Lead lag relationships and rating convergence among credit rating by alicejenny

VIEWS: 14 PAGES: 26

									The Journal of Credit Risk (95–119)                           Volume 7/Number 1, Spring 2011



Lead–lag relationships and rating
convergence among credit rating agencies
Andre Güttler
European Business School, International University Schloss Reichartshausen,
Department of Finance, Accounting and Real Estate, 65375 Oestrich-Winkel,
Germany; email: andre.guettler@ebs.edu




         Using a sample of corporate issuers rated by Moody’s and Standard & Poor’s
         (S&P) for the years 1994–2005, we find evidence that Moody’s rating migration
         rates are higher given a rating change by S&P. This seems to be tentative evidence
         that S&P assigns ratings in a timelier manner than Moody’s. Furthermore, we
         find that the tendency toward rating convergence is stronger for Moody’s than
         for S&P. Our findings are important given the concerns regarding the agencies’
         inherent incentives and their dominant market position.




1 INTRODUCTION
The international market for debt securities has experienced tremendous growth. At
the end of 2007 the amount outstanding – for financial institutions and corporate
issuers only – exceeded US$19 trillion (BIS (2007)). Because credit analysis is costly
to dispersed investors, it is more efficient to centralize credit analysis in a single
external credit rating agency (CRA), or at least in a small number of CRAs. Thus, in
virtually all cases, the creditworthiness of debt securities is assessed and made public
by external CRAs.
   In the international market for debt securities, Moody’s and Standard & Poor’s
(S&P)1 together command a market share of 80%, on average (Löffler (2007)), while

A previous version of this paper was entitled “Conditional Rating Transitions: The Case of S&P
and Moody’s”. We thank two anonymous referees, Patrick Behr, Albert L. Chun, Axel Eisenkopf,
Michael Kunisch, Peter Raupach, Sascha Steffen and seminar participants at the Deutsche Bun-
desbank, the annual meeting of the Swiss Society for Financial Market Research and the annual
meeting of the German Finance Association for their helpful comments. All errors and opinions
expressed in this paper are, of course, our own. Financial support by the E-Finance Lab is gratefully
acknowledged.
1 Throughout the paper we refer to the two CRAs in alphabetical order. The chosen order has no

implications with regard to the timeliness of the respective CRA’s rating assessments or the level
of rating convergence.


                                                 95
96   A. Güttler

     their combined specific coverage differs across regions and sectors. Obtaining a rating
     from both of the leading CRAs is standard in most rating markets, particularly in the
     US. Other CRAs such as Fitch Ratings or Dominion Bond Rating Service (DBRS)
     concentrate on particular business sectors or certain regions. Thus, Moody’s and
     S&P are the main providers of external credit risk assessments in most markets, and
     therefore the timeliness of the two dominant CRAs’ ratings and their tendency toward
     rating convergence are particularly important to market participants.
        Given the outstanding importance of credit ratings and the oligopolistic market
     structure, the CRAs’ business model has been viewed with increasing skepticism
     in recent years. Most CRAs charge companies that request credit ratings significant
     rating fees. Although the CRAs also offer paid research services, rating fees constitute
     the main part of their revenues.2 This could give rise to a conflict of interest, since
     CRAs might have a financial incentive to assign ratings that are favorable to issuers.3
     The outcome might therefore diverge from the CRAs’ proper function of assigning
     objective risk assessments to market participants.
        Yet, the CRAs’ potential conflict of interest would seem to be resolved by reputa-
     tional arguments (Ramakrishnan and Thakor (1984) and Millon and Thakor (1985)).
     In addition, a survey among rated issuers reveals that only 2.7% of them would agree
     with the assertion that the CRAs’ current business model encourages agencies to
     assign high ratings in order to satisfy issuers (Baker and Mansi (2002)). Thus, users
     trust the credit ratings because of the CRAs’ longstanding reputation in assessing a
     huge number of debt securities over time. CRAs also promote their reputation by
     releasing reports detailing their rating performance. These usually demonstrate that
     historical default frequencies rise with worsening rating levels. More recently, rating
     agencies also provide more information regarding rating accuracy and rating stability.
     For example, Moody’s (2010) reports a one-year (five-year) accuracy ratio of 83%
     (65%), an average rating prior to default of B2/B3, and a (large) rating action rate
     of 31.1% (8.5%) for corporate ratings. In addition, empirical results brought forward
     by Covitz and Harrison (2003) and Löffler (2007) seem to confirm that reputation
     predominates over conflicting financial interests at leading CRAs.
        However, despite the theoretical and empirical support for the CRAs, in the wake
     of the financial crisis that started in 2007, criticism is on the rise. Outside structured
     finance, their failure to foresee the crises of companies such as Enron, WorldCom or


     2 Historically, the main objective of the rating agencies has been to provide rating information to

     investors who paid for these services.
     3 Cantor and Packer (1997) find that this incentive seems to be particularly strong for smaller rating

     agencies. However, Jewell and Livingston (1999) and Feinberg et al (2004) show that those rating
     agencies (ie, other than Moody’s and S&P) provide incremental information by their ratings.


     The Journal of Credit Risk                                         Volume 7/Number 1, Spring 2011
              Lead–lag relationships and rating convergence among credit rating agencies                 97

Parmalat raised concerns about the timeliness of credit rating adjustments (Economist
(2005)). Although one has to admit that credit risk events related to fraud are very
difficult to assess, Enron’s default, involving an outstanding nominal amount of over
US$20 billion, is often held against the leading CRAs. Regardless of the fact that
most of the negative information was already known – as is best reflected in the stock
price collapse from over US$100 to US$3 – Moody’s and S&P’s downgrading to junk
status came very late.
   Two additional arguments might help to explain the mixed evidence regarding the
reputational incentive in the rating business. First, outcomes relating to the perfor-
mance of stock analysts are stressed. Among others, Trueman (1994) demonstrates
that “the likelihood that the analyst releases a forecast similar to those previously
announced by other analysts is greater than could be justified by his own informa-
tion.” The results indicate that analysts try to favorably affect investors’ assessments
of their own forecasting ability.4 Second, a CRA faces a trade-off between reputa-
tional and financial capital,5 as described by Boot et al (1993). In order to maximize
its reputational capital, the CRA has to make the most of the rating’s value for both
issuers and investors by minimizing the difference between the assigned rating and
the firm’s true quality. However, a CRA also has to stand up to competition from other
agencies (Bannier and Tyrell (2006)). Thus, to maximize its financial capital, a CRA
may announce a rating that takes into account the potential effects of competition, ie,
other CRAs’ credit ratings, while not necessarily reflecting the firm’s default risk.6
Following these theoretical arguments, CRAs might be inclined to take a very con-
servative approach toward the risk of announcing incorrect rating assessments, and
can therefore be expected to try to match as closely as possible a competitor’s rating
(or the competitors’ average rating). This conservative line minimizes the likelihood
that outside observers will be able to recognize inaccurate ratings.
   In this study we use rating information from Moody’s and S&P for the observa-
tion period January 1994–December 2005. This includes rating changes as well as
Watchlist additions. We use survival analysis to estimate conditional rating change
intensities,7 ie, we analyze how promptly rating changes by one CRA occur given
the other CRA’s rating action or rating level. First, we assess differences in the time-
liness of the two CRAs’ rating changes. This is done by estimating rating intensities


4 However, conflicting empirical evidence against herding among professional financial analysts is
presented by Bernhardt et al (2006).
5 Financial capital signifies (short-term) increases in profits that might arise due to increased rating

fees or to decreased (personnel) costs.
6 In practice, rating agencies aim to minimize these incentives by not discussing competitors’ rating

in rating committee meetings.
7 Rating change intensities can also be termed rating migration rates.




Technical Report                                                      www.thejournalofcreditrisk.com
98   A. Güttler

     for downgrades (upgrades) by each CRA conditional on whether there was a down-
     grade or negative Watchlist addition (upgrade or positive Watchlist addition) by the
     other CRA. Second, we examine whether the rating intensity is higher in the case of
     downgrades (upgrades) by one CRA if the rating level by the other CRA was lower
     (higher). This is done to provide evidence for a rating convergence to the other CRA’s
     rating level.
        We accomplish a relative comparison of Moody’s and S&P’s rating intensities with
     our approach. It allows us to judge which of the two CRAs reacts in a timelier manner,
     and which CRA is less likely to engage in rating convergence. This restriction to a
     relative comparison is a consequence of the fact that rating changes are not random
     events. The first CRA made a rating change presumably because it received public or
     private information that changed its risk assessment sufficiently to justify the observed
     rating change. Assuming the second CRA receives the same information8 and that it
     employs a comparable rating process,9 one would expect it, on average, to announce
     a similar rating change.10 Therefore, we do not provide an absolute comparison of the
     two CRAs’ ratings against an external benchmark such as a market-based credit risk
     measure. Rather, we assess relative differences between Moody’s and S&P regarding
     timeliness and rating convergence.
        In this paper we deliver two main results: First, we find evidence that Moody’s rating
     change intensities are higher given a rating change by S&P. This effect is somewhat
     stronger in the case of downgrades than for upgrades and seems to be tentative evi-
     dence that S&P assigns ratings in a timelier manner than Moody’s. Second, we find
     that the tendency toward rating convergence is stronger for Moody’s than for S&P.
     The respective increase in upgrade intensities is on average 38% larger for Moody’s
     when we observe a better rating by S&P than vice versa. Both results are novel.
        In this paper we complement two streams of literature. First, our results extend
     the evidence regarding the relative informational value of Watchlist additions versus
     rating changes. Norden and Weber (2004) show that negative Watchlist additions
     by Moody’s and S&P exhibit significant abnormal performance in stock and CDS
     markets, whereas actual downgrades do not. Using a market price expectations model,
     Hand et al (1992) find that negative and positive Watchlist additions by both CRAs
     are associated with stronger abnormal bond and stock price effects than in the case


     8 Even if CRAs receive the same information about an issuer they may not necessarily assign the
     same ratings since the interpretation of information could be different.
     9 Both CRAs employ a long-term rating approach that is meant to look through the business cycle.

     However, Moody’s uses an expected loss approach that incorporates the probability of default and
     the loss severity, whereas S&P only takes account of the probability of default.
     10 Analysis of split ratings reveals that most of Moody’s and S&P’s respective ratings are the same

     or just one notch apart (Cantor and Packer (1997)).


     The Journal of Credit Risk                                       Volume 7/Number 1, Spring 2011
             Lead–lag relationships and rating convergence among credit rating agencies             99

of actual rating changes. In contrast, we find no evidence that Watchlist additions by
one CRA increase the rating intensities of the other CRA.
   Given the close relationship between rating intensities and rating transition prob-
abilities, our findings also relate to research into the conditional factors of transition
matrices. In this line of literature it has been shown that rating transition probabilities
are influenced by several factors that are not included in the standard Markov setup.
These factors include previous rating changes by the analyzed CRA (Altman and Kao
(1992); Lando and Skødeberg (2002); Christensen et al (2004); Güttler and Raupach
(2010); and Hamilton and Cantor (2004)), the business cycle (Nickell et al (2000)
and Bangia et al (2002)), latent systematic factors (Wendin and McNeil (2006)), and
a bond’s age (Altman and Kao (1992); Kavvthas (2001); and Figlewski et al (2008)).
We contribute to these works by identifying additional rating information, ie, rating
changes by another CRA and rating level differences between the two CRAs, as being
predictors of conditional rating transition probabilities of a CRA.
   The remainder of the paper is organized as follows. Section 2 provides a description
of our dataset. Section 3 presents a short overview of the survival analysis methodol-
ogy. Section 4 is devoted to the empirical results, while the last section concludes.


2 DATASET DESCRIPTION
This study covers the period January 1994–December 2005. We use rating changes
as well as positive and negative Watchlist entries by Moody’s and S&P from
Bloomberg.11 Watchlist entries are introduced by the two CRAs to provide investors
with timelier information about the issuers’ credit risk. For instance, Steiner and
Heinke (2001) provide evidence that Watchlist additions are anticipated to a lesser
extent than rating changes. We therefore also take account of Watchlist entries in
addition to rating changes. Furthermore, we use withdrawn ratings as a signal that the
rating relationship between CRA and issuer was terminated.
   Given the broad range of different ratings for a given obligor, we construct a
single rating history for the long-term, foreign currency, senior unsecured ratings
of each issuer. We make use of a mapped numerical rating scale ranging from 1
(AAA/Aaa) to 19 (CC/Ca) throughout, ie, we assume that S&P’s AAA corresponds
to Moody’s Aaa, etc. Despite existing differences between the CRAs’ rating method-
ologies, this mapping procedure is widely used in academic practice (Morgan (2002)),
and by regulatory authorities. The SEC’s important investment/noninvestment bound-
ary BBB /BBC for S&P and Baa3/Ba1 for Moody’s is one example among others.


11 Wedisregard neutral Watchlist entries. First, the direction and therefore the meaning of these
Watchlist additions are unclear. Second, these Watchlist entries are rarely made by both CRAs.


Technical Report                                                  www.thejournalofcreditrisk.com
100   A. Güttler

      TABLE 1 Regional and sectoral distribution.

                                                (a) Regional distribution

                                                                             Rating changes
                                                                            ‚      …„      ƒ
                                                All companies             Moody’s        S&P
                                                 ‚ …„ ƒ                  ‚ …„ ƒ        ‚ …„ ƒ


               North America                    3282       0.701       4754       0.703      4716      0.681
               Europe                            695       0.149        902       0.133       901      0.130
               Asia                              415       0.089        605       0.089       717      0.103
               Latin/Central America             174       0.037        368       0.054       405      0.058
               Other                             114       0.024        134       0.020       190      0.027


                                                (b) Sectoral distribution

                                                                             Rating changes
                                                                            ‚      …„      ƒ
                                                All companies             Moody’s        S&P
                                                 ‚ …„ ƒ                  ‚ …„ ƒ        ‚ …„ ƒ


               Other nonfinancial                3544       0.757       5286       0.782      5359      0.773
               Utility                            63       0.013         65       0.010        47      0.007
               Bank                              667       0.143        889       0.131       961      0.139
               Other financial                    272       0.058        319       0.047       345      0.050
               Insurance                         134       0.029        204       0.030       217      0.031

      The table provides an overview of companies that held long-term senior unsecured credit ratings by Moody’s and S&P
      in the observation period January 1994–December 2005. Rating data is extracted from Bloomberg. Columns 1 and
      2 show the regional and sectoral distribution of 4680 sample companies which were assigned a credit rating by both
      CRAs for at least one day during the observation period 1994–2005. Columns 3 and 4 (5 and 6) exhibit the respective
      distribution of rating changes that have been assigned by Moody’s (S&P). We constrain the sample to those rating
      changes which have also been rated by the second CRA during that time. In part (a), “Other” comprises Africa,
      the Arabian Peninsula and the Pacific region. In part (b), the category “Other financial” includes asset managers,
      brokerage firms and the like.


         To investigate our two research questions we start with 4680 issuers which were
      rated by both Moody’s and S&P in the observation period January 1994–December
      2005. Our criterion for including companies in our analysis is that both CRAs rated
      a company for at least one day in our observation period. In addition to the rating
      history of January 1994–December 2005, we also use rating information from January
      1990–December 1993 to condition for previous rating actions. The starting point of
      our observation period is determined by our data source, Bloomberg, which begins
      at this point in time.
         Our results in columns 1 and 2 of part (a) of Table 1 provide the regional distribution
      at the company level. 70% of our sample companies are from North America, 15%

      The Journal of Credit Risk                                                  Volume 7/Number 1, Spring 2011
             Lead–lag relationships and rating convergence among credit rating agencies      101

from Europe, 9% from Asia and 4% from Latin and Central America. The remainder
of the sample is distributed over Africa, the Arabian Peninsula and the Pacific region.
Columns 3 and 4 (5 and 6) provide results on the rating level for Moody’s (S&P),
which are very similar to the company level. For rating changes by either CRA we
require valid rating observations by the other CRA for the time from the last rating
change to the point in time of the rating action of interest. This enables us to use
time-varying covariates, ie, rating information, from the other CRA in the survival
analysis.
   By using an international sample of bond rating changes we are able to increase
our sample size considerably. Additionally, our results should be relevant not only
for single markets, such as the US, but also for all other rating markets. Potential
differences in rating methodologies and the meaning of ratings across countries are
controlled for by our econometric setup (see Section 3).
   The sample’s sectoral distribution is shown in columns 1 and 2 in part (b) of Table 1
on the facing page on the company level. With a share of 76%, the nonfinancial
and nonutility companies represent the vast majority. Banks account for 14% and
insurance companies for an additional 3%. The remainder is distributed among utilities
(1%) and other financial firms (6%). Columns 3 and 4 (5 and 6) provide results on
the rating level for Moody’s (S&P) which are very similar to the company level. To
enlarge the sample and ensure that it is representative of the population of rated firms,
our analysis is not restricted to a particular business sector. Again, any remaining
differences are controlled for by our econometric setup (cf, Section 3).
   Table 2 on the next page provides information on the magnitude of the rating
changes. Overall, the number of rating changes is 6763 for Moody’s and 6929 for
S&P. The share of upgrades is higher in the case of Moody’s: 34% of its rating changes
were upward, compared with only 29% of S&P’s. The higher number of downgrades
for both CRAs is in line with the findings of Blume et al (1998), among others. In
general, it is explained by decreasing credit quality coupled with more stringent rating
standards. In addition, for both CRAs, the share of downgrades that exceed one rating
notch is higher than the share of large upgrades. In the case of Moody’s (S&P), large
changes account for 41% (34%) of all downgrades versus 29% (24%) of all upgrades.
Thus, downgrades are on average of a greater magnitude than upgrades. Besides,
Moody’s adjusts its ratings by more than one rating notch more often than S&P does.
The differences between the two CRAs’ rating change magnitudes are significant at
least at the 5% level using standard contingency tests.
   The number of Watchlist additions shows differences between Moody’s and S&P,
too. On the negative side, S&P assigned 4710 additions whereas Moody’s accounts
for only 3577 entries. On the other hand, Moody’s published 1504 positive Watchlist
additions for our selected companies; S&P released only 1273 positive additions.

Technical Report                                            www.thejournalofcreditrisk.com
102   A. Güttler

      TABLE 2 Distribution of the magnitude of the rating changes.


                                                     (a) Moody’s

                                      Downgrades                                    Upgrades
                             ‚            …„           ƒ ‚                             …„          ƒ
                                     One   Two     > Two                         One    Two    > Two
                             All    notch notches notches All                   notch notches notches

           Observations 4437 2629              1226          582       2326 1645            469          212
           In %         0.594 0.276            0.131        0.708      0.201 0.091


                                                        (b) S&P

                                      Downgrades                                    Upgrades
                             ‚            …„           ƒ ‚                             …„          ƒ
                                     One   Two     > Two                         One    Two    > Two
                             All    notch notches notches All                   notch notches notches

           Observations 4897 3223              1045          629       2032 1540            269          223
           In %         0.660 0.212            0.128        0.759      0.132 0.109

      The table provides an overview of the magnitude of the rating changes. Columns 1–4 include only companies with
      two long-term senior unsecured credit ratings by both Moody’s and S&P in the observation period January 1994–
      December 2005. In columns 5–8 we constrain the sample to those rating changes which have also been rated by
      the second CRA during that time. Rating data is extracted from Bloomberg.


      3 METHODOLOGY
      Close parallels exist between the time series of ratings and other time-to-event data
      such as clinical trials involving treatment and response. In the so-called survival
      analysis, patients are followed over time as they progress from one state (survival)
      to another (death). The patients’ survivals and deaths are related by the concept of
      intensity.12 It predicts the rate at which the patient will die by a specific point in
      time given that he or she has survived up to this time. One of the main advantages
      of survival analysis is the inclusion of censored observations, as one is not forced to
      exclude parts of the sample that could otherwise cause a selection bias.
         As in the case of conventional regression approaches, relevant independent vari-
      ables, which are denoted covariates in survival analysis, are employed to assess differ-
      ences in failure intensity among different patients. In the following the rating change
      intensity of group i at a given time t , ˛i (t ), is shown as:

                                     ˛i .t/ D ˛0i .t / exp.ˇ1 z1 .t / C ˇ2 z2 /                              (3.1)


      12   “Hazard rate” is an alternative term often used to denote this concept.


      The Journal of Credit Risk                                               Volume 7/Number 1, Spring 2011
             Lead–lag relationships and rating convergence among credit rating agencies                 103

   In this proportional intensity regression model, ˛0i .t / is an unspecified baseline
intensity, z1 .t/ is a vector of time-varying covariates, z2 is a vector of covariates
which are constant over t , and ˇ1 and ˇ2 are the regression parameters associated
with the covariates (Cox (1972)). We follow the approach of Lando and Skødeberg
(2002) and concentrate on rating changes by one notch to the neighboring rating class.
   In specification (1), time t is defined in three different ways. First, t is the time period
between the reported rating change and the last rating change or, as the case may be,
the initial rating. It thus expresses the time during which the company stayed within
the “old” rating class. Second, in the case of a rating that is subsequently withdrawn,
t is the time the company stayed within the rating class until it was withdrawn. Third,
we handle right-censoring by defining t as the time between the company’s entry into
a rating class and the end of the observation period (December 31, 2005).
   As we use time-varying covariates, z1 .t /, with rating information on the second
CRA, we can make use of more than one observation per rating change, ie, a change in
the respective covariate at a given time. The second covariate, z2 , is not time-varying
as, for instance, either the CRA’s last rating change was a downgrade or not. Including
z2 in specification (1) controls for the effect that consecutive rating changes in the
same direction are more frequent than in the opposite direction (see, for example,
Lando and Skødeberg (2002)).
   The model is semi-parametric since the rating change intensity has two components:
a nonparametric baseline intensity (corresponding to the intercept in conventional
regressions) and a parametric element which is determined by the covariates. Thus, by
using this approach we do not have to specify the form of the underlying distribution of
survival times. We instead assume a constant and proportional relationship between
the covariates and the dependent variable for all observations. As it is known that
rating intensities might also be driven by other factors such as the state of the business
cycle (see, for example, Bangia et al (2000)) or varying rating standards (Blume et
al (1998)) we absorb such time-varying, firm-invariant effects through the baseline
intensity.13 We further control for firm-specific effects by employing different baseline
intensities, ˛0i .t/, for different groups i . Specifically, we form groups according to
line of business, the company’s region and the credit cycle.14 The credit cycle is
approximated by a dummy which equals 1 if the downgrade ratio (number of Moody’s
and S&P’s downgrades over upgrades in our sample) is larger than the median ratio
and 0 otherwise. Thus we obtain stratified estimates that yield equal coefficients ˇ1
and ˇ2 across groups but with baseline intensities unique to each group.

13See, for instance, the discussion in Lando and Skødeberg (2002)
14 The time since issuance of the bond (see, for example, Altman and Kao (1992)) also influences
rating intensities. In our setup, however, this effect would seem to be of only minor relevance since
we concentrate on companies and not on single bonds.


Technical Report                                                     www.thejournalofcreditrisk.com
104   A. Güttler

      4 EMPIRICAL RESULTS
      Throughout we use the Cox regression of Equation (3.1) where only the employed
      covariates z1 .t/ and z2 are altered. The variation of the intensity of a certain rating
      change, ie, from Aa1 to Aa2, that is due to a specific covariate can be calculated
      by exp.ˇ1 /. To compare the results of the two CRAs for differing covariates, we
      calculate the mean variation of the rating intensities over all eighteen one-notch rating
      transitions, weighted by the number of actual rating changes out of the starting rating
      class.
         The following list provides an overview of the time-dependent covariates employed.

             Table 3 on page 106: downgrades by Moody’s.

                   – Columns 5/6: downgrade by S&P.
                   – Columns 7/8: Watchlist negative by S&P.
                   – Columns 9/10: riskier rating by S&P.

             Table 4 on page 108: downgrades by S&P.

                   – Columns 5/6: downgrade by Moody’s.
                   – Columns 7/8: Watchlist negative by Moody’s.
                   – Columns 9/10: riskier rating by Moody’s.

             Table 5 on page 110: upgrades by Moody’s.

                   – Columns 5/6: upgrade by Moody’s.
                   – Columns 7/8: Watchlist positive by Moody’s.
                   – Columns 9/10: less risky rating by Moody’s.

             Table 6 on page 112: upgrades by S&P.

                   – Columns 5/6: upgrade by S&P.
                   – Columns 7/8: Watchlist positive by S&P.
                   – Columns 9/10: less risky rating by S&P.

      For all these covariates we expect an increase in the rating change intensity. For
      instance, the downgrade intensity for companies rated by Moody’s should be higher
      for firms that experience a downgrade by S&P. Nevertheless, we have no a priori
      expectation as to which of these covariates causes the strongest increase in rating
      change intensities. In addition, we include the second, time-independent covariate z2
      in all specifications but omit these results in the tables.

      The Journal of Credit Risk                                 Volume 7/Number 1, Spring 2011
               Lead–lag relationships and rating convergence among credit rating agencies      105

4.1 Downgrades
Table 3 on the next page shows results for all downgrades by one rating notch by
Moody’s and rating actions by S&P as covariates of interest. In all three different
specifications of the table, z2 equals 1 if the issuer was previously downgraded by
Moody’s. Column 5 shows coefficients where z1 .t / equals 1 if the respective issuer
was downgraded by S&P at t . Fifteen out of the eighteen regression coefficients are
significantly different from zero on at least the 10% significance level.15 The weighted
mean variation of the downgrade intensity is 2.27, ie, a downgrade by Moody’s is
127% more likely if there was a downgrade by S&P. Column 7 shows coefficients
where z1 .t/ equals 1 if the respective issuer was put on Watchlist negative by S&P
at t . Here we find a somewhat weaker effect, as the weighted mean variation of the
downgrade intensity is 80% higher in the case of a negative Watchlist entry by S&P.
Column 9 shows coefficients where z1 .t / equals 1 if the respective firm had a lower,
ie, riskier, rating by S&P at t. The results are very similar to the case of a downgrade
by S&P, as the downgrade intensity is 134% higher.
   Table 4 on page 108 displays corresponding results for all downgrades by one rat-
ing notch by S&P and rating actions by Moody’s as covariates of interest. In all three
different specifications of the table, z2 equals 1 if the issuer was previously down-
graded by S&P. Column 5 shows coefficients where z1 .t / equals 1 if the respective
issuer was downgraded by Moody’s at t . Thirteen out of the eighteen regression coef-
ficients are significantly different from zero. The downgrade intensity is 82% higher
if there was a downgrade by Moody’s. Comparing these results with those shown in
column 5 of Table 3 on the next page, we find that Moody’s downgrade intensity is
25% larger (2.27 versus 1.82) in the case of a downgrade by S&P than vice versa.
Column 7 shows coefficients where z1 .t / equals 1 if the respective issuer was put on
Watchlist negative by Moody’s at t . The downgrade intensity is 72% higher in the
case of a negative Watchlist entry by Moody’s. This result is almost on a par with
the Table 3 on the next page column 7 results. Column 9 exhibits coefficients where
z1 .t/ equals 1 if the respective firm had a lower, ie, riskier, rating by Moody’s at t.
These results are rather similar to those for the two other specifications of the table,
as S&P’s downgrade intensity is 75% higher. Comparing these results with those
shown in column 9 of Table 3 on the next page, we find that Moody’s downgrade
intensity is 34% larger (2.34 versus 1.75) in the case of a lower rating by S&P than
vice versa.
   All in all, we find that rating actions by one CRA cause significantly higher rating
intensities for most one-notch downgrades by the other CRA. In line with findings by
Norden and Weber (2004) and Hand et al (1992) we observe that negative Watchlist

15 We   employ the 10% significance level throughout.


Technical Report                                              www.thejournalofcreditrisk.com
                                                                                                                                                                              106
The Journal of Credit Risk




                                                                                                                                                                              A. Güttler
                                 TABLE 3 Rating downgrade intensities for issuers rated by Moody’s conditional on S&P’s rating information. [Table continues on next page.]

                                                                                         Downgrade                  Watchlist                  Rating
                                            Ratings                                        by S&P                negative by S&P            lower by S&P
                                           ‚ …„ ƒ           Potential     Actual      ‚      …„      ƒ          ‚       …„      ƒ         ‚      …„      ƒ
                                           From To          changes      changes     Coefficient p value        Coefficient p value        Coefficient p value

                                           Aa1      Aa2         92           38          0.696       0.177         2.101       0.002        0.412        0.621
                                           Aa2      Aa3        170           57          1.528       0.000         0.154       0.681        0.689        0.112
                                           Aa3      A1         220          107          1.064       0.000         1.009       0.000        0.542        0.004
                                           A1       A2         329          152          0.560       0.001         0.739       0.000        0.562        0.003
                                           A2       A3         450          216          0.772       0.000         0.925       0.000        0.635        0.000
                                           A3       Baa1       472          196          1.140       0.000         0.665       0.000        0.834        0.000
                                           Baa1     Baa2       500          234          0.867       0.000         0.585       0.000        0.596        0.000
                                           Baa2     Baa3       535          261          0.825       0.000         0.304       0.056        0.743        0.000
                                           Baa3     Ba1        411          149          0.819       0.000         0.737       0.000        0.763        0.000
Volume 7/Number 1, Spring 2011




                                           Ba1      Ba2        249           93          0.775       0.001         0.437       0.109        0.971        0.000
                                           Ba2      Ba3        244           92          0.624       0.010         0.091       0.716        0.576        0.058
                                           Ba3      B1         340          139          0.988       0.000         0.661       0.002        0.651        0.016
Technical Report



                                 TABLE 3 Continued.

                                                                                                           Downgrade                          Watchlist                           Rating




                                                                                                                                                                                                                            Lead–lag relationships and rating convergence among credit rating agencies
                                                Ratings                                                      by S&P                        negative by S&P                     lower by S&P
                                               ‚ …„ ƒ                 Potential        Actual           ‚      …„      ƒ                  ‚       …„      ƒ                  ‚      …„      ƒ
                                               From To                changes         changes          Coefficient p value                Coefficient p value                 Coefficient p value

                                               B1         B2              472             237               0.623           0.000              0.464           0.003            0.689            0.002
                                               B2         B3              544             252               0.723           0.000              0.371           0.012            1.440            0.000
                                               B3         Caa1            413             194               0.815           0.000              0.183           0.303            1.077            0.000
                                               Caa1       Caa2            244             103               0.079           0.706              0.137           0.597            0.987            0.009
                                               Caa2       Caa3            138              46               0.694           0.040              0.592           0.079            1.511            0.002
                                               Caa3       Ca              114              63               0.097           0.760              0.362           0.182            0.670            0.220

                                               Weighted mean
                                               variation of rating intensity                               2.267                             1.796                              2.341

                                 The table shows rating-downgrade intensities for companies that held a rating by Moody’s in the observation period January 1994–December 2005. We include only companies
                                 with long-term senior unsecured credit ratings by both Moody’s and S&P in the observation period. We further employ rating data for the period January 1990–December 1993 to
                                 condition upon previous rating actions. Rating data is extracted from Bloomberg. The first two columns report the type of rating transition studied, eg, the first row exhibits rating
                                 changes from “Aa1” to “Aa2”. We concentrate on rating downgrades by one rating notch. The third column shows the number of all potential rating transitions out of the “From” rating
www.thejournalofcreditrisk.com




                                 class, ie, censored and uncensored observations, whereas the fourth column provides all observed rating transitions out of the “From” into the “To” rating class. Columns 5 and 6
                                 provide results for the specification with downgrades by S&P as time-varying covariates. Columns 7 and 8 show results for the specification with negative Watchlist entries by S&P
                                 as time-varying covariates. Columns 9 and 10 provide results for the specification with lower, ie, riskier, ratings by S&P as time-varying covariates. In all three specifications we omit
                                 results for the covariate of previous downgrades by Moody’s. We use a proportional intensity regression model by maximizing the partial likelihood of every rating downgrade by
                                 one full notch to the neighboring rating class. Each line thus comprises three separate regressions with discrete, time-varying covariates by S&P and covariates by Moody’s which
                                 are constant over time. We exclude the rating sequence “Aaa” to “Aa1” to allow for previous downgrades by Moody’s. A positive (negative) coefficient implies that the downgrade
                                 intensity is higher (lower) compared with the case of no downgrade (columns 5 and 6), no negative Watchlist entry (columns 7 and 8) or no lower rating (columns 9 and 10) by S&P.
                                 The last line shows the mean variation of the rating intensity, exp.ˇ1 /, over all one-notch rating transitions, weighted by the number of potential rating changes out of the starting
                                 rating class. We furthermore employ strata for regions, sectors and whether the rating change occurred in a month with an above-average rating downgrade ratio. We employ the
                                 robust sandwich estimate of Lin and Wei (1989) for the covariance matrix.




                                                                                                                                                                                                                            107
                                                                                                                                                                            108
The Journal of Credit Risk




                                                                                                                                                                            A. Güttler
                                 TABLE 4 Rating downgrade intensities for issuers rated by S&P conditional on Moody’s rating information. [Table continues on next page.]

                                                                                          Downgrade             Watchlist negative         Rating lower
                                            Ratings                                       by Moody’s               by Moody’s               by Moody’s
                                           ‚ …„ ƒ            Potential    Actual       ‚      …„      ƒ         ‚      …„        ƒ       ‚      …„      ƒ
                                          From    To         changes     changes      Coefficient p value       Coefficient p value       Coefficient p value

                                          AAC       AA           63          42          0.105       0.790        0.584        0.193       1.818        0.012
                                          AA        AA          178         120          0.444       0.052        1.151        0.000       0.567        0.030
                                          AA        AC          325         209          0.695       0.000        1.108        0.000       0.384        0.013
                                          AC        A           440         217          1.035       0.000        0.535        0.002       0.866        0.000
                                          A         A           500         247          0.615       0.000        0.454        0.002       0.184        0.178
                                          A         BBBC        558         288          0.414       0.002        0.086        0.530       0.513        0.000
                                          BBBC      BBB         586         278          0.780       0.000        0.642        0.000       0.493        0.000
                                          BBB       BBB         593         258          0.820       0.000        0.587        0.000       0.801        0.000
                                          BBB       BBC         391         186          0.617       0.000        0.499        0.002       0.524        0.001
Volume 7/Number 1, Spring 2011




                                          BBC       BB          306         141          1.087       0.000        0.417        0.013       0.476        0.013
                                          BB        BB          389         203          0.587       0.001        0.531        0.001       0.356        0.048
                                          BB        BC          528         272          0.488       0.001        0.467        0.002       0.675        0.000
Technical Report



                                 TABLE 4 Continued.

                                                                                                           Downgrade                      Watchlist negative                 Rating lower




                                                                                                                                                                                                                        Lead–lag relationships and rating convergence among credit rating agencies
                                               Ratings                                                     by Moody’s                        by Moody’s                       by Moody’s
                                              ‚ …„ ƒ                   Potential        Actual          ‚      …„      ƒ                  ‚      …„        ƒ               ‚      …„      ƒ
                                             From    To                changes         changes         Coefficient p value                Coefficient p value               Coefficient p value

                                             BC           B               610              317             0.238            0.059            0.245            0.106           0.462            0.003
                                             B            B               380              204             0.051            0.737            0.086            0.621           0.108            0.512
                                             B            CCCC            251              137             0.164            0.377            0.710            0.003           0.566            0.005
                                             CCCC         CCC             122               47             0.229            0.498            0.987            0.005           0.325            0.289
                                             CCC          CCC              54               18             0.319            0.591            1.275            0.005           1.849            0.007
                                             CCC          CC               48               39             0.902            0.037            0.526            0.126           0.554            0.095

                                             Weighted mean
                                             variation of rating intensity                                 1.817                             1.725                            1.748

                                 The table shows rating downgrade intensities for companies that held a rating by S&P in the observation period January 1994–December 2005. We include only companies with
                                 long-term senior unsecured credit ratings by both Moody’s and S&P in the observation period. We further employ rating data for the period January 1990–December 1993 to
                                 condition upon previous rating actions. Rating data is extracted from Bloomberg. The first two columns report the type of rating transition studied, eg, the first row exhibits rating
                                 changes from “AAC” to “AA”. We concentrate on rating downgrades by one rating notch. The third column shows the number of all potential rating transitions out of the “From”
www.thejournalofcreditrisk.com




                                 rating class, ie, censored and uncensored observations, whereas the fourth column provides all observed rating transitions out of the “From” into the “To” rating class. Columns
                                 5 and 6 provide results for the specification with downgrades by Moody’s as time-varying covariates. Columns 7 and 8 show results for the specification with negative Watchlist
                                 entries by Moody’s as time-varying covariates. Columns 9 and 10 provide results for the specification with lower, ie, riskier, ratings by Moody’s as time-varying covariates. We
                                 omit in all three specifications results for the covariate of previous downgrades by S&P. We use a proportional intensity regression model by maximizing the partial likelihood of
                                 every rating downgrade by one full notch to the neighboring rating class. Each line thus comprises three separate regressions with discrete, time-varying covariates by Moody’s
                                 and covariates by S&P which are constant over time. We exclude the rating sequence “AAA” to “AAC” to allow for previous downgrades by S&P. A positive (negative) coefficient
                                 implies that the downgrade intensity is higher (lower) compared with the case of no downgrade (columns 5 and 6), no negative Watchlist entry (columns 7 and 8), or no lower rating
                                 (columns 9 and 10) by Moody’s. The last line shows the mean variation of the rating intensity, exp.ˇ1 /, over all one-notch rating transitions, weighted by the number of potential
                                 rating changes out of the starting rating class. We furthermore employ strata for regions, sectors and whether the rating change occurred in a month with an above-average rating
                                 downgrade ratio. We employ the robust sandwich estimate of Lin and Wei (1989) for the covariance matrix.




                                                                                                                                                                                                                        109
                                                                                                                                                                            110
The Journal of Credit Risk




                                                                                                                                                                            A. Güttler
                                 TABLE 5 Rating upgrade intensities for issuers rated by Moody’s conditional on S&P’s rating information. [Table continues on next page.]

                                                                                          Upgrade                   Watchlist                  Rating
                                            Ratings                                        by S&P                positive by S&P           higher by S&P
                                           ‚ …„ ƒ           Potential    Actual       ‚      …„      ƒ         ‚        …„       ƒ       ‚      …„       ƒ
                                           From To          changes     changes      Coefficient p value       Coefficient p value        Coefficient p value

                                           Aa1      Aaa         64          10          1.962       0.118        2.077        0.087        0.410       0.593
                                           Aa2      Aa1        147          34          1.822       0.000        0.856        0.009         N/A         N/A
                                           Aa3      Aa2        184          71          0.166       0.527        0.494        0.086        0.611       0.106
                                           A1       Aa3        259          82          1.197       0.000        0.822        0.032        0.757       0.002
                                           A2       A1         332          98          0.816       0.000        1.321        0.000        1.072       0.000
                                           A3       A2         398         122          1.319       0.000        1.128        0.002        0.471       0.019
                                           Baa1     A3         388         122          0.544       0.008        0.377        0.371        0.656       0.001
Volume 7/Number 1, Spring 2011




                                           Baa2     Baa1       395         121          0.941       0.000        1.679        0.000        1.071       0.000
                                           Baa3     Baa2       414         152          0.561       0.002        1.002        0.000        0.901       0.000
Technical Report
                                 TABLE 5 Continued.

                                                                                                          Upgrade                            Watchlist                          Rating
                                               Ratings                                                     by S&P                         positive by S&P                   higher by S&P
                                              ‚ …„ ƒ                 Potential        Actual          ‚      …„      ƒ                  ‚        …„       ƒ               ‚      …„       ƒ




                                                                                                                                                                                                                        Lead–lag relationships and rating convergence among credit rating agencies
                                              From To                changes         changes         Coefficient p value                Coefficient p value                Coefficient p value

                                              Ba1         Baa3           276             120             1.176            0.000            0.780            0.017            0.554            0.009
                                              Ba2         Ba1            262             110             0.591            0.051            0.412            0.364            0.737            0.005
                                              Ba3         Ba2            303             102             0.518            0.015            0.880            0.002            0.923            0.000
                                              B1          Ba3            363             128             1.183            0.000            0.877            0.001            0.855            0.003
                                              B2          B1             443             151             1.058            0.000            1.067            0.000            0.786            0.002
                                              B3          B2             322             103             1.491            0.000            0.994            0.004            1.604            0.006
                                              Caa1        B3             200              59             0.628            0.087            0.977            0.011            1.636            0.029
                                              Caa2        Caa1           134              42             1.176            0.009            0.561            0.290            2.046            0.028
                                              Caa3        Caa2            69              18             2.657            0.000            2.066            0.119            0.378            0.613

                                              Weighted mean
                                              variation of rating intensity                              2.957                             2.847                             2.593

                                 The table shows rating upgrade intensities for companies that held a rating by Moody’s in the observation period January 1994–December 2005. We include only companies with
                                 long-term senior unsecured credit ratings by both Moody’s and S&P in the observation period. We further employ rating data for the period January 1990–December 1993 to
                                 condition upon previous rating actions. Rating data is extracted from Bloomberg. The first two columns report the type of rating transition studied, eg, the first row exhibits rating
www.thejournalofcreditrisk.com




                                 changes from “Aa1” to “Aaa”. We concentrate on rating upgrades by one rating notch. The third column shows the number of all potential rating transitions out of the “From” rating
                                 class, ie, censored and uncensored observations, whereas the fourth column provides all observed rating transitions out of the “From” into the “To” rating class. Columns 5 and 6
                                 provide results for the specification with upgrades by S&P as time-varying covariates. Columns 7 and 8 show results for the specification with positive Watchlist entries by S&P
                                 as time-varying covariates. Columns 9 and 10 provide results for the specification with higher, ie, less risky, ratings by S&P as time-varying covariates. In all three specifications
                                 we omit results for the covariate of previous upgrades by Moody’s. We use a proportional intensity regression model by maximizing the partial likelihood of every rating upgrade
                                 by one full notch to the neighboring rating class. Each line thus comprises three separate regressions with discrete, time-varying covariates by S&P and covariates by Moody’s
                                 which are constant over time. We exclude the rating sequence “Ca” to “Caa3” to allow for previous upgrades by Moody’s. A positive (negative) coefficient implies that the upgrade
                                 intensity is higher (lower) compared with the case of no upgrade (columns 5 and 6), no positive Watchlist entry (columns 7 and 8), or no higher rating (columns 9 and 10) by
                                 S&P. The last line shows the mean variation of the rating intensity, exp.ˇ1 /, over all one-notch rating transitions, weighted by the number of potential rating changes out of the
                                 starting rating class. We furthermore employ strata for regions, sectors and whether the rating change occurred in a month with a below-average rating downgrade ratio. We omit
                                 estimation results if we have too few observations of the respective covariate and mark these cases “N/A”. We employ the robust sandwich estimate of Lin and Wei (1989) for the
                                 covariance matrix.




                                                                                                                                                                                                                        111
                                                                                                                                                                          112
The Journal of Credit Risk




                                                                                                                                                                          A. Güttler
                                 TABLE 6 Rating upgrade intensities for issuers rated by S&P conditional on Moody’s rating information. [Table continues on next page.]

                                                                                          Upgrade              Watchlist positive          Rating higher
                                            Ratings                                      by Moody’s               by Moody’s                by Moody’s
                                           ‚ …„ ƒ            Potential    Actual      ‚      …„      ƒ         ‚      …„        ƒ        ‚      …„       ƒ
                                          From    To         changes     changes     Coefficient p value       Coefficient p value        Coefficient p value

                                          AAC       AAA          25           4          N/A          N/A         N/A          N/A          N/A         N/A
                                          AA        AAC          65           7          N/A          N/A         N/A          N/A          N/A         N/A
                                          AA        AA          151          35         0.431        0.301       1.329        0.002        1.059       0.038
                                          AC        AA          282          60         0.692        0.027       0.881        0.011        0.969       0.002
                                          A         AC          335          88         0.812        0.001       0.443        0.282        0.137       0.624
                                          A         A           392         128         0.310        0.211       1.019        0.000        0.265       0.214
                                          BBBC      A           441         139         0.872        0.000       0.769        0.001        1.122       0.000
                                          BBB       BBBC        500         170         0.948        0.000       0.929        0.000        0.541       0.002
                                          BBB       BBB         366         162         1.144        0.000       1.349        0.000        0.476       0.010
Volume 7/Number 1, Spring 2011




                                          BBC       BBB         293         128         0.515        0.007       1.253        0.000        0.485       0.034
                                          BB        BBC         313         127         1.062        0.000       1.320        0.000        0.762       0.001
                                          BB        BB          380         124         0.962        0.000       0.044        0.920        0.978       0.001
Technical Report



                                 TABLE 6 Continued.

                                                                                                             Upgrade                       Watchlist positive                   Rating higher




                                                                                                                                                                                                                           Lead–lag relationships and rating convergence among credit rating agencies
                                               Ratings                                                      by Moody’s                        by Moody’s                         by Moody’s
                                              ‚ …„ ƒ                   Potential         Actual          ‚      …„      ƒ                  ‚      …„        ƒ                 ‚      …„       ƒ
                                             From    To                changes          changes         Coefficient p value                Coefficient p value                 Coefficient p value

                                             BC           BB               431              138             1.199            0.000             1.429            0.000            0.503            0.035
                                             B            BC               295              119             1.039            0.000             1.603            0.000            0.703            0.003
                                             B            B                172               58             1.623            0.002             0.152            0.817            0.019            0.952
                                             CCCC         B                106               31             1.176            0.122             0.122            0.781            0.295            0.679
                                             CCC          CCCC              52               16             1.038            0.149             1.238            0.313            1.577            0.105
                                             CCC          CCC               15                6              N/A              N/A               N/A              N/A              N/A              N/A

                                             Weighted mean
                                             variation of rating intensity                                  2.513                              2.777                             1.878

                                 The table shows rating upgrade intensities for companies that held a rating by S&P in the observation period January 1994–December 2005. We include only companies with
                                 long-term senior unsecured credit ratings by both Moody’s and S&P in the observation period. We further employ rating data for the period January 1990–December 1993 to
                                 condition upon previous rating actions. Rating data is extracted from Bloomberg. The first two columns report the type of rating transition studied, eg, the first row exhibits rating
                                 changes from “AAC” to “AAA”. We concentrate on rating upgrades by one rating notch. The third column shows the number of all potential rating transitions out of the “From” rating
                                 class, ie, censored and uncensored observations, whereas the fourth column provides all observed rating transitions out of the “From” into the “To” rating class. Columns 5 and
www.thejournalofcreditrisk.com




                                 6 provide results for the specification with upgrades by Moody’s as time-varying covariates. Columns 7 and 8 show results for the specification with positive Watchlist entries by
                                 Moody’s as time-varying covariates. Columns 9 and 10 provide results for the specification with higher, ie, less risky, ratings by Moody’s as time-varying covariates. In all three
                                 specifications we omit results for the covariate of previous upgrades by S&P. We use a proportional intensity regression model by maximizing the partial likelihood of every rating
                                 upgrade by one full notch to the neighboring rating class. Each line thus comprises three separate regressions with discrete, time-varying covariates by S&P and covariates by
                                 Moody’s which are constant over time. We exclude the rating sequence “CC” to “CCC ” to allow for previous upgrades by S&P. A positive (negative) coefficient implies that the
                                 upgrade intensity is higher (lower) compared with the case of no upgrade (columns 5 and 6), no positive Watchlist entry (columns 7 and 8) or no higher rating (columns 9 and 10)
                                 by Moody’s. The last line shows the mean variation of the rating intensity, exp.ˇ1 /, over all one-notch rating transitions which is weighted by the number of potential rating changes
                                 out of the starting rating class. We furthermore employ strata for regions, sectors and whether the rating change occurred in a month with a below-average rating downgrade ratio.
                                 We omit estimation results if we have too few observations of the respective covariate and mark these cases “N/A”. We employ the robust sandwich estimate of Lin and Wei (1989)
                                 for the covariance matrix.




                                                                                                                                                                                                                           113
114   A. Güttler

      additions reveal vital information to market participants. Specifically, our relative
      comparison of the downgrade intensities provides evidence that Moody’s downgrade
      intensities seem to be more strongly affected by both downgrades and lower rating
      levels by S&P than vice versa. The latter results appear to confirm those presented in
      a paper by Welch (1990) in which he shows that stock recommendations by securities
      analysts have a significant influence on the stock recommendations of the subsequent
      two analysts. However, we find no differences in downgrade intensities in the case of
      negative Watchlist additions.

      4.2 Upgrades
      Table 5 on page 110 shows results for all upgrades by one rating notch by Moody’s
      and rating actions by S&P as covariates of interest. In all three different specifications
      of the table, z2 equals 1 if the issuer was previously upgraded by Moody’s. Column 5
      shows coefficients where z1 .t/ equals 1 if the respective issuer was upgraded by S&P
      at t . Sixteen out of the seventeen feasible regression coefficients are significantly
      different from zero. Moody’s upgrade intensity is 196% higher if there was an upgrade
      by S&P. Column 7 shows coefficients where z1 .t / equals 1 if the respective issuer
      was put on Watchlist positive by S&P at t . We find that the weighted mean variation of
      the upgrade intensity is 185% higher in the case of a positive Watchlist entry by S&P.
      Column 9 shows coefficients where z1 .t / equals 1 if the respective firm had a higher,
      ie, less risky, rating by S&P at t . One out of the eighteen separate regressions did
      not converge due to an insufficient number of observations. These results are slightly
      weaker, as the upgrade intensity is only 159% higher.
         Table 6 on page 112 displays corresponding results for all upgrades by one rating
      notch by S&P and rating actions by Moody’s as covariates of interest. In all three
      different specifications of the table, z2 equals 1 if the issuer was previously upgraded
      by S&P. Three out of the eighteen separate regressions did not converge due to an
      insufficient number of observations. Column 5 exhibits coefficients where z1 .t / equals
      1 if the respective issuer was upgraded by Moody’s at t . Eleven out of the fifteen
      feasible regression coefficients are significantly different from zero. The upgrade
      intensity is 151% higher if there was an upgrade by Moody’s. Comparing these results
      to those in column 5 of Table 5 on page 110, we find that Moody’s upgrade intensity
      is 18% larger (1.96 versus 2.51) in the case of an upgrade by S&P than vice versa.
      Interestingly, these upgrade intensity increases are much larger than the results shown
      in column 7 of Table 3 on page 106 and Table 4 on page 108 for negative Watchlist
      additions. Column 7 shows coefficients where z1 .t / equals 1 if the respective issuer
      was put on Watchlist positive by Moody’s at t . The weighted mean variation of the
      upgrade intensity is 178% higher in the case of a positive Watchlist entry by Moody’s.
      This result is almost on a par with the results in column 7 of Table 5 on page 110.

      The Journal of Credit Risk                                 Volume 7/Number 1, Spring 2011
                Lead–lag relationships and rating convergence among credit rating agencies         115

Column 9 exhibits coefficients where z1 .t / equals 1 if the respective firm had a higher,
ie, less risky, rating by Moody’s at t . These results are much weaker compared with
the two other specifications of the table, as S&P’s upgrade intensity is only 88%
higher. Comparing these results to those in column 9 of Table 5 on page 110, we find
that Moody’s upgrade intensity is 38% larger (2.59 versus 1.88) in the case of a higher
rating by S&P than vice versa.
   To summarize, we find that rating actions by one CRA cause significantly higher
rating intensities for most one-notch upgrades by the other CRA. We thus add evi-
dence to Hand et al (1992), who find weak results for positive Watchlist additions,
as we observe that positive Watchlist additions by the other CRA increase upgrade
intensities even more sharply than negative Watchlist additions increase downgrades.
Specifically, our comparison of upgrade intensities provides evidence that Moody’s
upgrade intensities seem to be more strongly affected by both upgrades and higher
rating levels by S&P. The latter results again appear to confirm those presented in a
paper by Welch (1990). However, we find no different upgrade intensities in the case
of positive Watchlist additions.


4.3 Robustness checks
The central assumption of the Cox regression is that of proportionality, ie, that a unit
variation in a covariate causes the baseline intensity to be multiplied by the exponential
of the covariate’s coefficient. Thus, enlargements and reductions of each covariate are
proportional. As a robustness check we test this proportionality assumption (Gramb-
sch and Therneau (1994)). For the 206 valid regression coefficients of interest in
Tables 3–6, the employed specification test rejects the proportionality hypothesis in
twenty-five cases (at the 5% significance level).16 For these exceptions we apply the
graphical Kaplan–Meier approach, which allows the comparison of survival curves
for different covariate values. In our case of binary covariates we obtain two survival
curves for each case where the survival probability should be lower for covariates
that equal one, ie, where we observe a positive rating intensity for the respective
covariate. The graphical comparison should also clarify whether the survivor curves
cross. We find in only eight of the twenty-five cases an intersection of the two survivor
curves. Otherwise we find no rejection of the proportionality hypothesis through the
use of the Kaplan–Meier approach. Given that the proportionality hypothesis is hardly
rejected in either of the two tests, our Cox regression model seems to be correctly
specified.


16   Results are not shown here but are available upon request.


Technical Report                                                  www.thejournalofcreditrisk.com
116   A. Güttler

      5 DISCUSSION AND CONCLUSION

      Using a sample of issuers rated by both Moody’s and S&P, in this paper we have
      analyzed whether one of the two dominant CRAs adjusts its ratings in a timelier
      manner than the other. In addition, we have examined whether there is a tendency
      by one (or both) of the CRAs toward the other CRA’s rating. We find evidence that
      Moody’s rating change intensities are higher given a rating change by S&P. This
      effect is somewhat stronger in the case of downgrades than for upgrades and seems
      to be tentative evidence that S&P assigns ratings in a timelier manner than Moody’s.
      Second, we find that the tendency toward rating convergence is stronger for Moody’s
      than for S&P.
         Despite delivering novel results, we admit the following limitations: first, due to
      data restrictions, the period covered in the study is too short to determine any credit-
      cycle effects. Research done with credit ratings of one single CRA typically covers a
      longer horizon. We nevertheless try to control for credit cycle effects by incorporating
      the state of the credit cycle as a control variable in our specifications. Second, our
      results might – at least partly – be driven by different rating methodologies. Whereas
      Moody’s uses an expected loss approach that incorporates the probability of default
      and the loss severity, S&P only takes account of the probability of default. With
      particular regard to rating convergence, however, practitioners and regulators do not
      seem to differentiate much between Moody’s and S&P. For example, the often used
      junk rating threshold is defined in the same way as our rating matching. Third, the
      refined rating scale Caa1, Caa2, Caa3 was introduced by Moody’s around 1997. This
      may lead to an over-representation of the transitions to rating Caa (Caa2) in the period
      1994–97. The scale CCCC, CCC, CCC was introduced earlier by S&P, which may
      bias our results for the first years.
         A further potential criticism might be that we only include Moody’s and S&P in
      our study and disregard all other CRAs. Of course, the inclusion of additional rat-
      ing actions by the other CRAs might provide interesting insights into the lead–lag
      relationships and the rating convergence between large and smaller CRAs. However,
      since Moody’s and S&P are by far the largest rating agencies for securities in capital
      markets in the world – as is demonstrated by their huge share of the market – we are
      able to cover a large dataset solely with those issuers rated by both. The inclusion of
      further, much smaller rating agencies would decrease the available dataset substan-
      tially. Even the number three in the market for credit ratings, Fitch Ratings, is not
      large enough to possess a long and broad history of corporate ratings. Other rating
      agencies do not even operate worldwide, such as the Japanese rating agencies JCR
      and R&I, or are limited to a specific sector, such as AM Best, which specializes in
      the insurance and banking sector.

      The Journal of Credit Risk                                Volume 7/Number 1, Spring 2011
             Lead–lag relationships and rating convergence among credit rating agencies            117

   Further research might enlarge the scope of our study to include structured finance
ratings. The financial crisis that began in 2007 has raised serious questions as to
whether the leading CRAs reacted quickly enough to warn investors about the risks
of investing in securities backed by US subprime mortgages. Including Fitch Ratings
in this area as a third CRA might be possible since it commands a much larger market
share in this particular segment of the rating business.


REFERENCES
Abad-Romero, P., and Robles-Fernandez, D. (2006). Risk and return around bond rating
  changes: new evidence from the Spanish stock market. Journal of Business Finance
  and Accounting 33(5), 885–908.
Altman, E. I., and Kao, D. L. (1992).The implications of corporate bond ratings drift. Financial
  Analysts Journal 48, 64–67.
Baker, H. K., and Mansi, S. A. (2002). Assessing credit rating agencies by bond issuers and
  institutional investors. Journal of Business Finance and Accounting 29(9), 1367–1398.
Bangia, A., Diebold, F., Kronimus, A., Schagen, C., and Schuermann, T. (2002). Ratings
  migration and the business cycle, with applications to credit portfolio stress testing. Jour-
  nal of Banking and Finance 26(2), 445–474.
Bannier, C. E., and Tyrell, M. (2006). Modelling the role of credit rating agencies. Do they
  spark off a virtuous circle? Working Paper, University of Frankfurt.
Barron, M. J., Clare, A. D., and Thomas, S. H. (1997). The effect of bond rating changes and
  new ratings on UK stock returns. Journal of Business Finance and Accounting 24(3),
  497–509.
BBA (2006). BBA Credit Derivatives Report 2006.
Bernhardt, D., Campello, M., and Kutsoati, E. (2006). Who herds? Journal of Financial
  Economics 80(3), 657–675.
BIS (2007). BIS Quarterly Review.
Blume, M. E., Lim, F., and MacKinlay, A. C. (1998). The declining credit quality of US
  corporate debt: myth or reality? Journal of Finance 53(4), 1389–1414.
Boot, A., Greenbaum, S., and Thakor, A. (1993). Reputation and discretion in financial
  contracting. American Economic Review 83(5), 1165–1183.
Cantor, R., and Packer, F. (1997). Differences of opinion and selection bias in the credit
  rating industry. Journal of Banking and Finance 21(10), 1395–1417.
Christensen, J. H. E., Hansen, E., and Lando, D. (2004). Confidence sets for continuous-
  time rating transition probabilities. Journal of Banking and Finance 28(11), 2575–2602.
Covitz, D. M., and Harrison, P. (2003). Testing conflicts of interest at bond ratings agencies
  with market anticipation: evidence that reputation incentives dominate. Working Paper,
  Finance and Economics Discussion Series no. 2003-68, Board of Governors of the US
  Federal Reserve System.
Cox, D. R. (1972). Regression models and life tables. Journal of the Royal Statistical Society
  B 34, 187–220.
Economist, The (2005). Credit-rating agencies. Who rates the raters? (March 26.)


Technical Report                                                 www.thejournalofcreditrisk.com
118   A. Güttler

      Feinberg, M., Shelor, R., and Jiang, J. (2004). The effect of solicitation and independence
        on corporate bond ratings. Journal of Business Finance and Accounting 31(9), 1327–
        1353.
      Figlewski, S., Frydman, H., and Liang, W. (2008). Modeling the effect of macroeconomic
        factors on corporate default and credit rating transitions. Working Paper, NYU Stern
        School of Business.
      Grambsch P. M., and Therneau, T. M. (1994). Proportional hazards test and diagnostics
        based on weighted residuals. Biometrika 81(3), 515–526.
      Güttler, A., and Raupach, P. (2010). The impact of downward rating momentum. Journal of
        Financial Services Research 37(1), 1–23.
      Hamilton, D. T., and Cantor, R. (2004). Rating transitions and defaults conditional on watch-
        list, outlook and rating history. Moody’s Special Comment.
      Hamilton, D. T., Cantor, R., and Ou, S. (2002). Default and recovery rates of corporate bond
        issuers. Moody’s Special Comment.
      Hand, J. R. M., Holthausen, R. W., and Leftwich, R. W. (1992). The effect of bond rating
        agency announcements on bond and stock prices. Journal of Finance 47(2), 733–752.
      Jewell, J., and Livingston, M. (1999). A comparison of bond ratings from Moody’s, S&P
        and Fitch IBCA. Financial Markets, Institutions, and Instruments 8(4), 1–45.
      Kavvathas, D. (2001). Estimating credit rating transition probabilities for corporate bonds.
        Working Paper, University of Chicago.
      Lando, D., and Skødeberg, T. M. (2002). Analyzing rating transitions and rating drift with
        continuous observations. Journal of Banking and Finance 26(2), 423–444.
      Lin, D. Y., and Wei, L. J. (1989). The robust inference for the Cox proportional hazards
        model. Journal of the American Statistical Association 84, 1074–1078.
      Löffler, G. (2007). Profits first, or clients first? Some lessons from Moody’s stock price.
        Working Paper, University of Ulm.
      Millon, M. H., and Thakor, A. V. (1985). Moral hazard and information sharing: a model of
        financial information gathering agencies. Journal of Finance 40(5), 1403–1422.
      Moody’s (2010).The performance of Moody’s corporate debt ratings. December 2009 Quar-
        terly Update. Moody’s Investors Service, Special Comment.
      Morgan, D. (2002). Rating banks: risk and uncertainty in an opaque industry. American
        Economic Review 92(4), 874–888.
      Nickell, P., Perraudin, W., and Varotto, S. (2000). Stability of rating transitions. Journal of
        Banking and Finance 24(1), 203–227.
      Norden, L., and Weber, M. (2004). Informational efficiency of credit default swap and stock
        markets: the impact of credit rating announcements. Journal of Banking and Finance
        28(11), 2813–2843.
      Perry, L. G. (1985). The effect of bond rating agencies on bond rating models. Journal of
        Financial Research 8, 307–315.
      Poon, W. P. H., and Firth, M. (2005). Are unsolicited credit ratings lower? International
        evidence from bank ratings. Journal of Business Finance and Accounting 32(9), 1741–
        1771.
      Ramakrishnan, R. T. S., and Thakor, A. V. (1984). Information reliability and a theory of
        financial intermediation. Review of Economic Studies 51(3), 415–432.


      The Journal of Credit Risk                                     Volume 7/Number 1, Spring 2011
             Lead–lag relationships and rating convergence among credit rating agencies      119

Steiner, M., and Heinke, V. G. (2001). Event study concerning international bond price
  effects of credit rating actions. International Journal of Finance and Economics 6(2),
  139–157.
Trueman, B. (1994). Analyst forecasts and herding behavior. Review of Financial Studies
  7(1), 97–124.
Welch, I. (1990). Herding among security analysts. Journal of Financial Economics 58(3),
  369–396.
Wendin, J., and McNeil, A. J. (2006). Dependent credit migrations. Working Paper, ETH
  Zurich.




Technical Report                                            www.thejournalofcreditrisk.com

								
To top