Sanger. 2000. “IPO Spreads:
You Get What You Pay For.” Working Paper.

Boot, A. and A. Thakor. 1993. “Security Design.” The
Journal of Finance 48(4): 1349-1378.

Burch, T. R., V. Nanda, and V. Warther. 2004. “Does it Pay to
be Loyal? An Empirical Analysis of Underwriting Relationships and
Fees.” Journal of Financial Economics.

Butler, A. 2007. “Distance Still Matters: Evidence from
Municipal Bond Underwriting.” Review of Financial Studies.

Carter, R. and S. Manaster. 1990. “Initial Public Offerings
and Underwriter Reputation.” Journal of Finance 45(4): 1045-1067.

Carter, R., F. Dark, and A. Singh. 1998. “Underwriter
Reputation, Initial Returns, and the Long-run Performance of IPO
Stocks.” Journal of Finance 53(1): 285-311.

Chen, H.-C. and J. Ritter. 2000. “The Seven Percent
Solution.” Journal of Finance LV(3): 1105-1131.

Childs, P., S. Ott, and T. Riddiough. 1996. “The Pricing of
Multiclass Commercial Mortgage-backed Securities.” Journal of
Financial and
Quantitative Analysis

 31 (4): 581-603.

Cliff, M. and D.
Denis
 king of Portugal: see Diniz.
. 2003. “Do IPO Firms Purchase Analyst
Coverage with Underpricing?” Working Paper.

DeMarzo, P. and D. Duffie. 1999. “A Liquidity-Based Model of
Security Design.”
Econometrica

 67(1): 65-99.

Han, L.-M. and G. C. Lai. 1995. “An Analysis of Securitization
in the Insurance Industry.” The Journal of Risk and Insurance
62(2): 286-296.

Hansen, R. 2001. “Do Investment Banks Compete in IPOS? The
Advent of the ‘7% Plus Contract’.” Journal of Financial
Economics 59:313-346.

Hebb, G. and G. MacKinnon. 2000. “Valuation Uncertainty and
IPO’s: Investment Bank versus Commercial Bank.” Working Paper.

Heidari, M. and L. Wu. 2004. “What Constitutes a Good Model?
An Analysis of Models for Mortgage-Backed Securities.” Working
Paper.

Heitfield, E. and T. Sabarwal. 2004. “What Drives Default and
Prepayment on Subprime Auto Loans?” Journal of Real Estate Finance
and Economics 29(4).

James, C. 1992. “Relationship-Specific Assets and the Pricing
of Underwriter Services.” Journal of Finance 47(5): 1865-1885.

Lacour-Little, M. and G. H. Chun. 1999. “Third Party
Originators and Mortgage Prepayment Risk: An Agency Problem?”
Journal of Real Estate Research 17.

Lester, T., M. Asaria, and U. van der Linden. 2002.
“Securitization: Korea and Taiwan follow Japanese lead.”
International Financial Law Review 21(10): 22.

Livingston, M. and J. J. Jewell. “Split Ratings, Bond Yields,
and Underwriter Spreads for Industrial Bonds.” Journal of Financial
Research 21(2): 185-204.

Livingston, M. and R. Miller. 2000. “Investment Bank
Reputation and the Underwriting of Nonconvertible Debt.” Financial
Management (Summer).

Loughran, T. and J. Ritter. 2002. “Why Don’t Issuers Get
Upset About Leaving Money on the Table in IPOs?” Review of
Financial Studies 15(2): 413-443.

Lucas, D. J., L. S. Goodman, and F. Fabozzi. 2004. “Default
Rates on Structured Finance Securities.” Journal of Fixed Income
14(2): 44.

Park, S. M., M. Han, and Y. Kim. 2002. “Structured
finance.” International Financial Law Review: 85.

Pergamalis, G. 2003. “Greece unveils new securitization
law.” International Financial Law Review: 1.

Ritter, J. 1991. “The Long-Run Performance of Initial Public
Offerings.” Journal of Finance 46(1): 3-27.

Santos, J. and K. Tsatsaronis. 2002. “The Cost of Barriers to
Entry: Evidence from the Market for Corporate Euro Bond
Underwriting.” Working Paper.

Silber, W. L. 1983. “The Process of Financial
Innovation.” American Economic Review 73(2).

Tinic, S. 1988. ”
Anatomy
 , branch of biology concerned with the study of body structure of various organisms, including humans. Comparative anatomy is concerned with the structural differences of plant and animal forms.
 of Initial Public Offerings of Common
Stock.” Journal of Finance 43(4): 789-822.

Torstilla, S. 2001. “What Determines IPO Gross Spreads in
Europe?” European Financial Management 7(4): 523-541.

van Eck, T. H. 1995. “Asset-Backed Securities.” The
Handbook of Fixed Securities: 583.

Yasuda, A. 2003. “Do Bank-Firm Relationships Affect Bank
Competition in the Corporate Bond Underwriting Market?” Working
Paper.

Notes

(1.) We thank Richard Ottoo, Elena
Goldman
   , Emma 1869-1940.

Russian-born American anarchist. Jailed repeatedly for her advocacy of birth control and opposition to military conscription, she was deported to the Soviet Union in 1919.
,
Susan

 Hume, and
Michael
  [Heb.,=who is like God?], archangel prominent in Christian, Jewish, and Muslim traditions. In the Bible and early Jewish literature, Michael is one of the angels of God’s presence.
 
Ehrlich
 , Paul 1854-1915.

German bacteriologist who conducted pioneering research in chemotherapy and developed the chemical Salvarsan as a treatment of syphilis.
 for many useful comments and suggestions. We thank
Maggie

 
Huang
) is a Chinese surname. While Huang is the pinyin romanisation of the word, it may also be romanised as Wong, Vong, Bong, Ng, Uy, Wee, Oi, Oei or Ooi, Ong, Hwang, or Ung due to pronunciations of the word in
 for research assistance. We thank Bloomberg LP for providing the data.
All errors remain our responsibility.

(2.) Another line of literature related to IPOs focused not on the
underwriting fees but the issue of underpricing, when the issue price is
significantly lower than the closing price on the first day or days of
trading. See Ritter (1991), Tinic (1988), Loughran and Ritter (2002) and
Cliff and Denis (2003).

by David Puskar, Bloomberg L.P. E-mail: dpuskar@bloomberg.net

Aron

 A.
Gottesman

,
Lubin School of Business

, Pace University.
E-mail: agottesman@pace.
edu

 

TABLE 1.
Issuance in $ Billions for U.S. corporate bonds and
non-private placed ABS

       U.S. Corporate   U.S. Asset-Backed
Year       Bonds           Securities

2006       1,138               839
2005         866               826
2004         873               673
2003         848               505
2002         669               442
2001         789               365
2000         524               259
1999         529               227

TABLE 2.
Outstanding and new issuance in $ Billions for global ABS

                   2001       2001       2002       2002

Year            Outstanding    New    Outstanding    New

Card               270.1       71        295.1       69.9
Auto               168.9       97.1      188.2      107
Home Equity        184.9       90.7      228.4      121
Manufacturing       52.8        7.2       49.2        4.6
Student             52.4       11.8       67.1       25.7
Other              330.4      106        384.9      119
Total             1059        384       1213        447

                   2003       2003       2004       2004

Year            Outstanding    New    Outstanding    New

Card               307.1       68.1       300.1      53.6
Auto               192.5       95.3     1,115       175.3
Home Equity        277.5      164         342.8     219
Manufacturing       40.4        0.6        35.1       1.2
Student             94.5       37.4       123.7      45.2
Other              466.8      169         691.8     341
Total             1379        535       1,669       742

                   2005        2005        2006         2006

Year            Outstanding     New     Outstanding     New

Card                297.4        67.9       300.2        65
Auto                193.4       115         194.8        92.1
Home Equity         414         258         501.4       199
Manufacturing        30.5         0.7        26.5         4.4
Student             162.9        64.3       199.2        24.6
Other               995.4       527       1,456         267
Total             2,094       1,032       2,678       1,236

TABLE 3.
Non-private placement U.S. ABS by Deal Type in $ Millions

                 1999      2000      2001      2002      2003

ABS               2,007     2,980     8,498     6,131     9,807
AUTOS            61,752    82,345    95,085   105,699    96,013
BOATS             1,285
BU.S.INESS          988       543     1,365       939     2,958
CARDS            40,234    54,533    76,665    71,534    65,395
CBO                           493        70
CDO                           264        46
CMO
CONSUMERS                      27
CREDIT LINK                                        50       275
EQUIPMENT        11,047    13,757     8,095     6,040    12,271
FILM                          179
HLTHCARE            124                 850       700
HOME EQTY        70,247    67,740   132,152   206,161   275,097
HOME IMP            871       290
MANUFCT HM       15,673    10,227    10,987     5,830       604
MBB
MUNICIPAL
N.A.
PLANES            3,455     7,029     7,100       526     3,670
PYMT RIGHTS                             127
RE-SEC              154       417       920    13,163       184
RV                1,950       280     2,932
STUDENTS          9,321    17,131    12,188    23,570    38,175
SWAP TRU.S.T                                      415
TAX LIENS           261       157       278       317        30
TRADE               346       344       110       638       622
UTILITY           7,852     1,000     8,416     1,167       500
Grand Total     227,568   259,735   365,886   442,880   505,603

                 2004      2005      2006     Grand Total

ABS               6,194    10,558    15,843        62,019
AUTOS            80,446   108,503    86,530       716,373
BOATS                                               1,285
BU.S.INESS        2,676     2,652     1,036        13,158
CARDS            53,181    59,084    66,341       486,966
CBO                                                   563
CDO                   5     1,071                   1,386
CMO                                  12,608        12,608
CONSUMERS                                              27
CREDIT LINK         133     2,682       509         3,648
EQUIPMENT         9,374    12,888    14,446        87,919
FILM                                                  179
HLTHCARE                                101         1,775
HOME EQTY       454,507   536,886   568,945     2,311,735
HOME IMP                        0                   1,160
MANUFCT HM          685       589       201        44,797
MBB                                     100           100
MUNICIPAL                     800       800         1,600
N.A.                        1,403                   1,403
PLANES            1,118     2,473     1,120        26,492
PYMT RIGHTS                             200           327
RE-SEC           14,729    12,908                  42,475
RV                  403                             5,565
STUDENTS         48,672    68,224    68,133       285,415
SWAP TRU.S.T                                          415
TAX LIENS            50                  66         1,159
TRADE               350                             2,410
UTILITY             790     5,361     1,922        27,008
Grand Total     673,313   826,081   838,900     4,139,966

TABLE 4.
ABS issuance by top 20 underwriters in $ Millions

                   1999      2000      2001      2002      2003

Lehman             31,302    28,604    35,408    39,570    46,006
Credit Suisse      35,986    24,628    49,186    42,790    52,765
Bank of America    11,084    15,152    48,200    64,807    49,639
Countrywide           440     6,718    11,682    30,406    28,589
JP Morgan           7,639     9,025    45,137    41,213    44,418
Citigroup                                                  33,025
Deutsche Bank       1,553                        32,124    46,094
Merrill Lynch      19,217    14,875     8,296    15,427    18,972
RBS Greenwich                           1,449     6,602    28,896
Morgan Stanley                          1,357    22,440    31,059
Bear Steams        12,310    13,767    24,736    15,740    19,586
Salomon            27,703    37,310    34,521    41,063    19,669
Goldman Sachs      13,548    10,239     5,245     6,759     6,916
Barclays                                2,578     9,047     9,057
Banc One            2,539     1,063    15,377    22,749    29,518
UBS                                               1,930     6,185
Deutsche Bk AB        703    23,784    33,595     3,918
Wachovia              145                         6,059     2,894
CS
Greenwich           7,254     6,323    17,019    19,808       874
Grand Total       227,568   259,735   365,886   442,880   505,603

                   2004      2005      2006     Grand Total

Lehman             65,979    80,829    91,646       419,345
Credit Suisse      65,061    77,118       817       348,350
Bank of America    48,808    52,578    54,225       344,493
Countrywide        82,102    79,022    65,293       304,252
JP Morgan          35,038    43,079    50,547       276,096
Citigroup          74,146    70,529    70,482       248,182
Deutsche Bank      43,335    46,574    45,770       215,450
Merrill Lynch      46,964    43,336    48,206       215,293
RBS Greenwich      51,463    71,151    54,962       214,523
Morgan Stanley     46,519    58,077    48,477       207,928
Bear Steams        31,893    39,253    39,688       196,972
Salomon                                             160,266
Goldman Sachs      19,140    35,235    56,820       153,902
Barclays           13,419    29,086    42,223       105,409
Banc One            6,581                            77,828
UBS                12,587    22,939    19,084        62,725
Deutsche Bk AB                                       62,000
Wachovia           14,691    20,497    11,956        56,242
CS                                     55,600        55,600
Greenwich                                            51,278
Grand Total       673,313   826,081   838,900     4,139,966

TABLE 5.
Average percentage ABS underwriting fees for Top 20 Underwriters

                  1999   2000   2001   2002   2003   2004   2005

Lehman            0.37   0.38   0.39   0.39   0.30   0.29   0.26
Credit Suisse     0.30   0.37   0.30   0.35   0.24   0.25   0.24
Bank of America   0.36   0.30   0.29   0.25   0.27   0.25   0.24
Countrywide       0.25   0.40   0.54   0.44   0.77   0.63   0.70
JP Morgan         0.25   0.25   0.27   0.27   0.27   0.26   0.24
Citigroup                                     0.29   0.27   0.29
Deutsche Bank     0.33                 0.28   0.25   0.25   0.23
Merrill Lynch     0.30   0.33   0.31   0.32   0.39   0.35   0.32
RBS Greenwich                          0.24   0.36   0.28   0.27
Morgan Stanley                  0.29   0.26   0.27   0.24   0.21
Bear Steams       0.30   0.25   0.32   0.27   0.31   0.24
Salomon           0.31   0.30   0.36   0.28   0.30
Goldman Sachs     0.26   0.29   0.25   0.49   0.22   0.36   0.27
Barclays                        0.38   0.21   0.25   0.23   0.24
Banc One          1.15   0.31   0.24   0.24   0.28   0.30
UBS                                           0.30   0.26   0.27
Deutsche Bk AB    0.25   0.27   0.30   0.34
Wachovia                               0.25   0.36   0.30   0.34
CS
Greenwich         0.28   0.44   0.45   0.34   0.25

Grand Total       0.32   0.32   0.33   0.30   0.34   0.35   0.35

                  2006   Total Amount   Avg

Lehman            0.26      419,345     0.34
Credit Suisse               348,350     0.29
Bank of America   0.22      344,493     0.26
Countrywide       0.68      304,252     0.66
JP Morgan         0.22      276,096     0.25
Citigroup         0.29      248,182     0.28
Deutsche Bank     0.27      215,450     0.26
Merrill Lynch     0.38      215,293     0.35
RBS Greenwich     0.24      214,523     0.27
Morgan Stanley    0.22      207,928     0.23
Bear Steams       0.25      196,972     0.29
Salomon                     160,266     0.31
Goldman Sachs     0.25      153,902     0.27
Barclays          0.22      105,409     0.23
Banc One                     77,828     0.29
UBS               0.25       62,725     0.26
Deutsche Bk AB               62,000     0.29
Wachovia          0.26       56,242     0.30
CS                0.24       55,600     0.24
Greenwich                    51,278     0.39

Grand Total       0.34    4,139,966     0.34

TABLE 6.
Dollar issuance of ABS by year and S&P credit rating

               1999      2000      2001      2002      2003

AAA            25,336    35,506    86,631   162,646   250,621
AA+                60        67       522     1,555     3,309
AA                316       389     1,126     3,024     9,816
AA-                26        19       103       211       408
A+                457       375     1,762     1,622     1,885
A                 348       890     3,666     5,909    10,257
A-                                     56       209     1,511
BBB+              800                  12       318     1,959
BBB               171     1,380     2,448     5,563     7,870
BBB-               49        77       149       627     1,211
BB+                         500     3,625       824        22
BB                  6       689       325       723       164
BB-               227        77                 322        84
B+              1,126       570         0       125         0
B                 438        75        86       491       143
B-              1,548       257       711        43
CCC+               14       189        78        63
CCC             2,067     1,796       187       396        39
CCC-              194       470     1,095        85
CC                                    448        15
D               1,775     1,287       653       101
N.A.           32,380    31,730    42,972    57,060    29,123
NR            160,233   183,390   219,233   200,948   187,157
Grand Total   227,568   259,735   365,886   442,880   505,603

               2004      2005      2006     Grand Total

AAA           476,898   620,109   658,438     2,316,186
AA+            11,280    20,144    22,063        58,999
AA             15,932    21,481    23,562        75,646
AA-             3,201     6,731     7,775        18,474
A+              5,033     9,077     8,470        28,681
A              13,622    14,510    11,175        60,376
A-              4,858     5,914     4,255        16,802
BBB+            4,235     5,867     6,187        19,378
BBB             8,246     9,194     9,143        44,015
BBB-            3,105     4,621     1,922        11,761
BB+               201       571     1,091         6,834
BB                219       729     4,259         7,115
BB-                 4         5        59           777
B+                  0         2       262         2,085
B                  24         0     4,754         6,011
B-                                    114         2,673
CCC+                                                344
CCC                12               2,827         7,323
CCC-                                              1,843
CC                                                  463
D                  15                 427         4,258
N.A.           26,549    53,124    42,806       315,745
NR             96,264    52,844    28,463     1,128,531
Grand Total   673,313   826,081   838,900     4,139,966

TABLE 7.
Average ABS percentage underwriting fee by year and S&P credit rating

              1999   2000   2001   2002   2003   2004   2005   2006

AAA           0.36   0.31   0.33   0.28   0.27   0.23   0.22   0.21
AA+           0.63   0.35   0.51   0.36   0.42   0.44   0.32   0.29
AA            0.50   0.40   0.44   0.40   0.42   0.46   0.36   0.36
AA-           0.45   0.58   0.49   0.49   0.43   0.50   0.46   0.44
A+            0.25   0.38   0.35   0.37   0.60   0.54   0.49   0.46
A             0.60   0.47   0.49   0.43   0.47   0.49   0.49   0.49
A-                          0.50   0.47   0.81   0.46   0.61   0.61
BBB+          0.40                 0.63   0.72   0.53   0.66   0.58
BBB           0.61   0.63   0.65   0.54   0.70   0.57   0.58   0.50
BBB-          1.11          0.75   0.59   1.05   0.64   0.67   0.54
BB+                         0.60   0.29          0.25   0.74   0.80
1313                 0.46   0.60   0.38   1.25          0.25   0.60
BB-           0.10   0.63          0.34   0.65                 1.58
13+           0.35                 0.45                        0.50
B             0.33          0.48   0.46                        0.61
B-            0.39   0.45   0.37   0.50
CCC+          0.44   0.18          0.64
CCC           0.44   0.39   0.64   0.56                        0.26
CCC-          0.67   0.34   0.35   0.63
CC                                 0.70
D             0.56   0.73   0.65   0.62                        1.00
N.A.          0.36   0.39   0.37   0.35   0.46   0.29   0.25   0.23
NR            0.28   0.28   0.28   0.23   0.21   0.19   0.15   0.13
Grand Total   0.32   0.32   0.33   0.30   0.34   0.35   0.35   0.34

              Grand Total

AAA              0.24
AA+              0.35
AA               0.39
AA-              0.46
A+               0.49
A                0.48
A-               0.58
BBB+             0.61
BBB              0.58
BBB-             0.67
BB+              0.68
1313             0.58
BB-              0.46
13+              0.37
B                0.60
B-               0.39
CCC+             0.47
CCC              0.32
CCC-             0.42
CC               0.70
D                0.67
N.A.             0.36
NR               0.25
Grand Total      0.34

TABLE 8.
ABS by original weighted average life in $ Millions

               1999      2000      2001      2002      2003

<.5             9,571    14,014    13,755    17,964    16,514
.5<1.5         30,281    32,735    38,727    48,441    56,934
1.5<2.5        29,776    37,723    37,436    56,827    62,948
2.5<3.5        46,903    65,263   125,935   150,672   173,683
3.5<4.5        13,527    10,971    13,943    16,199    16,002
4.5<5.5        17,419    33,622    40,501    35,348    64,363
5.5<6.5         6,368     3,453     4,578     7,754    16,704
6.5<7.5         8,578    11,729    14,925    10,593    11,960
7.5<8.5         1,437     1,355     1,211     3,461     5,235
.8.5<9.5        2,477     1,779     3,632     1,788     1,237
9.5<10.5        2,665     2,911     3,767     3,697     5,346
10.5<11.5       1,558     2,043     2,494     2,031     3,521
11.5<12.5       1,201     1,417       910     1,122     1,205
12.5<13.5         293       824       951       340       452
13.5<14.5         281       492       409        26       698
14.5<15.5         341         5        13       170
15.5<16.5           4         4         0
16.5<17.5          17                  15       966       403
17.5<18.5           8         7                            95
18.5<19.5                             270                  11
19.5<20.5                             380        60
20.5<21.5                                                  20
25.5<26.5                              52
26.5<27.5                              32
28.5<29.5
29.5<30.5
N.A.           54,863    39,388    61,949    85,421    68,270
Grand Total   227,568   259,735   365,886   442,880   505,603

               2004      2005      2006     Grand Total

<.5            15,344    22,481    24,197      133,841
.5<1.5         89,052   158,837   193,907      648,914
1.5<2.5        91,820   131,865   156,263      604,658
2.5<3.5       224,989   214,266   143,196    1,144,906
3.5<4.5        17,552    24,292    47,769      160,255
4.5<5.5        55,255    93,836    96,417      436,761
5.5<6.5        21,426    15,867    15,639       91,788
6.5<7.5        25,358    30,649    27,340      141,133
7.5<8.5         6,062    16,008    15,462       50,232
.8.5<9.5        3,348     5,232    10,001       29,494
9.5<10.5        7,392    11,733    10,439       47,950
10.5<11.5       3,752     1,804     3,077       20,280
11.5<12.5       1,324     3,427     2,627       13,234
12.5<13.5         113     1,939     1,513        6,427
13.5<14.5         508     2,121     3,621        8,156
14.5<15.5       1,513     3,556     7,257       12,854
15.5<16.5         207     1,999       703        2,917
16.5<17.5         578       749       170        2,898
17.5<18.5         188       989       631        1,919
18.5<19.5                             550          832
19.5<20.5                   100                    540
20.5<21.5                                           20
25.5<26.5                                           52
26.5<27.5                                           32
28.5<29.5                   676                    676
29.5<30.5                     0                      0
N.A.          107,532    83,654    78,119      579,196
Grand Total   673,313   826,081   838,900    4,139,966

TABLE 9.
Average ABS percentage underwriting fee by original weighted average
life

              1999   2000   2001   2002   2003   2004   2005   2006

<.5           0.14   0.13   0.13   0.13   0.12   0.12   0.12   0.12
.5<1.5        0.19   0.18   0.18   0.18   0.18   0.18   0.18   0.18
1.5<2.5       0.26   0.27   0.25   0.27   0.28   0.26   0.21   0.25
2.5<3.5       0.29   0.27   0.26   0.27   0.26   0.24   0.22   0.20
3.5<4.5       0.34   0.39   0.52   0.41   0.81   0.61   0.68   0.32
4.5<5.5       0.38   0.38   0.41   0.40   0.47   0.53   0.44   0.47
5.5<6.5       0.44   0.61   0.59   0.45   0.48   0.27   0.47   0.41
6.5<7.5       0.48   0.42   0.45   0.37   0.37   0.53   0.46   0.43
7.5<8.5       0.59   0.34   0.41   0.29   0.32   0.25   0.25   0.24
8.5<9.5       0.45   0.56   0.38   0.39   0.41   0.26   0.24   0.25
9.5<10.5      0.51   0.56   0.54   0.51   0.44   0.37   0.32   0.33
10.5<11.5     0.43   0.48   0.46   0.44   0.38   0.36   0.36   0.34
11.5<12.5     0.49   0.44   0.56   0.59   0.38   0.33   0.31   0.30
12.5<13.5     0.40   0.47   0.59   0.48   0.29   0.41   0.31   0.46
13.5<14.5     0.45   0.50   0.41          0.34   0.33   0.32   0.31
14.5<15.5     0.50   0.50          0.73          0.48   0.37   0.25
15.5<16.5                                               0.27   0.24
16.5<17.5     0.42          0.50   0.35   0.24   0.35   0.37   0.33
17.5<18.5                                 0.20   0.33   0.31   0.33
18.5<19.5                   0.25                               0.31
19.5<20.5                          0.40                 0.65
20.5<21.5
25.5<26.5                   0.25
26.5<27.5
28.5<29.5
29.5<30.5
N.A.          0.34   0.31   0.29   0.26   0.29   0.28   0.26   0.26
Grand Total   0.32   0.32   0.33   0.30   0.34   0.35   0.35   0.34

              Grand Total

<.5              0.13
.5<1.5           0.18
1.5<2.5          0.25
2.5<3.5          0.25
3.5<4.5          0.51
4.5<5.5          0.46
5.5<6.5          0.43
6.5<7.5          0.46
7.5<8.5          0.30
8.5<9.5          0.33
9.5<10.5         0.44
10.5<11.5        0.40
11.5<12.5        0.39
12.5<13.5        0.41
13.5<14.5        0.35
14.5<15.5        0.38
15.5<16.5        0.26
16.5<17.5        0.33
17.5<18.5        0.30
18.5<19.5        0.29
19.5<20.5        0.53
20.5<21.5
25.5<26.5        0.25
26.5<27.5
28.5<29.5
29.5<30.5
N.A.             0.29
Grand Total      0.34

TABLE 10.
Underwriting fees--full period analysis

Independent Variable          US               US

Intercept                  0.70055 **       0.767986 **
                          -8.388164       (10.35501)
AAA                       -0.023476 **     -0.018663 **
                         (-2.594656)      (-2.132015)
Mezzanine                  0.075074 **      0.076529 **
                          (6.170144)       (6.658604)
Subordinated               0.197144 **      0.187523 **
                         (15.77486)       (15.81513)
Deal Amount                0.022627 **      0.024246 **
                          (4.857101)       (5.630582)
Auto                       0.017201
                          (1.340346)
Credit Card               -0.007355
                         (-0.45365)
Home Equity                0.028464
                          (2.414706)
Student Loan               0.015939
                          (0.945704)
Par Amount                -0.059351 **     -0.063594 **
                        (-15.47095)      (-18.49084)
Prestige                   1.455502 **      1.452287 **
                         (14.03731)       (14.02468)
WAL                        0.008641 **      0.007966 **
                          (6.500939)       (6.891374)
Loyalty                    0.148386 **      0.148779 **
                         (25.23054)       (25.83672)
Count

1999                       0.0103           0.006956
                          (0.630703)       (0.433347)
2000                       0.037779 **      0.033018 **
                          (2.825256)       (2.502031)
2001                       0.031504 **      0.027193 **
                          (2.429743)       (2.119839)
2002                       0.010687         0.007649
                          (0.905397)       (0.652873)
2003                       0.044663 **      0.041355 **
                          (4.030266)       (3.771524)
2004                       0.00915          0.009419
                          (1.001979)       (1.032641)
2005                       0.008753         0.008815
                          (1.06862)        (1.077612)
# of Observations            9204             9209
Adjusted R-squared         0.305714         0.30517

Independent Variable          US               US

Intercept                  0.660729 **      0.486393 **
                          (9.024349)       (7.41368)
AAA                       -0.014146        -0.023758 **
                         (-1.796523)      (-3.100573)
Mezzanine                  0.098982 **      0.083624 **
                          (9.237869)       (8.245917)
Subordinated               0.215614 **      0.222635 **
                         (19.82468)       (21.48809)
Deal Amount                0.014717 **      0.017696 **
                          (3.590823)       (4.670519)
Auto                      -0.018403
                         (-1.773387)
Credit Card               -0.01667
                         (-1.203752)
Home Equity               -0.062119 **
                         (-6.324422)
Student Loan              -0.033503 **
                         (-2.355085)
Par Amount                -0.043233 **     -0.039234 **
                        (-12.72165)      (-12.68719)
Prestige                   0.179105 *       0.238656 **
                          (1.904005)       (2.542794)
WAL                        0.011605 **      0.011838 **
                         (10.03838)       (11.6708)
Loyalty

Count                      0.000186 **      0.000178 **
                         (46.54663)       (46.38599)
1999                       0.083306 **      0.094771 **
                          (6.807137)       (7.910568)
2000                       0.061172 **      0.074281 **
                          (5.257324)       (6.462479)
2001                       0.055068 **      0.068142 **
                          (4.834924)       (6.056174)
2002                       0.035127 **      0.043721 **
                          (3.361887)       (4.209006)
2003                       0.053801 **      0.061949 **
                          (5.408508)       (6.276172)
2004                       0.010349         0.010769
                          (1.259876)       (1.309623)
2005                       0.013856         0.014312 *
                          (1.874233)       (1.934175)
# of Observations            9989             9994
Adjusted R-squared         0.38761          0.38463

* Significant at the .10 level

** Significant at the .05 level

The above table shows the regression of underwriter fee (the
dependent variable in basis points) on a number  of independent
variables that affect the marketability of ABS. The sample includes
non/private placed US ABS from  1999/2006. AAA is a dummy variable
assigned 1 if bond is rated AAA, zero otherwise. The variables
Mezzanine  Bond and Subordinated Bond are dummy variables assigned 1
if bond includes a class descriptor of mezzanine or  subordinated
respectively, zero otherwise. Deal Amount is the log of deal amount/
/dollar amount of entire deal of which  the specific class/bond is
included in. Dummy variables Auto, Credit Card, Home equity and
Student Loan are variables  assigned 1 if deal is of given type,
zero otherwise. Dummy variables are assigned for each year included
in the study. Par  amount is the log of par amount for the bond.
Prestige is the proportion of market share that the underwriter had
over the  study period. WAL is weighted average life of the bond at
issuance and serves as a measure of maturity/term of bond.  Loyalty
is a dummy variable assigned 1 if underwriter used in current bond
also served as underwriter in previous  issuance. Count is defined
as the number of bond underwritten by the same underwriter for a
given issuer of ABS during  the sample period.

TABLE 11.
Underwriting Fees and Loyalty including Deal Type--Year by Year
Analysis

Year                 1999           2000           2001

Independent
Variable              US             US             US

Intercept          0.381 ***      0.595 **       0.5787 ***
                  (3.0300)       (2.3977)       (3.8764)
AAA               -0.0454 ***     0.0243         0.0059
                 (-2.7636)       (0.8895)       (0.4234)
Mezzanine          0.1733 ***     0.1726 ***     0.0897 ***
                 (10.6514)       (5.4976)       (4.6866)
Subordinated       0.2072 ***     0.1873 ***     0.1425 ***
                 (11.7161)       (5.8756)       (7.2838)
Deal Amount       -0.0099         0.0193         0.0063
                 (-1.2876)       (1.3243)       (0.7475)
Auto              -0.0205         0.0234        -0.0351
                 (-1.4486)       (0.8813)      (-1.5035)
Cards             -0.1112 ***    -0.0253        -0.0706 ***
                 (-5.5595)      (-0.7686)      (-2.8384)
Home Equity        0.0279 **      0.0514 **     -0.015
                  (2.0665)       (1.9854)      (-0.735)
Students          -0.0604 **      0.0036        -0.1009 ***
                 (-1.9939)       (0.0727)      (-3.1404)
Par Amount        -0.0009        -0.0449 ***    -0.0279 ***
                 (-0.1394)      (-3.9149)      (-3.7232)
Prestige           0.1256         0.4606 *       0.1771 *
                  (1.4057)       (1.9346)       (1.68)
WAL                0.0265 ***     0.0116 ***     0.0198 ***
                 (12.3228)       (3.818)        (8.1437)
Loyalty            0.0141         0.0124         0.0373 ***
                  (1.5017)       (0.6867)       (3.3301)
# Observations       628            670            706
Adjusted           0.6221         0.3899         0.4827
R-squared

Year                 2002           2003           2004

Independent
Variable              US             US             US

Intercept          0.9645 ***     0.5389 ***     0.5662 ***
                  (3.5776)       (2.7018)       (2.7822)
AAA                0.0108        -0.0718 ***    -0.056 *
                  (0.4968)      (-3.4231)      (-1.6713)
Mezzanine          0.0461         0.0363        -0.011
                  (1.178)        (1.2441)      (-0.2658)
Subordinated       0.1324 ***     0.2849 ***     0.2255 ***
                  (3.8285)       (9.5392)       (5.2834)
Deal Amount        0.0455 ***     0.0185 *       0.0565 ***
                  (3.3139)       (1.6628)       (4.4672)
Auto               0.015          0.0478         0.0368
                  (0.383)        (1.2064)       (0.8361)
Cards              0.0797         0.0264         0.0603
                  (1.6933)       (0.543)        (1.1175)
Home Equity        0.1008 ***     0.091 **       0.0303
                  (2.479)        (2.3927)       (0.741)
Students          -0.0158         0.0997 **      0.0609
                 (-0.3121)       (2.0163)       (1.1717)
Par Amount        -0.0948 ***    -0.0433 ***    -0.091 ***
                 (-8.2542)      (-5.4224)      (-9.1559)
Prestige           0.5874 **     -0.3257         1.9272 ***
                  (2.3515)      (-1.2408)      (10.4074)
WAL                0.0072         0.0117 ***    -0.0006
                  (1.6199)       (3.184)       (-0.1801)
Loyalty            0.0879 ***     0.2316 ***     0.2036 ***
                  (4.4802)      (17.1008)      (13.5823)
# Observations       864            1551           2255
Adjusted           0.3733         0.3856         0.3307
R-squared

Year                 2005

Independent
Variable              US

Intercept          1.0553 ***
                  (4.1878)
AAA                0.0122
                  (0.312)
Mezzanine          0.0957 **
                  (2.111)
Subordinated       0.1701 ***
                  (3.6019)
Deal Amount        0.0411 ***
                  (3.0294)
Auto               0.0016
                  (0.0341)
Cards              0.0041
                  (0.0683)
Home Equity       -0.0938 **
                 (-2.1067)
Students           0.051
                  (0.9717)
Par Amount        -0.1024 ***
                 (-9.3927)
Prestige           3.0328 ***
                 (10.0582)
WAL               -0.0009
                 (-0.2471)
Loyalty            0.1949 ***
                 (12.481)
# Observations       1642
Adjusted           0.3451
R-squared

 * Significant at the .10 level

 ** Significant at the .05 level

 *** Significant at the .01 level

The above table shows the regression of underwriter fee (the
dependent variable in basis points) on a number of  independent
variables that affect the marketability of ABS. AAA is a dummy
variable assigned 1 if bond is rated  AAA, zero otherwise. The
variables Mezzanine Bond and Subordinated Bond are dummy variables
assigned 1 if bond  includes a class descriptor of mezzanine or
subordinated respectively, zero otherwise. Deal Amount is the log of
deal  amount//dollar amount of entire deal of which the specific
class/bond is included in. Dummy variables Auto, Credit  Card, Home
equity and Student Loan are variables assigned 1 if deal is of given
type, zero otherwise. Dummy variables  are assigned for each year
included in the study. Par amount is the log of par amount for the
bond. Prestige is the  proportion of market share that the
underwriter had during the Year listed. For example Year 1999 table
represents 1999  Prestige values applied to 2001 issuance. WAL is
weighted average life of the bond at issuance and serves as a
measure of maturity/term of bond. Loyalty is a dummy variable
assigned 1 if underwriter used in current bond also  served as
underwriter in previous issuance.

TABLE 12.
Underwriting Fees and Loyalty excluding Deal Type--Year by Year
Analysis

Year                 1999           2000           2001

Independent
Variable              US             US             US

Intercept          0.776 ***      0.8731 ***     0.813 ***
                  (7.4818)       (4.3988)       (6.6326)
AAA               -0.0392 **      0.0283         0.0024
                 (-2.3351)       (1.0615)       (0.181)
Mezzanine          0.1608 ***     0.1621 ***     0.0913 ***
                  (9.7347)       (5.4044)       (5.0299)
Subordinated       0.1561 ***     0.1582 ***     0.1201 ***
                  (9.488)        (5.7418)       (6.9235)
Deal Amount       -0.0131 *       0.0186         0.004
                 (-1.8087)       (1.4274)       (0.5658)
Par Amount        -0.019 ***     -0.0567 ***    -0.0391 ***
                 (-3.249)       (-5.7488)      (-6.3947)
Prestige           0.1721 *       0.2348         0.1895 *
                  (1.9495)       (1.10914)      (1.866)
WAL                0.0245 ***     0.0103 ***     0.0163 ***
                 (11.8112)       (3.5949)       (7.8303)
Loyalty            0.0117         0.0036         0.0423 ***
                  (1.2089)       (0.2059)       (3.9883)
# Observations       628            670            706
Adjusted           0.5943         0.3874         0.474
  R-squared

Year                 2002           2003           2004

Independent
Variable              US             US             US

Intercept          1.3387 ***     0.6268 ***     0.5274 ***
                  (5.4417)       (3.478)        (2.9357)
AAA                0.0102        -0.0598 ***    -0.0614 **
                  (0.4802)      (-2.9174)      (-1.9941)
Mezzanine          0.1056 ***     0.0458 *      -0.0145
                  (2.9432)       (1.6626)      (-0.3809)
Subordinated       0.1532 ***     0.2699 ***     0.2291 ***
                  (4.7274)       (9.3143)       (5.4949)
Deal Amount        0.0248 **      0.0222 **      0.0559 ***
                  (2.0579)       (2.17)         (4.7705)
Par Amount        -0.0901 ***    -0.0483 ***    -0.0865 ***
                 (-8.8059)      (-6.6109)      (-9.8189)
Prestige           0.4731 *      -0.4718 *       1.9144 ***
                  (1.9208)      (-1.8363)      (10.4759)
WAL                0.0075 **      0.0132 ***     0.0015
                  (2.0513)       (4.145)        (0.5513)
Loyalty            0.1111 ***     0.2318 ***     0.2037 ***
                  (5.9494)      (17.512)       (13.8607)
# Observations       864            1551           2260
Adjusted           0.3659         0.3829         0.3311
  R-squared

Year                 2005

Independent
Variable              US

Intercept          0.5295 **
                  (2.3573)
AAA               -0.0291
                 (-0.767)
Mezzanine          0.0422
                  (0.9649)
Subordinated       0.1715 ***
                  (3.6478)
Deal Amount        0.0517 ***
                  (4.0184)
Par Amount        -0.0864 ***
                 (-8.6769)
Prestige           2.7686 ***
                  (9.3056)
WAL                0.004
                  (1.3611)
Loyalty            0.184 ***
                 (11.9804)
# Observations       1642
Adjusted           0.3374
  R-squared

* Significant at the .10 level

** Significant at the .05 level

*** Significant at the .01 level

The above table shows the regression of underwriter fee (the
dependent variable in basis points) on a number of independent
variables that affect the marketability of ABS. AAA is a dummy
variable assigned 1 if bond is rated AAA, zero otherwise. The
variables Mezzanine Bond and Subordinated Bond are dummy variables
assigned 1 if bond includes a class descriptor of mezzanine or
subordinated respectively, zero otherwise. Deal Amount is the log
of deal amount--dollar amount of entire deal of which the specific
class/bond is included in. Par amount is the log of par amount for
the bond. Prestige is the proportion of market share that the
underwriter had during the Year listed. For example Year 1999
table represents 1999 Prestige values applied to 2000 issuance.
WAL is weighted average life of the bond at issuance and serves
as a measure of maturity/term of bond. Loyalty is a dummy variable
assigned 1 if underwriter used in current bond also served as
underwriter in previous issuance.

TABLE 13.
Underwriting Fees and Count including Deal Type--Year by Year Analysis

Year                    1999           2000           2001

Independent
Variable                 US             US             US

Intercept             0.5092 ***     0.5126 **      0.5502 ***
                     (4.3688)       (2.4653)       (3.9818)
AAA                  -0.0424 ***     0.066 ***      0.0169
                    (-2.7811)       (2.7901)       (1.3666)
Mezzanine             0.1751 ***     0.1681 ***     0.1083 ***
                    (11.3598)       (5.7954)       (5.7537)
Subordinated          0.2011 ***     0.1872 ***     0.1747 ***
                    (12.7849)       (6.3971)       (9.2661)
Deal Amount          -0.014 *        0.017         -0.0007
                    (-1.9533)       (1.3031)      (-0.0845)
Auto                 -0.0227 *       0.0006        -0.0617 ***
                    (-1.8865)       (0.0266)      (-3.0835)
Credit Card          -0.1074 ***    -0.0489        -0.1072 ***
                    (-5.8878)      (-1.6454)      (-4.9552)
Home Equity           0.0194         0.0066        -0.0612 ***
                     (1.5752)       (0.2829)      (-3.4005)
Student Loan         -0.0582 **     -0.043         -0.1529 ***
                    (-1.9992)      (-0.9291)      (-5.7023)
Par Amount           -0.0029        -0.0377 ***    -0.0176 **
                    (-0.4993)      (-3.582)       (-2.458)
Prestige              0.0967         0.5788 ***     0.2155 **
                     (1.1956)       (2.8451)       (2.1584)
WAL                   0.0265 ***     0.0136 ***     0.0219 ***
                    (13.0862)       (4.9388)      (10.0958)
Count                 0              0.0001 ***     0 ***
                     (0.3328)       (4.4085)       (5.0457)
# of Observations       729            749            776
Adjusted              0.6256         0.4033         0.5117
  R-squared

Year                    2002           2003           2004

Independent
Variable                 US             US             US

Intercept             0.88 ***       0.4112 **      0.3121
                     (3.7126)       (2.258)        (1.6191)
AAA                   0.0078        -0.0622 ***    -0.0503
                     (0.4089)      (-3.2573)      (-1.6149)
Mezzanine             0.0235         0.05 *         0.0142
                     (0.6837)       (1.8708)       (0.3681)
Subordinated          0.142 ***      0.3033 ***     0.2253 ***
                     (4.6458)      (11.076)        (5.6633)
Deal Amount           0.0397 ***     0.0161         0.0656 ***
                     (3.2617)       (1.588)        (5.5188)
Auto                  0.0061         0.0368        -0.0447
                     (0.1784)       (1.0554)      (-1.1087)
Credit Card           0.0697 *       0.0473         0.0635
                     (1.7197)       (1.0931)       (1.2681)
Home Equity          -0.0249        -0.0124        -0.0888 **
                    (-0.6947)      (-0.3688)      (-2.3508)
Student Loan         -0.0503         0.0179         0.0139
                    (-1.1416)       (0.4179)       (0.2908)
Par Amount           -0.0805 ***    -0.03 ***      -0.077 ***
                    (-7.9165)      (-4.0769)      (-8.2489)
Prestige             -0.0972        -0.3943 *       0.4623 **
                    (-0.4402)      (-1.6556)       (2.3481)
WAL                   0.0124 ***     0.013 ***     -0.0003
                     (3.1668)       (3.9529)      (-0.0895)
Count                 0.0002 ***     0.0002 ***     0.0002 ***
                    (14.7364)      (24.3328)      (21.0229)
# of Observations       905            1596           2324
Adjusted              0.4761         0.4675         0.3895
  R-squared

Year                    2005

Independent
Variable                 US

Intercept             0.8957 ***
                     (3.8435)
AAA                   0.0086
                     (0.2378)
Mezzanine             0.079 *
                     (1.881)
Subordinated          0.1735 ***
                     (3.9694)
Deal Amount           0.0543 ***
                     (4.2914)
Auto                  0.0102
                     (0.2379)
Credit Card           0.1186 **
                     (2.2324)
Home Equity          -0.0983 **
                    (-2.491)
Student Loan          0.055
                     (1.1644)
Par Amount           -0.0979 ***
                    (-9.7265)
Prestige              0.021
                     (0.0645)
WAL                  -0.0032
                    (-0.9874)
Count                 0.0002 ***
                    (20.2979)
# of Observations       1675
Adjusted              0.4197
  R-squared

* Significant at the .10 level

** Significant at the .05 level

*** Significant at the .01 level

The above table shows the regression of underwriter fee (the
dependent variable in basis points) on a number  of independent
variables that affect the marketability of ABS. AAA is a dummy
variable assigned 1 if bond is  rated AAA, zero otherwise. The
variables Mezzanine Bond and Subordinated Bond are dummy variables
assigned  1 if bond includes a class descriptor of mezzanine or
subordinated respectively, zero otherwise. Deal Amount is the  log
of deal amount//dollar amount of entire deal of which the specific
class/bond is included in. Dummy variables  Auto, Credit Card, Home
equity and Student Loan are variables assigned 1 if deal is of given
type, zero otherwise.  Dummy variables are assigned for each year
included in the study. Par amount is the log of par amount for the
bond.  Prestige is the proportion of market share that the
underwriter had during the Year listed. For example Year 1999 table
represents 1999 Prestige values applied to 2000 issuance. WAL is
weighted average life of the bond at issuance and  serves as a
measure of maturity/term of bond. Count is defined as the number of
bond underwritten by the same  underwriter for a given issuer of ABS
during the sample period.

TABLE 14.
Underwriting Fees and Count excluding Deal Type--Year by Year Analysis

Year                 1999           2000           2001

Independent
Variable              US             US             US

Intercept          0.8362 ***     0.6106 ***     0.725 ***
                  (8.6112)       (3.5232)       (6.2007)
AAA               -0.0362 **      0.0692 ***     0.0086
                 (-2.347)        (3.0259)       (0.7068)
Mezzanine          0.1646 **      0.1536 ***     0.0976 ***
                 (10.5934)       (5.574)        (5.5345)
Subordinated       0.158 **       0.1597 ***     0.1452 ***
                 (10.7407)       (6.2392)       (8.5943)
Deal Amount       -0.0163 **      0.0218 *      -0.0011
                 (-2.4282)       (1.8815)      (-0.1628)
Par Amount        -0.0186 ***    -0.048 ***     -0.0294 ***
                 (-3.462)       (-5.2568)      (-4.7982)
Prestige           0.1603 **      0.552 ***      0.2381 **
                  (1.9744)       (2.9104)       (2.3556)
WAL                0.0244 ***     0.0128 ***     0.0195 ***
                 (12.52)         (4.9128)      (10.3799)
Count              0              0.0001 ***     0 ***
                  (1.2206)       (4.633)        (4.8798)
# Observations       729            749            776
Adjusted           0.6035         0.4028         0.4875
  R-squared

Year                 2002           2003           2004

Independent
Variable              US             US             US

Intercept          0.8542 ***     0.3017 *       0.1127
                  (3.9522)       (1.8301)       (0.6324)
AAA               -0.0066        -0.0741 ***    -0.0861 ***
                 (-0.3527)      (-3.9783)      (-2.973)
Mezzanine          0.0324         0.0325        -0.0135
                  (1.0195)       (1.2978)      (-0.3775)
Subordinated       0.1725 ***     0.3096 ***     0.2455 ***
                  (6.0282)      (11.6756)       (6.2927)
Deal Amount        0.0292 ***     0.018 *        0.0565 ***
                  (2.777)        (1.9307)       (5.1285)
Par Amount        -0.0677 ***    -0.0256 ***    -0.0589 ***
                 (-7.3879)      (-3.77)        (-7.0436)
Prestige          -0.0014        -0.2934         0.4624 **
                 (-0.0064)      (-1.2527)       (2.3487)
WAL                0.0115 ***     0.0129 ***     0.0054 **
                  (3.5604)       (4.5219)       (2.0966)
Count              0.0002 ***     0.0002 ***     0.0002 ***
                 (15.4522)      (24.7537)      (20.6272)
# Observations       905            1596           2329
Adjusted           0.4713         0.4662         0.3843
  R-squared

Year                 2005

Independent
Variable              US

Intercept          0.3469 *
                  (1.6707)
AAA               -0.0435
                 (-1.228)
Mezzanine          0.031
                  (0.7597)
Subordinated       0.1909 ***
                  (4.3706)
Deal Amount        0.0568 ***
                  (4.7382)
Par Amount        -0.0716 ***
                 (-7.7467)
Prestige          -0.1615
                 (-0.4926)
WAL                0.0027
                  (0.9771)
Count              0.0002 ***
                 (19.3452)
# Observations       1675
Adjusted           0.4059
  R-squared

* Significant at the .10 level

** Significant at the .05 level

*** Significant at the .01 level

The above table shows the regression of underwriter fee (the
dependent variable in basis points) on a number  of independent
variables that affect the marketability of ABS. AAA is a dummy
variable assigned 1 if bond  is rated AAA, zero otherwise. The
variables Mezzanine Bond and Subordinated Bond are dummy variables
assigned 1 if bond includes a class descriptor of mezzanine or
subordinated respectively, zero otherwise. Deal  Amount is the log
of deal amount//dollar amount of entire deal of which the specific
class/bond is included in.  Dummy variables are assigned for each
year included in the study. Par amount is the log of par amount for
the  bond. Prestige is the proportion of market share that the
underwriter had during the Year listed. For example  Year 1999 table
represents 1999 Prestige values applied to 2000 issuance. WAL is
weighted average life of the  bond at issuance and serves as a
measure of maturity/term of bond. Count is defined as the number of
bond  underwritten by the same underwriter for a given issuer of ABS
during the sample period.