Section C. Consistency Across Сектора - 3 – Август 2000 Кредитные рейтинги и дополнительные источники информации...

^ Section C. Consistency Across Сектора


The Basel Committee has proposed a greater reliance on external ratings to determine the risk

weighting of banking book assets. According to the proposed new standardised approach, the

risk weighting may depend both on credit rating and sector (i.e. sovereigns, banks, or

corporates). Sovereign credits have reduced risk weightings relative to banks at all ratings A

and higher,89 and at all ratings relative to corporates at BBB and higher. Bank credits have

lower risk weightings than corporate credits at A and BBB ratings. At other ratings, the

charges are identical across Сектора.

This paper examines the extent to which sectoral differences exist for default rates and

recoveries in cases of default. Consistent with the distinctions of the new approach, we focus

on the differences among sovereign, bank, and corporate ratings. When biases are apparent,

are they consistent with the asymmetries in the weights proposed by the Basel Committee?

Identical risk weights across Сектора may be most effective when ratings in different Сектора

reflect similar levels of expected loss; different weights may be most effective when ratings

reflect different levels of expected loss.

Both the literature on this topic, and our own analysis to follow, are based on the historical

experience of the ratings of the two major agencies, Moody’s Investors Service and Standard

and Poor’s. Since a downgrade to a lower rating implies that an eventual default is more

likely, we also examine sectoral differences in the degree of downgrade risk.

We also investigate the extent to which obligor domicile has affected the association of credit

ratings with default probabilities, recoveries, and downgrades. Most historical credit rating

and default data are the ratings of US entities, and issued by Moody’s and S&P, agencies

headquartered in the United States. If, as some suggest, the new standardised approach greatly

increases the importance of Moody’s and S&P’s ratings of non-US entities, then the degree to

which these ratings are consistent across obligor domicile is of interest. International

differences in accounting and legal systems, business practices, and the role of government in

the economy may make it difficult to compare the default risk of bond issuers domiciled in

different countries.

Our empirical results suggest that Moody’s and S&P have not been completely successful in

calibrating their ratings so that like-rated bonds of different Сектора are exposed to similar

levels of default risk. US banks experienced significantly more defaults than US industrial

firms over this period, taking the year and Moody’s rating as given. US non-bank financial

firms have had greater defaults than US industrial firms, taking the year and S&P rating as

given. These results are at odds with the proposal that, for some rating levels, bank and

ценные бумаги firm obligations carry a lower capital requirement than an otherwise identical

liability of an industrial firm. Nevertheless, it is worth noting that our sample period may be

somewhat atypical for the US in that it included an interval in which an unusually large

number of depository institutions failed.

89 Here we ignore the proposed first option for weighting claims on banks, based on the rating of the sovereign in which it is

domiciled, and consider only the second option based on the bank rating itself.


Limited availability of data on the ratings of non-US firms made it difficult to reach

statistically robust conclusions as to whether the rating agencies have been more successful in

calibrating their ratings across geographically distinct issuers. Apparent geographical

discrepancies in default rates are no longer significant once we simultaneously control for the

credit rating and time period.

^ Overview of literature

The literature on sectoral differences in the measurement of credit risk is fairly limited. One

area of focus is split ratings, that is, when the same issuer receives different ratings from the

various agencies. The literature also covers the association of ratings and spreads, and the

association of ratings with subsequent outcomes, including rating transition and default. A

number of the studies report evidence of sectoral differences in indirect measures of the risk

of default loss, such as yield spreads, the relative likelihood of subsequent upgrades and

downgrades, recovery rates, and the frequency of split ratings, which are understood to reflect

uncertainty about the issuer’s credit quality. In particular, results in this literature suggest that,

holding ratings constant, bank bonds may be riskier than industrial bonds, but the results are

mixed for comparisons between sovereign and corporate bonds and for geographical

distinctions. Few papers address default rates directly, and we know of no previous work that

undertakes a systematic statistical analysis of sectoral differences in default rates.

Turning to split ratings, Donald Morgan (1998) measured the frequency of credit rating

agency disagreement in the banking versus other Сектора. Consistent with Cantor and Packer

(1994), he finds that split ratings tend to be more frequent in banking than in other Сектора.

Cantor and Packer (1995) find that split ratings are more common for lower-rated sovereigns

than lower-rated US corporates and less common for higher rated (AAA/AA) rated sovereigns

than higher-rated US corporates. These results suggest greater opacity in the measurement of

credit risk for banks relative to corporates, for lower-rated sovereigns relative to corporates,

and less opacity for higher-rated sovereigns relative to corporates. The first result may be

inconsistent with one of the asymmetries of the new proposed capital charges (the lower

capital charge for banks relative to corporate credits at some rating levels). The last result may

be consistent with another asymmetry (the lower capital charge for sovereigns relative to

corporate credits at some rating levels).

With regard to obligor domicile, two papers merit mention. An examination of split ratings by

Beattie and Searle (1992) suggests that agencies judge issuers from their own country more

leniently. However, Cantor and Packer (1994) found that, for ratings of international banks,

observed differences between home and foreign ratings reflected principally differences in the

scales of individual ratings agencies, rather than home-country bias.

The relationship of ratings and spreads also appears to differ by sector. Cantor and Packer

(1995), and Jackson and Perraudin (1999) document a tendency for spreads to be higher for

sovereign credits at lower credit ratings (BBB and lower) than similarly rated corporate

credits. While this may be due to lower expected recovery on defaulted sovereign bonds than

corporate bonds, there is too little recent history of defaulted sovereign bonds to test this

explanation. Spreads on bank debentures also appear to have been greater throughout the

1990s than spreads on comparably rated corporate bonds (Jackson and Perraudin, 1999).

Recoveries might account for some of this difference, as Altman and Kishore (1996) report

US industry differences in “recovery” (i.e. salvage) rates on defaulted bonds, and finds that

financial institutions tend to have lower recovery rates than industrials.


Nickell, Perraudin, and Varotto (2000) focus on rating “transitions” (also known as rating

“migrations”), and find that banks tend to have less stable ratings than industrials. Higher

rated banks have more downgrades, but lower-rated banks are upgraded more often than

lower-rated industrial issuers are. Jackson and Perraudin (1999), drawing on a table in the

above study, report that over 1-year horizons, banks rated B suffer fewer bond defaults than

B-rated industrial issuers, although the difference is not statistically significant. Comparing

sovereigns with other issuers, S&P (1999) has reported greater stability for sovereign ratings

than corporate ratings. However, S&P’s brief does not take account of the far greater

frequency of withdrawn ratings in the corporate sector, nor does it address issues of statistical

significance raised by the relatively small number of observations for sub-prime sovereigns.

With regard to distinctions between US and non-US companies, Nickell et al. (1998) find that

higher-rated Японияese firms are more likely to be downgraded by Moody’s and that lowerrated

Японияese firms were less likely to be upgraded. Another analysis of ratings in Япония

suggests that the Японияese ratings of Moody’s Investors Service may be relatively tough, since

fewer defaults have been observed over time in Япония than would have been predicted by

Moody’s ratings, despite Япония’s stagnant economic conditions in the 1990s (JCIF, 1999).

^ Limitations of the analysis

Our empirical analysis uses the ratings history of rated issuers from the two largest credit

rating agencies in the world, Moody’s (for issuers with rated bonds outstanding during the

period 1970-1998), and S&P (for the period 1981-1998). The focus on these two agencies is

an unavoidable limitation of the analysis. Clearly the proposed framework does not envision

the external credit ratings that apply to be only those of the largest two agencies; however,

these are the only two rating agencies that provide sufficient data for a sectoral comparison of

the association of ratings with defaults, recoveries, and downgrades.

Another limitation of the analysis is that even Moody’s and S&P do not provide historical

data on their ratings in all Сектора. The performance of ценные бумаги rated in the lucrative and

extensive areas of municipal finance and structured finance are not part of the publicly

available databases of Moody’s and S&P. Some questions have been raised with regard to the

stability of ratings standards in the area of structured finance (Cantor and Packer, 1994). The

municipal finance area is one in which ratings are suggested to be much tougher and

associated with lower default probabilities than other Сектора even by the rating agencies

themselves (for example, Moody’s, 1999). However, the data are simply not available to

investigate the performance of ratings in those industry Сектора relative to other Сектора.

To the extent that the disclosure of ratings histories is a signal of the stability and

dependability of those histories, we should expect the ratings inconsistencies observed across

Сектора and across countries to be a lower bound of the unobserved inconsistencies that may

exist more generally.

^ Descriptive statistics: default rates by Сектора

Table 1 reports the one-year default rates by initial rating and sector of issuer, according to the

Moody’s database, which covers defaults between 1970 and 1998. The default rates are

calculated using estimates of mid-period denominators, constructed by subtracting half of the

number of ratings withdrawn (over the whole period) from the number of rated issuers at the


beginning of the period. Rating withdrawals are generally not adverse credit events (Carty,


Default rates are measured across seven Сектора - US banks, other US financial firms, US nonfinancial

firms, non-US banks, other non-US financial firms, non-US non-financial firms, and

sovereigns. In terms of overall default rates, the default rate for US banks of 1.43% is about

the same as that of US non-financial firms. Both are well above the default rate of 0.50% for

non-US non-financial firms, which in turn is higher than the 0.08% default rate for non-US

banks. Based on these numbers alone, it appears that US firms are riskier than non-US firms

are, and non-US banks are particularly safe. Sovereigns had incurred just one bond default

since 1983 on a foreign-currency obligation rated by Moody’s.90

However, overall sectoral default rates indicate little about sectoral differences in the

correspondence of ratings to default, since the underlying ratings composition of each sectoral

pool of borrowers is likely to differ. For instance, if the average ratings of US banks were

much higher than those of US non-financial firms, the similar default rates would imply that

ratings were relatively lenient for banks. Or, if the average ratings of non-US firms were

much higher than for US firms, the observed difference in the default rates may result purely

from this difference of ratings composition.

In Table 2, we control for the ratings composition and report the default rates for US and non-

US firms at each Moody’s rating level. We report both one-year and five-year default rates. In

both cases, estimates of mid-period denominators are constructed by subtracting half the

number of withdrawn ratings, as before.91 The five-year default rates are the average

outcomes for annual cohorts from January 1970 to January 1994. At a one-year horizon, US

firms rated Ba show a slightly higher propensity to default (1.3% vs. 0.8%); those rated B and

in the Caa/Ca/C range also show a higher propensity to default (6.9% vs. 2.4% for B’s, and

20% versus 15% for CCCs). Not surprisingly, measuring defaults at a five-year horizon

results in higher default rates at each rating level. At each rating category other than Aaa, the

likelihood of default over a five-year horizon is noticeably greater for US firms. These figures

suggest that, during the sample period, Moody’s was more conservative in its rating of non-

US than US firms, but with very limited data for non-US companies at the lower rating levels,

it is difficult to reach a definitive conclusion from this comparison.

In Table 3, we again control for the ratings composition and this time report the one and fiveyear

default rates for US banks and non-financial firms at each Moody’s rating level. At a

one-year horizon, US banks rated Ba show a slightly higher propensity to default than US

non-financial firms (1.9% vs. 1.3%), and the default rates are higher in the B and Caa-C

ranges as well (13.8% vs. 6.6% for B’s, and 56.4 versus 18.8% for Caa-C). Again, measuring

defaults at a five-year horizon results in more defaults at each rating level, and more striking,

but consistent, sectoral differences. At each rating category other than Aaa, the likelihood of

default over a five-year horizon is significantly greater for US banks. At least during the

90 The default was a late payment on a Eurobond by Pakistan in 1998, which was not considered a default under S&P’s

definition because the coupon was disbursed within the contractual grace period.

91 The implicit assumption in this construction is that ratings withdrawals are distributed evenly through the period. The

mid-period denominator produces a measure of the default rate that is close to the average “hazard rate” of default.


sample period, Moody’s was more conservative in its rating of US non-financial firms than

US banks.92

^ Probit regression analysis

Of course, the distinctions noted in Tables 2 and 3 could be a reflection of factors other than

genuine sectoral differences. Differences over time in the frequency of sectoral ratings,

combined with different default patterns across time, could account for the differences. For

instance, there has been a much greater percentage of Moody’s ratings, particularly at the

lower rating levels, assigned to non-US borrowers since the mid-1990s than previously. It

may also be stated that, while the condition of US firms improved from the late 1980s-early

1990s to the mid-1990s, non-US firms were going through different economic conditions in

their respective countries.

Table 4 presents summary results for four multivariate probit models that we estimated, where

the dependent variable, estimated over thoСШАnds of “issuer-years”, is the probability that the

issuer defaulted that year. (Full details of the estimated model coefficients from which these

statistics are derived appear in Appendix Table A1.) The explanatory variables include an

indicator for each year to control for time-varying effects, an indicator variable for each rating

level below A3 or A-, and indicator variables for four broad issuer classes: US non-financial

firms, US banks, other US financial firms, and all non-US firms.93 (Sovereigns were excluded

from this exercise because, with only a few dozen low-rated issuer-years skewed toward the

late 1990s, both the actual and expected number of defaults were clearly too low to make

reliable inferences.)

Because the probit representation is based on a non-linear multivariate function, individual parameter estimates are difficult to interpret out of context. To facilitate interpretation of the results, we compute an estimated probability of default for each sector over all of the issueryear observations in the sample, using the estimated parameters for year, rating and sector, and for each computation assuming that all of the observations came from a single sector. The sectoral statistics presented in Table 4 represent the difference in this estimated probability of default for the indicated sector relative to that for the US non-financial sector. For example, for the restricted model in the first column, in which the three classes of US firms are treated as a single group, the average “fitted” one-year default probability is estimated to be a hair higher (by 0.09%) for non-US firms than US firms. (Note, however, that the difference is not distinguishable from zero with 95% confidence.) Since the amount of non-US default data is limited, it is impossible to derive definitive conclusions from the analysis. However, it may be that the apparent “home bias” in ratings in Table 2 is a result of time effects.

92 Higher default rates for US banks relative to US non-financial firms are also apparent from S&P data (available for years 1981-1998).

93 The dearth of same-year defaults of A and Aa-rated credits (a total of 2 in the Moody’s sample and 6 for S&P, in both cases out of thoСШАnds of issuers) posed practical obstacles for including dummy variables for high ratings in the probit specification.


The results in the next column, however, show that the higher frequency of US bank defaulters at a given Moody’s rating (compared to US non-financial firms, as documented in Table 3), is both robust to time effects and statistically significant. The average default rate is 0.77% higher for the full sample, and a striking 2.29% higher for the “junk bond” portion of the sample. The results are slightly different for S&P ratings, with other US financial firms showing a statistically significant elevation in default rate.


While the probit results are indicative of statistical significance, it is important to note that the default-rate discrepancies, with regard to Moody’s ratings of US banks and US non-financial firms, result mainly from one historical episode – the thrift crisis of the late 1980s and early 1990s. Table 5 indicates that 21 of the 33 bank defaults for the whole period of 1970-98 were of US thrifts in 1989-91. More than 40% of the 49 rated thrifts at the beginning of 1989 defaulted. In retrospect, Moody’s greatly overestimated the ability of thrifts to make it through the years 1989-91 without default. To the extent that there have been dramatic changes in the US bank regulatory regime, and the methodology for rating banks has been adjusted to account for them, bank ratings have not necessarily been more lenient, that is, associated with higher default rates at a given credit rating, subsequently.

Interestingly, the thrift crisis did not have the same impact on estimated sectoral default rates by S&P rating. Only 10 issuers in the US bank category (which includes thrift institutions) rated by S&P defaulted during 1989-91. The disparate experiences arose at least in part because S&P rated substantially fewer of the speculative-grade depository institutions than Moody’s at that time.

^ Recovery rates

Since expected losses are a function of both the expected probability of default and the expected severity of loss given default, sectoral differences in the probability of default at given ratings do not necessarily imply sectoral differences in expected losses. If they were counter-balanced by differences in recovery rates, then the expected losses could be the same across Сектора. And in contrast to S&P, which says that its ratings are meant to rank the relative likelihood of corporate default, Moody’s explicitly indicates that it includes considerations of recovery in its corporate ratings.

The most detailed paper to date on recoveries by industry is by Altman and Kishore (1996), the results of which are reproduced in Caouette et al (1996). Altman and Kishore use S&P’s convention of measuring recoveries as the market price of the bonds as a percentage of face value shortly after default. The recoveries on the defaults of the 66 financial institutions averaged 35.7%, below the 41% average on all 696 defaults. The lower default rate did not appear to be a function of lower seniority since around two-thirds of the financial institution issues were senior secured or senior unsecured obligations, relative to less than one-third for the entire sample. Thus, recoveries have been lower for financial institutions, opposite to what we should expect if recoveries were counterbalancing the higher default rates of banks.

Moody’s database allows for a more recent examination of the degree to which recoveries, and by extension, expected losses, could differ by industry. Moody’s measures a recovery rate as the secondary market prices of a bond 30 days after default. The database reports


recoveries on the bonds of 595 issuers that were rated by Moody’s and defaulted between 1970 and 1998. In the case of multiple classes of bonds outstanding for any one firm, we take the weighted average of recoveries for that firm.

In Table 6, we report the recovery rate on the defaulted corporate bonds, broken out in the first two rows by US bank versus US non-financial firms, and then in the next two rows, by US versus non-US firms. (The absence of a separate category of US non-bank financial firms explains why the first two rows do not quite add up to the third.) The US bank recoveries are starkly lower, with an average recovery of 22% versus around 40% for non-financial firms.

The statistic resulting from the t-test on the differences is 3.8, indicating statistical significance beyond the 95% confidence level. By contrast, the average recoveries for the 31 rated bonds of non-US firms that defaulted is 42%, which does not differ significantly from the sample of 565 rated US firms alone.

It is possible that differences in the average seniority of the bonds issued by firms in different Сектора, or of the bonds issued by firms domiciled in countries outside the US could be distorting the results. If bank obligations tended to be less senior than non-financial obligations, that could explain some of the differences in recoveries that we see. Conversely, if US firm obligations tended to be more junior than those of non-US firms, that could be masking differences in recoveries that are not apparent in the aggregate sample. To partially control for sectoral differences in the level of seniority, we recalculate recovery rates by sector for subordinated bonds only, reported in Table 7. The sample size decreases from 596 to 358 issuers. The difference in average recovery between US banks and non-financial companies remains large (19.9% vs. 35.5%), and at high levels of statistical significance (tstatistic equals 3.1).94 The difference in recoveries between US and non-US firms has increased somewhat (from 3.1% to 6.3%), but remains statistically insignificant. Only five of the thirty-one non-US defaulted bond issuers with available data on recoveries had subordinated bonds outstanding.

In conclusion, it appears that the differences in default rates between US banks and US nonfinancials were not counterbalanced by the differences in recoveries on those defaults. If anything, the recoveries tended to be much lower for US banks than those for US nonfinancial firms. However, keeping in mind that neither result is statistically significant, the slightly lower default rates for US relative to non-US firms may have been accompanied by somewhat lower average recovery rates.

^ Downgrade rates

Downgrades reflect an increased likelihood of default of an obligation in future periods. Thus, if two bonds are subject to equal near-term default risk, the instrument with greater downgrade risk would likely have more longer-term default risk. Similarly, it may be appropriate for two bonds with differing short-term default prospects to carry the same credit rating if the bond that is less likely to default in the near term has, at the same time, more vulnerability to a gradual deterioration in credit quality – i.e. greater downgrade risk.

94 One caveat with respect to this comparison is that subordinated bank bonds may not be of equivalent seniority to subordinated industrial bonds, given that deposit liabilities are senior to all bank debentures in the United States.


While default rates over a long horizon, such as 10 years, also measure long-term credit risk, analysis using such measures must impose a cut-off year for initial ratings in the relatively distant past. Thus, an important complementary question in assessing the consistency of ratings across Сектора may be whether the likelihood of a default or downgrade, controlling for initial rating level, is consistent across Сектора – or at least whether any differences are offsetting.

Table 8 shows downgrade rates for sovereign and other issues by the beginning-of-period Moody’s and S&P’s credit ratings for the period 1981-1998. The shorter period is utilised since the S&P data are available from that year. For the purposes of our analysis, an issuer is considered downgraded if its rating on unsecured senior debt moves from one letter-grade category to a lower letter-grade category, or if it defaulted. (Thus, downgrades within letter grades - e.g. from A1 to A3 - are not counted.) Again, one-half of the withdrawn issuers are removed from the denominator, and rates at both one-year and five-year horizons are reported.

Table 8 indicates that sovereigns have shown strikingly lower downgrade rates at both the one-year and five-year horizons at the highest rating levels of Aaa (AAA), Aa (AA), and A than other entities rated by Moody’s and S&P. For example, 38% of all firms rated Aa by Moody’s were downgraded over a five-year horizon versus 5% of all sovereigns. At least in terms of the direction of the bias, these are consistent with the lower risk weights placed on sovereign credits in the proposed standardised approach. However, in the categories of Baa, where sovereign credits also have lower risk weights relative to corporates, the downgrade rates are higher for sovereigns than corporates.

Downgrade rates for sovereigns in the non-investment grade ranges [Ba (BB), B] appear similar to other firms at the one-year time horizon, but lower at the five-year horizon. Since our calculation of downgrade rates at a five-year horizon utilises annual cohorts based on initial ratings only through 1994, there were very few sovereign observations at the Ba and B grade levels, and the lower downgrade rates should not be taken too seriously.

Table 9 again reports downgrade rates, but this time comparing US and non-US firms. Here the results are mixed, and Moody’s and S&P ratings provide contrasts. Among the differences in Moody’s ratings between US and non-US firms, the differences in one-year downgrade rates are noticeable only at Baa (6.3% for US vs. 8.4% for non-US) and B (9.7% for US vs.

6.8% for non-US) and Caa-C (18.9% for US vs. 15.2% for non-US). Given a Moody’s low rating, non-US firms are less likely to be downgraded or default within a year. However, given a low S&P rating, non-US firms are more likely to be downgraded.

At a five-year horizon, Moody’s and S&P downgrade rates are more closely in line with each other. Moody’s data indicate that non-US firms were more likely to be downgraded from Aaa than US firms, but that US firms at every subsequent rating category were more likely to be downgraded than non-US firms. The differential in downgrade rates were especially large in the lower rating levels: 30.5% vs. 18.8% for Ba’s, 35.3% vs. 28.9% for B’s, and 47.1% vs.

15.4% for Caa-Cs. S&P results also show higher downgrade rates for US firms, though in a somewhat more limited rating area - A, BBB, and BB, and B. However, the difference in downgrade rates at S&P’s BB (26.6% for US firms vs. 9,4 % for non-US firms) and B (30.9% vs. 9.6%) rating levels are particularly large. At the same rating level, US firms have faced greater downgrade risk than non-US firms.


Table 10 reports the downgrade rates for US banks versus US non-financials. With Moody’s

data, for higher-grade credits rated Aaa and Aa, US banks have been far more likely to be

downgraded than US non-financials, both at one and five-year terms. While flattening for the medium rating levels (A, Baa), the difference again is apparent at both one and five yearterms for non-investment grade ratings - for example, Ba, B and Caa-C.

The results using S&P data, though somewhat weaker, are basically consistent with those using Moody’s. While AAA/AA rating levels are associated with more US bank downgrades than US non-financials, non-financials tend to have higher downgrade rates for the A/BBB middle region. Banks have significantly lower downgrade rates in the B rated area.

As with the default rate analysis, we test for the independent influence of sectoral distinctions on the likelihood of a downgrade by estimating multivariate probit models, and using the estimated coefficients to compute implied sectoral differences (Table 11). The dependent variable is the probability of a letter-grade demotion or default. The explanatory variables are the same as for the default probits presented in Table 4, except that a sovereign issuer indicator dummy, as well as dummy variables for Aa and A rating levels, can now be included. (Full details of the estimated model coefficients from which the reported statistics are derived appear in Appendix Table A2.) S&P’s somewhat lower downgrade rates for sovereigns relative to corporate issuers (documented in Table 8) turn out to be both robust to calendar dummies and statistically significant. The mixed results of the US versus non-US comparisons (from Table 9) now result in significantly higher downgrade rates for non-US firms, once the relative abundance of non-US ratings in more recent years (which saw a lower-than-average overall rate of downgrades) is taken into account.95 Finally, consistent with the figures in Table 10, US banks have been significantly more prone to downgrades than US non-financial firms.

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