Do Multiple Credit Ratings Signal
Complexity ? Evidence from the
European Triple-A Structured Finance
Securities
March 2014
Frank J. Fabozzi
EDHEC-Risk Institute
Mike E. Nawas
Bishopsfield Capital Partners
Dennis Vink
Nyenrode Business Universiteit
Abstract
In much of the current research on market practices with respect to the use of credit ratings,
the rating shopping hypothesis and the information production hypothesis feature prominently.
Both of these hypotheses predict an inverse relationship between the number of ratings and a
security’s funding cost; that is, more ratings will reduce funding costs and, conversely, fewer
ratings will increase funding costs. Our study finds precisely the opposite to have been the case
for the mainstay of the structured finance securities market in Europe prior to 2007, namely
the triple-A tranches of European residential mortgage-backed securities. Our findings suggest
that structured finance markets may behave differently than what would be predicted by two
hypotheses traditionally used to explain the number of ratings and funding costs: the rating
shopping and information production hypotheses. Obtaining multiple credit ratings may be a
signal for complexity, for which investors demand a risk premium.
JEL Classifications: G12; G24; L11.
Keywords: Credit ratings, regulation, mortgage-backed securities.
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Copyright © 2015 EDHEC
1. Introduction
The recent global financial crisis that began with the subprime mortgage problem in the United
States in the summer of 2007 has called for a closer examination of market practices with respect
to the use of ratings assigned to structured products assigned by credit rating agencies (CRAs).
This has led to a well-established body of research in which two hypotheses feature prominently.
The first is the “rating shopping” hypothesis, which asserts that underwriters of securities may
shop for the most favourable (combination of) ratings in order to minimise funding cost. The
second body of research involves the “information production” hypothesis, which puts forward
the notion that funding costs can be minimised by increasing the number of credit ratings on a
given security because this adds to the information available to potential investors in the security.
These two hypotheses both predict an inverse relationship between the number of ratings and
a security’s funding cost; that is, more ratings will reduce funding cost and, conversely, fewer
ratings will increase funding cost.
Regulatory and supervisory bodies responsible for overseeing financial markets have picked up
on these research findings. In Europe, where public concern about the social utility and risks of
structured products in particular has been intense and widespread, European Union (EU) policymakers have adopted new regulations aimed at increasing the information content of structured
finance securities.1 Under these new rules, issuers are required to engage at least two CRAs to rate
structured finance securities. The reason that authorities singled out structured finance securities
for requiring more than one rating is that due to their complexity, they see a need to provide
investors with more information on these securities.2
In this paper, we first test whether prior to the 2007 crisis the inverse relationship between
the number of ratings and funding cost that would be predicted by the rating shopping and
information production hypotheses was empirically valid for the mainstay of the European
structured finance market: the triple-A rated portion of residential mortgaged-backed securities
(RMBS). We find that the predicted relationship by these two hypotheses is not supported. In fact,
we find the opposite: for our sample we find evidence that the greater the number of ratings, the
higher the funding costs.
We suggest that our empirical findings may be explained by the complexity of the securities: in
a market such as the European RMBS market there is a mix of relatively complex and relatively
straightforward securities, and issuers may only find it necessary to obtain more than one triple-A
rating for the more complex securities in order to issue securities at the lowest possible interest
cost. In this view, the number of triple-A ratings obtained for a structured finance security is a
signal for the complexity of the security because only securities with relative complexity warrant
incurring the costs of engaging multiple CRAs. This is because with a fewer number of ratings the
underwriters may struggle to find an adequate universe of investors to purchase the securities. In
this view, investors perceive the number of triple-A ratings as a signal for complexity, for which
they demand a risk premium.
We test our “complexity hypothesis” in this paper in a number of ways. First, we test whether
for structured finance securities funding costs are higher when they are rated triple-A by two
or three CRAs rather than by one. Second, we test whether structured finance securities with
complex features have more ratings than those without the complex features. Consequently,
this paper provides two main contributions to the research on market practices with respect
to the use of credit ratings. The first one is that in the European structured finance market,
more ratings are associated with a higher funding cost. This contribution has global implications
as it challenges the commonly held view that multiple ratings are obtained to decrease the
funding cost (e.g. Skreta and Veldkamp (2009), Bongaerts, Cremers, and Goetzmann, (2012), and
He, Qian, and Strahan (2012)). Our second contribution is that our results provide evidence that
1 - Regulation (EU) No 462/2013 of the European Parliament and of the Council of 21 May 2013 amending Regulation (EC) No 1060/2009 on credit rating agencies (CRA III).
2 - EU Press Release MEMO/13/571, 18 June 2013.
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the complexity of structured finance securities is a driver for issuers to obtain a higher number
of credit ratings, and, vice versa, that tranches with less complexity are associated with a lower
number of obtained ratings. Both contributions have ramifications for the rating shopping view
in that the rating shopping hypothesis would predict that securities with one or two triple-A
ratings would have higher funding costs than securities with three triple-A ratings, to reflect the
risk that in such cases an issuer may have shopped for the one or two particular CRAs who were
prepared to assign a triple-A rating to the security and being unable to convince all three CRAs
to do so.
The results also relate and contribute to a several issues in the literature regarding the effects
of complexity on the pricing of structured finance securities. Furfine (2014) finds that the
underwriter effectively determines a deal’s complexity and shows that loan performance is worse
for loans packaged in more complex securitisations compared to less complex securitisations. In
this respect, our results suggest that the issuer chooses more credit ratings for more complex
deals. Interestingly, Bas-Isaac and Shapiro (2013) suggest that more complex deals imply a
reduced accuracy of credit ratings on structured finance products in the period just preceding
the financial crisis. Focusing on the actual credit performance of tranches, Griffin and Tang
(2012) call into question the traditional rating shopping view and argue that tranches rated by
multiple agencies actually perform worse than deals that are rated by one rating agency. Our
empirical findings shed some light on the discussion, as they suggest that investors are aware
of additional risks associated with multiple credit ratings. Our results imply that investors take
into account the risk associated with more complex deals and perceive the number of ratings
obtained by the issuer as a signal for complexity and demand a risk premium.
For policymakers, our empirical results open up the possibility that the EU requirement of two
credit ratings for structured finance tranches, which came into force on 20 June 2013, may
lack effectiveness, as it does not focus on structured finance securities with complex features.
Policymakers should be cognizant of the risk that a requirement of a minimum number of ratings
may reduce the information content on the complexity of a security that could otherwise (i.e.
without the requirement) be signalled by the number of ratings. The European Commission will
revisit the mandate by 1 January 2016. That review will be followed by a report to the European
Parliament and to the Council and may be accompanied by a further legislative proposal.3
Our paper will help the European Commission in assessing whether or not its current position
regarding the requirement of number of ratings is prudent and effective.
The rest of the paper is organised as follows. In Section 2 we review the hypotheses on the
impact of the number of credit ratings on the funding cost. Section 3 describes the data. Section
4 examines the impact of the number of triple-A credit ratings on the funding cost of an RMBS.
Section 5 examines the relation between complexity and the number of triple-A ratings. We
conclude in Section 6.
2. Hypotheses on the Impact of the Number of Credit Ratings on Funding Cost
To understand the impact that the number of ratings could have on funding cost, it is important
to understand the process by which credit ratings are obtained for structured finance securities.
The originator of the security will typically request one or more of the CRAs to rate a planned
transaction. Throughout the structuring phase of the security it remains at the originator’s
discretion whether to withdraw or maintain such a rating request. Prior to the issuance of the
security, the originator would have obtained detailed feedback from a CRA it has approached for
a rating, as to the rating that is likely be assigned given the characteristics of the collateral and
the structure of the transaction.
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3 - Source: //www.allenovery.com/publications/en-gb/Pages/CRA-III-and-the-over-reliance-on-rating-in-question.aspx.
There are various hypotheses identified in the literature that offer an explanation for the
selection of the CRA or CRAs to rate the tranches of a structured finance transaction and the
decision as to the number of agencies to obtain a rating from. These hypotheses include the
rating shopping hypothesis and information production hypothesis mentioned earlier. The rating
shopping hypothesis asserts that an issuer will search (i.e., shop) for an additional credit rating
for a debt obligation in order to decrease its cost of capital. Examples of research in this area
include Sangiorgi, Sokobin, and Spatt (2009), Farhi, Lerner, and Tirole (2013), Bolton, Freixas, and
Shapiro (2012), and Opp, Opp, and Harris (2013). One form of the rating shopping hypothesis
relates to shopping for a higher rating, and some forms focus on shopping for as many ratings as
possible even if at the same rating level. An important example of the first form, applied in the
structured finance market, is the research by Skreta and Veldkamp (2009). The second form can
be found in He, Qian, and Strahan (2012), who state that having a smaller number of ratings on a
security will increase funding cost because “investors price the risk that issuers ‘shopped for the
best rating’ when tranches have fewer than three ratings. By shopping, an issuer could ‘censor’
out pessimistic ratings, thus reducing the number of ratings observed by investors.”
Since the focus in this paper is on triple-A rated tranches, we only test the second form of the
rating shopping hypothesis, that is, where the effect of the number of ratings on funding cost is
measured. This is because an issuer cannot shop for a better rating (the other form of the rating
shopping hypothesis) if the CRA it obtains a rating from awards the highest (i.e. triple-A) rating
to a tranche in the first place.
Another body of research examines the information production of credit ratings. The information
production hypothesis, suggested by Bongaerts, Cremers and Goetzmann (2012), asserts that
multiple credit ratings may provide investors with more information regarding the credit risk of
a debt obligation. More specifically, obtaining additional credit ratings reduces the uncertainty
about the creditworthiness of a debt obligation: “an extra rating in agreement with existing
ratings would reduce credit quality uncertainty and thereby lower credit spreads.” They do not
find evidence to support this hypothesis using a sample of U.S. corporate bond issues from 2000
to 2008.
The relevant rating shopping hypothesis (i.e. the hypothesis dealing with triple-A rated securities)
and the information production hypothesis both suggest the same about the impact of more
than one triple-A rating for a tranche on that tranche’s funding cost: an inverse relationship. The
arguments for the inverse relationship, however, are different. The rating shopping hypothesis
suggests that investors will penalise securities that have one or two triple-A ratings rather than
three triple-A ratings,4 because the investors will price in the risk that in those cases the issuer
may have shopped for the one or two CRAs that indeed were prepared to assign a triple-A
rating to the security. The CRA whose rating is missing may have been unwilling to assign a
triple-A rating and the issuer therefore may have chosen not to enter into (or to terminate an
existing) contract with this reluctant CRA. So securities with one or two triple-A ratings will have
higher funding costs than securities with three triple-A ratings. The argument of the information
production hypothesis for the inverse relationship is that with every additional rating, even if at
the same (triple-A) level, additional information about the security is produced, which reduces
the risk associated with investing in the security, which then can be rewarded by a lower funding
cost.
An alternative to the rating shopping and information production hypotheses is that the decision
by an issuer to use more than one triple-A rating is due to the complexity of the structure. We
refer to this as the “complexity hypothesis” and suggest that multiple ratings, even when they are
the same rating, may reflect the issuer’s view of how the market would perceive the transaction
in terms of the risk associated with its complexity. In this framework, a rational issuer would only
4 - Three rating are mentioned here because there are three major credit rating agencies which typically rate structured finance securities: Standard & Poor’s, Moody’s, and Fitch.
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obtain multiple ratings if required, in order to reasure investors that the complexity of the security
has been reviewed by more than one CRA. In this view, investors perceive the number of triple-A
ratings as a signal for complexity, for which they demand a risk premium.
There has been some research aimed at examining the potential impact of complexity of a security
on the process of obtaining credit ratings. In particular Skreta and Veltkamp (2009) focused on
the topic, suggesting that the incentive to shop for ratings may be greater with complex securities
than with simple securities. They put forward a theory where “for simple assets, agencies issue
nearly identical forecasts. Asset issuers then disclose all ratings because more information reduces
investors’ uncertainty and increases the price they are willing to pay for the asset. For complex
assets, ratings may differ, creating an incentive to shop for the best rating.” Their theory does not
consider, though, whether or not issuers seek to obtain more credit ratings for complex securities
than for simple securities, nor did the authors test their theory on complexity empirically. In
addition to Skreta and Veldkamp (2009), several other papers analyze the effects of complexity on
the pricing of securities. Carlin, Kogan and Lowery (2013) show that complexity affects both the
liquidity and price volatility of assets, arguing that this might be explained by the likelihood that
more computational errors are made when valuing complex assets. Arora et al. (2009) show that
complex assets are difficult to price.
By empirically testing our complexity hypothesis, which suggests a higher funding cost when
securities have additional triple-A credit ratings, we automatically test the rating shopping and
information production hypotheses that both suggest the opposite (a lower funding cost when
there is more than one triple-A rating). Furthermore, our empirical findings build on and further
develop an understanding of the impact of complexity on asset pricing as we investigate our
complexity hypothesis, in detail, as follows. First, we examine in fact whether or not, and to
what extent, multiple triple-A ratings lead to higher funding costs. We delve deeper into the
relationship between multiple triple-A ratings and funding cost by testing whether the difference
in funding costs of securities with two versus three triple-A ratings depends on any particular
combination of the two CRAs that are retained. Second, we examine whether or not securities that
have certain complex features are likely to have more triple-A ratings than securities without the
complex features.
3. Data and Descriptive Statistics
The only structured finance asset class within the European structured finance market that has
had a sufficiently large number of issuances to conduct meaningful empirical analysis is RMBS.
We collected the entire set of triple-A rated Euro-denominated RMBS issued at par between
1999 and 2006 as reported in Structured Finance International (SFI), a publication of Euromoney
Institutional Investor Plc. The cut-off of 2007 was intentional for two reasons. First, regulatory
bodies designed the requirement to have more than one credit rating on structured finance
securities to address concerns with respect to market practices that led to the crisis in 2007, so
the relevant dataset has to be up to 2007. Second, since the crisis, the number of new structured
finance securities that are sold to investors5 has dropped so dramatically that it impedes empirical
analysis.
We focus our analysis on triple-A tranches as this represents by far the largest part of the RMBS
market and because in this market the lower rated tranches typically only exist by virtue of the
need to provide credit support to a related triple-A tranche. That is, issuers in the RMBS market
almost always create, for each transaction, more than one tranche of securities, ranked in order
of seniority. Investing in the most senior tranche of a transaction carries less risk for an investor
than investing in one of the junior tranches of the same transaction because the senior tranche
is protected from credit losses by the junior tranches. Issuers typically evidence this lower risk
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5 - Since the crisis of 2007 it has become common for issuers of structured finance securities not to sell its securities to third parties but to retain them as collateral for loans from the European Central Bank.
by contracting a CRA that is prepared to assign the highest possible credit rating to the senior
tranche: triple-A. As a result of the lower risk to the investor, the funding cost to the issuer of
the (triple-A) senior tranche is lower than the funding cost to the issuer of the (lower rated)
junior tranches. For each transaction, the issuer tries to maximise the relative size of the (low
cost) senior tranche compared to the (high cost) junior tranches. However, there is a limit to this
maximisation. If this issuer reduces the relative size of the junior tranches too much, the CRA may
become unwilling to assign a triple-A rating to the senior tranche, as they may decide that the
senior tranche has lost too much of the junior tranche loss-protection. So the mix of senior and
junior securities per transaction is a result of careful structuring and negotiation between the
issuer (and its underwriters) with the CRAs. This way of structuring to a certain rating level is an
important reason why the securities are called “structured finance securities”. In practice, in the
European RMBS market almost all transactions are structured such that the senior tranche is by
far the largest and is indeed rated triple-A by at least one CRA.
SFI reports the spread at which each tranche is issued. There are two reasons why we use this
spread measure rather than secondary market spreads. The first is that secondary market spreads
vary continuously throughout a security’s life and will be impacted by not only the rating but
also by the actual performance of the collateral underlying the security (defaults and recoveries).
A secondary market spread will reflect more information on the security’s (actual and expected)
performance than the credit rating, which in practice is not changed continuously: credit ratings
are reviewed by CRAs periodically (under normal circumstances annually or semi-annually), not
continuously. This problem does not exist in the case of new issuance spreads.
The second reason for using the new issuance spread is the difficulty of obtaining reliable secondary
market spreads since such spreads are typically derived from pricing matrices or dealer indicative
quotes. So even though secondary market trades could theoretically be preferable in that they
provide a cross-sectional snapshot of where tranches trade in the market at a given point in time,
no reliable secondary market spread data are available due to the lack of active trading in the
structured finance sector.
There are tranches that are both fixed-rate and floating-rate. For our analysis we want to have a
consistent benchmark for assessing the primary market spread. If fixed-rate tranches were to be
included in our study, then it would be necessary to determine the appropriate benchmark yield
curve for each tranche in the sample. By restricting the tranches in our sample to floating-rate
tranches where the reference rate is the same interest rate benchmark, we avoid this problem.
The coupon reset formula for a floating-rate security is the reference rate plus the quoted margin.
In our study, we use only floating-rate tranches benchmarked off the European interbank offered
rate (EURIBOR) and trading at par.6 The quoted margin, or spread, over the prevailing EURIBOR at
issue is the tranche’s funding cost. The quoted margin for a floating-rate tranche issued at par
is the additional per annum compensation for the risk to investors of purchasing that particular
tranche. For securities issued above or below par, the quoted margin reflects, in addition to credit
risk, a yield adjustment not related to risk; for that reason only EURIBOR referenced floating-rate
tranches issued at par are included in our sample.
Our final sample consists of 441 Euro-denominated triple-A RMBS tranches. The sample represents
83% of the entire set of Euro-denominated triple-A RMBS issued in the period 1999 to 2006 with
a total par value of €229.42 billion (87% of the entire set). Table 1 provides summary statistics
for our sample, showing relevant statistics regarding the dummy variables and the continuous
variables that we measured.
6 - EURIBOR reflects the interest rate at which highly credit rated banks can borrow, in euros, from other banks on an unsecured basis. EURIBOR is determined and communicated on a daily basis for a variety
of maturities.
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4. The Impact of the Number of Triple-A Ratings on the Funding Cost of a Security
In this section, we first describe the regression model that we use for measuring the relationship
between (combinations of) triple-A ratings and the funding costs of securities, after which we
present the empirical results.
4.1. Empirical Model
We estimate the following two regression models to test the impact of the number of credit
ratings on the funding cost for our RMBS sample:
where Spreadit represents the new issuance spread of tranche i at time t. Two Ratersit stands for
a dummy variable and corresponds to a tranche that is rated by two CRAs. Moody’s-Fitch Ratingit
stands for a dummy variable and corresponds to a tranche that is rated exclusively by Moody’s
and Fitch. Moody’s-S&P Ratingit stands for a dummy variable and corresponds to a tranche that
is exclusively rated by Moody’s and S&P. S&P-Fitch Ratingit stands for a dummy variable and
corresponds to a tranche that is rated exclusively by S&P and Fitch. Three Ratersit stands for a
dummy variable and corresponds to a tranche that is rated by all three CRAs.7
In Tables 2 and 3 we report the impact on the spread of the number of credit ratings and other
commonly used control variables in a pooled time-series and cross-sectional panel dataset. Given
the nature of our data, we had to deal with three potential econometric issues. First, to remove
systematic heterogeneity from the error term, we used a heteroskedasticity-consistent variancecovariance matrix as suggested by White (1980). Second, observations in the aggregate may be
affected by the same macroeconomic conditions; therefore, it is necessary to control for the
time effect. To deal with the potential error-dependence problem, we follow Petersen (2009)
and use dummy variables that correspond to different quarters. Each dummy variable is equal to
one if the securitisation from which the tranche is included was issued during the corresponding
quarter, and zero otherwise. Because of the use of time dummies, we do not include any other
macroeconomic variables in our analysis. Third, when bond metrics such as spreads are the unit
of observation, a problem arises when there are multiple observations for the same issuer. As a
result, the observations cannot be treated as independent of each other. For this reason, we follow
Petersen’s suggestion and take into account issuer fixed effects in our analysis: we cluster for all
tranches issued by the same RMBS originator. Finally, we control for size (log of tranche amount)
and for the subordination level of each tranche in the sample to obtain robust cross-sectional
results. The need to control for a tranche’s subordination level is due to the triple-A nature of
our sample (as explained in Section 3). The subordination level as a structuring feature is used in
securitisation transactions to support each of the triple-A tranches in our sample. We measure
the level of subordination below each triple-A tranche by the tranche’s attachment point (i.e. the
point at which credit losses can no longer be absorbed by tranches subordinated to that tranche).
To compute the subordination levels for all tranches in our sample, we first divided the par value
of each tranche by the total amount of the transaction’s liabilities. We then calculated a tranche’s
attachment point as the percentage of the total liabilities subordinate to that tranche. Thus, the
tranche will not suffer any losses until after that percentage of the liabilities has been lost.
4.2 Empirical Results
Table 2 shows the empirical results for various regressions of regression model (1); in each column
we display the results of a particular regression. In the first two regressions, “one rater” is the
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7 - Contrary to the dummy variables applied for analysing specific combinations of two triple-A ratings, we do not introduce dummy variables related to analysing the effect of which particular single CRA
rates a tranche triple-A in the cases where the tranche is rated only by one CRA. There are too few cases to generate meaningful results. As can be seen in Table 1, Panel A, only 45 of our triple-A securities
were rated by one of the three CRAs.
omitted class. Regressions (1) and (2) show the effect on the spread at issuance of two and three
triple-A ratings compared to tranches where there was only one triple-A rating. Regressions (3)
and (4) list the empirical results when “three raters” is the omitted class. In Regressions (5) and (6),
“two raters” is the omitted class. In regressions (1), (3) and (5) we have controlled for issuer fixed
effects and in regressions (2), (4) and (6) we have not.
It can be seen in regressions (1) and (2) that all the coefficients of two and three raters are positive
and highly significant with computed t-statistics between 3.20 and 5.15. This means that Eurodenominated RMBS securities rated triple-A by two or three CRAs between 1999 and 2006 had
on average a higher spread at issuance than securities with only one triple-A rating. For example,
in regression (2), two triple-A ratings show a spread increase on average of almost 4 basis points
(t-statistic of 3.20) compared to one triple-A rating. We observe a spread increase of about 6
basis points (t-statistic of 5.15) for three ratings compared to one rating. We also observe that
on average securities with three triple-A ratings have a higher spread than securities with two
triple-A ratings. For example, in regression (6) we see that three triple-A ratings give a spread
increase of more than 2 basis points (t-statistic of 3.45) compared to two triple-A ratings.
By using regression model (2), we analyse whether the results described above are sensitive to any
particular combination of CRAs. That is, when comparing two triple-A ratings with one or three
raters, does the positive impact between the number of triple-A ratings and spread at issuance
hold irrespective of which combination of CRAs is retained for providing two triple-A ratings?
The results can be found in Table 3. In regressions (1) and (2) in Table 3, we can see that for all
combinations of two triple-A ratings, on average the spread at issuance is higher than when only
one triple-A rating is obtained: the spread increase is about 3 to 4 basis points with t-statistics
ranging from 2.55 to 2.91.
The results presented in Tables 2 and 3 are consistent. We see that both prior to and after
controlling for issuer fixed effects, the coefficients are positive and highly significant and robust
for every combination of CRAs. So, we can conclude that for the European RMBS market pre-crisis,
there was on average a positive relationship between the number of triple-A ratings assigned
to a security and the spread at issuance. Apparently one must reject the “rating shopping” and
“information production” hypotheses for this market.
5. Could the Number of Triple-A Ratings Be a Signal of Complexity?
In this section, we explain which indicators we use as a proxy for complexity in structured finance
securities. We then describe the empirical model that we use to measure complexity. Finally, we
present our empirical results.
5.1. Complexity in Structured Finance
As set out in Section 3, the most commonly applied structured finance technique to obtain a
triple-A rating is by applying subordination to enhance the creditworthiness of the structure’s
most senior tranche. The higher the risks associated with the pool of assets, the greater the
amount of subordination in the capital structure needed to protect the most senior tranche from
investment losses to such an extent that a CRA will assign the highest triple-A rating to this senior
tranche. So, other factors being equal, a high level of subordination can be seen as a proxy for
complexity of a triple-A tranche, be it the quality of the underlying pool, the predictability of
losses, the rigour of the legal structure or any other way causing complexity.
In addition, for European RMBS, “slicing-off” the bottom portion of a triple-A rated senior tranche
to create a so-called “super-senior” tranche rated triple-A and a subordinated tranche also rated
triple-A, has been a common feature to cater to different investor requirements: some investors
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sought only to purchase triple-A securities that were protected by a subordinated tranche that
itself was of such a high credit quality that a CRA was prepared to assign it a triple-A rating, even
though this tranche was not the most senior in the capital structure. Consequently, the investors
in the “super-senior” tranche were protected by a tranche that was of triple-A quality itself and
even further removed from the risk of investment losses. The creation of two triple-A tranches
in one transaction is another indicator of complexity as they are clearly engineered for reasons
beyond achieving a triple-A rating.
Based on these two measures of complexity, in our complexity hypothesis framework we expect
that issuers will seek more triple-A ratings on tranches with higher subordination levels; and we
also expect that issuers will seek more triple-A ratings in structures that have applied the supersenior/subordinate triple-A splitting-off feature.
5.2 Empirical Model
Empirically testing our complexity hypothesis requires a different model than the one we used in
Section 4, where we ran ordinary least squares regressions to estimate the relationship between
funding cost and number of triple-A ratings. To examine the impact of our indicators of complexity
on the number of ratings, we do not measure the direct relationship between those indicators
and the funding cost. This is because the indicators of complexity are structuring techniques
that precisely aim to reduce the funding cost for the issuer. For example, modelling the simple
relationship between the funding cost of the triple-A tranche and the level of subordination
would only display the obviously inverse relationship between the two but indicate nothing about
the impact of the complexity of the pool (as measured by the subordination level) on the number
of ratings.
We therefore use a different empirical model. We want to investigate whether complexity of the
triple-A tranche has an impact on the number of credit ratings. The issuer can choose to obtain
one, two or three credit ratings. We consider three unique situations in which the issuer chooses
the number of ratings given a set of tranche complexity characteristics. The first situation is when
the issuer chooses three triple-A ratings instead of two. Secondly, the issuer chooses to obtain
three triple-A ratings instead of one. Third, the issuer chooses two triple-A ratings over one. To
investigate the impact of complexity characteristics on the probability on the number of obtained
credit ratings, we estimate the following probit model:
(3)
where Yi is an indicator that takes the value of 1 if tranche i has either two or three credit
ratings, x’i is a vector of complexity factors, β is a vector of parameters to be estimated, and Φ is
a standardised normal cumulative distribution function. We have also applied the logit approach.
The results obtained from this approach are consistent with our probit model. Therefore, we only
report the results based on the probit model.
Table 4 Panel A presents the results of probit regressions where Yi is an indicator that takes the
value of 1 if tranche i has three credit ratings and zero two credit ratings. In Panel B we repeat
the analysis, however then Yi is an indicator that takes the value of 1 if tranche i has three credit
ratings and zero two credit ratings. Finally, in Panel C the same analysis is applied for when Yi is
an indicator that takes the value of 1 if tranche i has two credit ratings and zero one credit rating.
Our right hand side variables that are of interest to the analysis at hand are our two indicators
of complexity: the level of the subordination for each triple-A tranche and whether the issuer
has applied the super-senior/subordinate triple-A splitting-off feature. Super Seniorit represents
a dummy variable of 1 when a tranche is a super senior tranche (i.e. supported by a subordinated
triple-A tranche), zero otherwise. Subordinatedit represents a dummy variable of 1 when a tranche
is a subordinated triple-A tranche, zero otherwise. Seniorit represents triple-A securities that have
10
not been split into super senior and subordinated triple-A tranches, and is the omitted class.
Furthermore, in our baseline specification we apply controls for size (because we expect tranches
with a larger size to have more credit ratings) and for time (as it is well known that over time
RMBS originators have increased the number of obtained credit ratings per tranche, we include
year fixed effects).
5.3 Empirical Results
In Table 4 we report the results of probit regressions. The table shows the regression coefficients of
the complexity factors on the probability of having multiple ratings. We report robust z-statistics
within brackets underneath the estimated coefficients.
Panel A displays the first of the three unique situations that we analyse, namely when the issuer
chooses to obtain three triple-A ratings compared to two. In column (2) we present the result of
a probit regression, in which super-senior and subordinated variable are the only variables on the
right-hand side. We find that tranches that are super-senior and those that are subordinated are
65.9% and 81.9% more likely to have three ratings compared to two. In column (3), subordination
level is the only variable on the right-hand side. The results are significant at the 1% level and show
that tranches with a higher subordination level are more likely to have three ratings. In column (4)
we include all variables: size, senior-subordinate, subordination level and year fixed effects. For
subordinated tranches the effect is virtually unchanged: subordinated triple-A tranches are 84.1%
more likely to have three ratings compared to two. For super-senior tranches, however, the sign is
now negative instead of positive and significant only at the 10% level. The effect of subordination
level, our other proxy for complexity, is now much stronger and remains highly significant. The
results displayed in column (4) can be interpreted as follows. In column (4) all variables are included
simultaneously. The variable “super-senior tranche” is, by its nature, strongly correlated with the
variable “subordination level”, and we cannot rule out the possibility that the positive effect of
the senior tranche on the number of credit ratings is simply a consequence of senior tranches have
higher subordination levels. This interpretation is strengthened by the fact that the inclusion of
subordination level reduces the statistical precision of the super-senior tranche in the analysis.
In Panel B we repeat the analysis displayed in Panel A, but now the object of analysis is the
choice for three triple-A ratings compared to one, whereas in Panel A the object of analysis was
the choice for three triple-A ratings compared to two. For two of our proxies for complexity,
the results are similar to our earlier findings in Panel A: where we found that the presence of a
subordinated tranche and higher subordination levels are associated with the choice for three
credit ratings. As regards the super-senior tranches, no significant impact on the choice for three
versus one triple-A rating is found, either when considered independently in column (2) or after
adding all the factors in column (4).
In Panel C the object of analysis is the choice for two triple-A ratings compared to one. In this
specific situation our factors for complexity show no significant impact on the choice between
two and one triple-A ratings. In columns (2) and (3) we can see that the factors all have the
expected positive signs, but the measurements do not pass our significance tests.
In sum, the findings described above suggest that the higher the subordination level (an indicator
for complexity) the greater the probability that tranches will have three triple-A ratings when
compared to either two or one triple-A ratings. The impact of having the super-senior/subordinated
triple-A splitting off feature on the choice for multiple credit ratings is less straightforward. For
the super-senior portions of the transactions where the splitting off feature has been applied, we
virtually find no significant results (and sometimes with the wrong sign, albeit insignificant). For
the subordinated triple-A tranches, we do find a highly significant impact on the choice between
three and two, and between three and one triple-A rating. As stated before, for the situations
11
analysing our set of complexity factors that influence the choice for two versus one triple-A
ratings, we do see the expected signs, but the results do not pass our significance tests.
6. Conclusion and Policy Implications
In much of the current research on market practices with respect to the use of credit ratings, the
rating shopping hypothesis and the information production hypothesis feature prominently. Both
of these hypotheses predict an inverse relationship between the number of ratings and a security’s
funding costs; that is, more ratings will reduce funding costs and, conversely, less ratings will
increase funding costs. We find precisely the opposite to have been the case for the mainstay of
the structured finance securities market in Europe prior to 2007, namely the triple-A tranches of
European RMBS. Looking at the relationship between the number of CRAs and funding costs we
find that on average, tranches with three triple-A ratings had higher a funding cost than tranches
with one or two triple-A ratings, and tranches with two triple-A ratings had a higher funding cost
than tranches with only one triple-A rating.
We develop a complexity hypothesis to help explain the observed positive relationship between
the number of ratings and the funding spread. We suggest that the number of ratings may be a
signal for complexity, for which investors demand a risk premium. We test an empirical model that
measures the relationship between the number of triple-A ratings and certain complexity features
that are key to the structured finance market: the application of subordination in order to achieve
a triple-A rating and the splitting of a triple-A tranche into a super-senior and a subordinated
triple-A tranche. Our complexity hypothesis is that these complexity features are likely to cause
issuers to obtain a greater number of triple-A ratings in order to secure a successful placement of
these tranches with investors, notwithstanding the complexity of the securities in question.
Indeed we find, for the same data set of European RMBS, that the higher the subordination level,
the greater the probability that tranches will have three triple-A ratings rather than either two
triple-A ratings, or one. The impact of our other complexity indicator (i.e. having the super-senior/
subordinated triple-A splitting off feature) is less clear. For the resultant subordinated triple-A
tranches, we do find a highly significant impact on the choice between three and two ratings,
and between three ratings and one triple-A rating, but for the super-senior tranches we find less
convincing results. For the choice between two versus one triple-A rating our results do not pass
our significance tests.
Academically, these results are relevant globally for our understanding of how credit ratings may
impact market behaviour. Our findings suggest that the structured finance market may behave
differently than what would be predicted by the rating shopping and information production
hypotheses. Obtaining multiple credit ratings may be a signal for complexity, as suggested by a
number of our findings. Hence further analysis on the role of complexity features of a security on
market practices with respect to credit rating agencies is called for.
For European policymakers the relevance lies in the review, scheduled to take place before 1
January 2016, of the efficacy of the EU Regulation that requires issuers to engage at least two
CRAs for the rating of structured finance securities. The current regulation, which came into force
on 20 June 2013, may lack effectiveness, as it does not focus on structured finance securities with
complex features. Policymakers should be cognizant of the risk that a requirement of a minimum
number of ratings may reduce the information content on the complexity of a security that
could otherwise (i.e. without the requirement) be signalled by the number of ratings. Our paper
will help the European Commission in assessing whether or not its current position regarding the
requirement of number of ratings is prudent and effective.
12
Table 1
Summary of Statistics Variables Used in the Analyses
This table reports summary statistics of Euro-denominated RMBS issued and sold in the European market (83% of the entire set). Panel
A reports the statistics for the dummy variables. Number of Ratings represent the number of ratings for each tranche. Tranches with
dual ratings are tranches that obtained two ratings. Moody’s-Fitch Rating stands for a dummy variable of 1 when the tranche is rated
exclusively by Moody’s and Fitch, zero otherwise. Moody’s-S&P Rating stands for a dummy variable of 1 when the tranche is exclusively
rated by Moody’s and S&P, zero otherwise. S&P-Fitch Rating stands for a dummy variable of 1 when the tranche is rated exclusively by
S&P and Fitch, zero otherwise. Tranches with Single Rating represent the number of tranches rated by one CRA. Senior represents a dummy
variable of 1 when the tranche has not been divided in a super senior and subordinated triple-A tranche, zero otherwise. Super Senior
tranche represents a dummy variable of 1 when the tranche represents the super senior part of the senior triple-A tranche, zero otherwise.
Subordinated triple-A tranche represents a dummy variable of 1 when the tranche is the subordinated part of the senior tranche, zero
otherwise. Panel B reports the statistics for the continuous variables. Spread at Issue is the tranche’s quoted spread over the EURIBOR
Interbank offered rate for a tranche issued at par. Log Principal Amount is the log of the size of the tranche. Subordination Level is the
cumulative subordination level of each tranche in a transaction.
13
Table 2
Regressions of Triple-A RMBS Spread to Number of Ratings
This table shows the results of regressing a tranche’s new issuance spread on the number of credit raters, internal credit enhancement,
and the size of the tranche. Regression (1)–(6) are based on a sample of triple-A Euro-denominated RMBS tranches issued (at par) between
January 1, 1999 to December 31, 2006 (83% of the entire set). One Rater represent a dummy variable of 1 when the tranche is rated by
one rater, zero otherwise. Two Raters represent a dummy variable of 1 when the tranche is rated by two raters, zero otherwise. Three
Raters represent a dummy variable of 1 when the tranche is rated by three raters, zero otherwise. Subordination Level is the cumulative
subordination level of each tranche in a securitisation structure. Log of Principal is the log of the tranche size in euro millions. Time effects
are included in regression (1)-(6) as control variables but are not shown in the tables. Issuer effects are included in regressions (1), (3) and
(5). The table shows the coefficient and White (1980) heteroskedasticity-adjusted t-statistic in brackets. Dashes, ‘-‘ denote not included in
the analysis. The symbols ***, **, and * denote parameter estimates for which zero falls outside the 99%, 95% and 90% posterior confidence
intervals, respectively.
Table 3
Regressions of Triple-A RMBS Spread to Dual Ratings
This table shows the results of regressing a tranche’s new issuance spread on the number of credit raters, dummies for dual ratings by
combinations of credit raters, internal credit enhancement, and the size of the tranche. Regression (1)–(4) are based on a sample of triple-A
Euro-denominated RMBS tranches issued (at par) between January 1, 1999 to December 31, 2006 (83% of the entire set). Dual Rating by
Moody's and Fitch stands for a dummy variable of 1 when the tranche is rated exclusively by Moody’s and Fitch, zero otherwise. Dual
Rating by Moody's and S&P stands for a dummy variable of 1 when the tranche is exclusively rated by Moody’s and S&P, zero otherwise.
Dual Rating by S&P and Fitch stands for a dummy variable of 1 when the tranche is rated exclusively by S&P and Fitch, zero otherwise.
Three Raters stands for a dummy variable of 1 when the tranche is rated by all three credit rating agencies, zero otherwise. Subordination
Level is the cumulative subordination level of each tranche in a securitisation structure. Log of Principal is the log of the tranche size in
euro millions. Time effects are included in regression (1)-(4) as control variables but are not shown in the tables. Issuer effects are included
in regressions (1) and (3). The table shows the coefficient and White (1980) heteroskedasticity-adjusted t-statistic in brackets. Dashes, ‘-‘
denote not included in the analysis. The symbols ***, **, and * denote parameter estimates for which zero falls outside the 99%, 95% and
90% posterior confidence intervals, respectively.
14
Table 4
Multiple Triple-A Credit Ratings and Tranche Characteristics
This table shows probit regression results. The sample includes floating-rate tranches triple-A Euro-denominated RMBS issued at par in the
period January 1, 1999 to December 31, 2006 (83% of the entire set). In Panel A the dependent variable is a binary variable that takes the
value of one if the tranche has three credit ratings, and zero if the tranche has two ratings. In Panel B the binary variable that takes the
value of one if the tranche has three credit ratings, and zero if the tranche has one rating. in Panel C the binary variable that takes the value
of one if the tranche has two credit ratings, and zero if the tranche has one rating. Log Principal Amount is the log of the size of the tranche.
Subordination Level is the cumulative subordination level of each tranche in a transaction. Super Senior Triple-A tranche represents a
dummy variable of 1 when the tranche represents the super senior part of the senior triple-A tranche, zero otherwise. Subordinated Triple-A
tranche represents a dummy variable of 1 when the tranche is the subordinated part of the senior tranche, zero otherwise. Panel B reports
the statistics for the continuous variables. Dashes, ‘-‘ denote not included in the analysis. Year 2000-2006 are dummy variables that
indicate the tranche was issued and sold. Reported are regression coefficients with robust z-statistics in brackets. The symbols ***, **, and *
denote parameter estimates for which zero falls outside the 99%, 95% and 90% posterior confidence intervals, respectively.
15
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17
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