Are Conservative Ratings More Accurate? An Evaluation and Comparison of RMBS Ratings

Are Conservative Ratings More Accurate? 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An Evaluation and Comparison of RMBS Ratings Gina Nicolosi, Lei Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8595537/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract We compare the levels and accuracies of residential mortgage-backed securities (RMBS) ratings from S&P, Moody’s, Fitch, and DBRS and find the following. While DBRS ratings are the most conservative, they have the lowest discriminatory power of default risk in short-, intermediate-, and long-term horizons. In addition, DBRS ratings have little or no incremental predictive power for default beyond that of other CRAs on joint-rated RMBS. In contrast, S&P ratings are the most generous yet have the highest discriminatory power of default risk. The superior performance of S&P ratings can be attributed to a balanced trade-off between early warnings of default and false alarms. The findings highlight the differences between conservative ratings and accurate ratings. Figures Figure 1 Figure 2 Introduction Critics, ranging from journalists and politicians to academics, blamed the US housing bubble and the subsequent financial crisis from 2008 to 2009 partly on severely inflated and grossly inaccurate credit ratings of residential mortgage-backed securities (RMBS) and other structured finance products (see, for example, Benmelech and Dlugosz, 2010; Lucchetti and Ng, 2007; United States Financial Crisis Inquiry Commission, 2011; and White, 2009). They argued that conflicts of interest in the issuer-pay model, lack of competition in the rating industry, issuers’ rating shopping behavior, investors’ capital arbitrage considerations, and the complexity of deal structures led to significantly inflated and inaccurate credit ratings of RMBS, particularly those backed by subprime mortgage loans. The overly optimistic ratings, in turn, resulted in capital misallocation and fueled the US housing bubble in the mid-2000s. The massive rating downgrades of large amounts of RMBS during the financial crisis lent support to this view. In response to the financial crisis, the US Congress passed the Dodd-Frank Act in 2010. One major goal of the Act is to improve the accuracy and quality of credit ratings by reducing conflicts of interest, improving transparency, promoting competition, and increasing oversight of the US credit rating industry (Lu et al., 2023). In a study of the impact of the Act on credit ratings, Dimitrov et al. (2015) find a downward trend in US corporate bond ratings since its passage. The authors argue that CRAs became more protective of their reputational capital after the Act and tightened their rating standards to minimize legal/regulatory penalties due to default detection failures. Other studies find a similar increase in rating conservatism (i.e., lower ratings on bond issues with similar observable characteristics), suggesting that rating standards have tightened over time (Blume et al., 1998; Alp, 2013; Baghai et al., 2014). [1] Although credit ratings on structured finance products like RMBS were at the eye of the financial storm of 2008-2009, most post-crisis empirical studies on rating standards and accuracy focus on corporate bond ratings. Using regulatory disclosure data from 2013 to 2022, we examine RMBS ratings from three legacy CRAs (S&P, Moody’s, Fitch) and DBRS instead for two reasons. First, prior studies suggest that structured finance products are more prone to rating inflation due to asset complexity and opacity (Skreta and Veldkamp, 2009; Opp et al., 2013). Furthermore, the market for RMBS credit rating has become more competitive since the financial crisis (Livingston et al., 2021). Theoretical and empirical studies indicate that increased competition has a significant impact on rating inflation/accuracy (Becker and Milbourn, 2011; Skreta and Veldkamp, 2009; Flynn and Ghent, 2017). We find that DBRS ratings on RMBS are the most conservative, both before and after the financial crisis. On the other hand, S&P ratings are the most generous among the four CRAs. Interestingly, the conservatism of DBRS ratings did not result in a loss of market share for the small CRA. Rather, as reported by Livingston et al. (2021), DBRS increased its market share of RMBS ratings from about 1% in 2006 to almost 15% by 2019. Given the severity of the financial crisis, it is not surprising that there was a demand for more conservative ratings. While there are both pushes from regulators and pulls from investors for conservative ratings, they are not unequivocally more desirable. Providing investors with early warnings of potential defaults and protecting CRAs from reputational loss and legal/regulatory penalties, conservative ratings likely also lead to high rates of false alarms and punish some low-risk bond issuers with unjustifiably pessimistic ratings and consequently higher financing costs. For example, Baghai et al. (2014) find evidence that the firms most affected by rating conservatism issue less debt and, consequently, experience lower growth. Furthermore, excessively pessimistic ratings also deter risk-averse investors from investing in otherwise creditworthy bonds. More importantly, it is not clear whether conservative ratings are more accurate, and the empirical evidence is mixed. On the one hand, Dimitrov et al. (2015) show that credit ratings issued after the Dodd-Frank Act, while more conservative, are less informative and more likely to trigger false alarms. On the other hand, a recent study by Afik and Galil (2025) finds that US corporate bond ratings have improved their accuracy while becoming more conservative over the last several decades. Using a simple numerical example, we demonstrate that there are two different possible causes of rating conservatism: tighter rating standards and more accurate default risk estimation. In the former case, CRAs make no improvement to their risk assessment frameworks but instead apply more stringent standards, resulting in lower, but not necessarily more accurate, average ratings. In the latter case, CRAs improve their risk estimation techniques and consequently more accurately assess default risk, which can result in lower average ratings in some cases (Afik and Galil, 2025). While both stringent rating standards and improved rating methodology can lead to rating conservatism, the testable empirical implications differ. Stringent rating standards without more precise default risk estimation do not lead to an overall improvement in rating accuracy; the resulting increased default detection comes at the expense of a higher false alarm risk. Conversely, rating conservatism due to better rating methodology and information leads to an overall improvement in rating accuracy and thus an optimal balance between type I and type II errors. To determine whether stringent standards or more accurate risk estimation drives the conservatism of DBRS ratings, we empirically compare the accuracy and false alarm/default detection tradeoffs of RMBS ratings issued by DBRS and the three legacy CRAs. First, we find that DBRS ratings have the lowest predictive power of default risk among the four CRAs. In contrast, while S&P has the most generous ratings, its ratings are the most accurate for one-, three-, and ten-year horizons. Second, the most conservative DBRS ratings result in a significantly higher risk of false alarms. For example, the ten-year false alarm rate of the lowest rating category is almost 70% for DBRS, much higher than that of S&P (5%). Finally, DBRS ratings have no or weak marginal predictive power for defaults beyond that of the three legacy CRAs. On the other hand, ratings from the three legacy CRAs have strong additional predictive power for defaults, particularly for those in the lowest rating category. The findings strongly suggest that DBRS’s rating conservatism is driven by overly stringent rating standards rather than better default risk estimation. The severity of the financial crisis unsurprisingly led to widespread criticism of loose rating standards and demands for more conservative ratings. Contrary to conventional wisdom, this study’s findings highlight the distinction between conservative ratings and accurate ratings. While an informative and accurate rating system must possess higher discriminatory powers of default risk, it is not necessarily ‘tougher.’ Consequently, over-emphasizing rating standard rigor might be counterproductive; indeed, Dimitrov et al. (2015) argued that ‘increasing the legal and regulatory costs of CRAs might have an adverse effect on the quality of credit ratings.’ The study also contributes to the debate on the relationship between industry competition and rating inflation/quality. On the one hand, several studies argue and find evidence that increased competition among CRAs leads to rating inflation and/or lower rating quality due to intensive rating shopping by bond issuers and cratering behavior by CRAs (Becker and Milbourn, 2011; Bolton et al., 2012; Flynn and Ghent, 2018). On the other hand, Xia (2014) find that the entry of a new CRA increases rating information content and improves rating quality. Consistent with the mixed empirical evidence, our findings suggest that increased competition does not necessarily lead to rating inflation or improved rating accuracy. Over the last two decades, the US Securities and Exchange Commission (SEC) has adopted a series of rules to implement the provisions of the Credit Rating Agency Reform Act and the Dodd-Frank Act. A major goal of these rules is to increase transparency and facilitate rating performance evaluation and comparison (Lu et al., 2023). For example, CRAs registered as a Nationally Recognized Statistical Rating Organization (NRSRO) are required to file an annual disclosure NRSRO Form. An important part of the NRSRO Form is the credit rating transition and default matrices. In addition, SEC Rule 17g-7 requires registered CRAs to publicly disclose their rating actions at the individual issuer/issue level. Taking advantage of the regulatory disclosure data, this study presents the first comprehensive and independent evaluation and comparison of RMBS ratings from four different CRAs over short, intermediate, and long horizons. This study is closely related to Dimitrov et al. (2015) and Afik and Galil (2025) but differs in several important aspects. First, different from those studies on corporate bond ratings, we examine ratings on RMBS, a more complex and opaque asset class that is harder to rate and more prone to rating inflation (Skreta and Veldkamp, 2009, Opp et al., 2013 ). The information opacity and issue complexity suggest that RMBS rating conservatism variations across CRAs might reflect differences in rating methodologies, which in turn affect rating accuracy. Second, earlier studies investigated changes in rating conservatism and accuracy over time, while we analyze cross-sectional differences between CRAs at a point in time. A major challenge in time-series studies is the potential for contemporaneous confounding factors. Indeed, whether rating conservatism has increased over time has been disputed by Krystyniak and Staneva (2024), who suggest that the average rating declines over time might result from increases in default risk. Additionally, when bond characteristics vary over time, portfolio-dependent measures of rating accuracy become unreliable for intertemporal comparisons. For example, Hamerle et al. (2003) demonstrate that the two most commonly used metrics, the Accuracy Ratio and AUC statistic, are influenced not only by the accuracy of CRAs’ default probability estimations but also by the composition of the rated bond portfolios. As a result, these measures are ill-suited for comparing rating accuracy across time. Instead, we compare ratings on RMBS that are jointly rated by pairs of CRAs at one point in time. This study is also related to Doherty et al. (2012) who examine S&P’s entry into the municipal bond rating market. Consistent with Doherty et al. (2012), we observe that new market entrants apply more stringent rating standards, alleviating the concern that increased competition may lead to the ‘race to the bottom’ problem. Different from Doherty et al. (2012), however, we find the new entrant into the RMBS market does not provide more informative ratings. The remainder of the paper is organized as follows. Section I illustrates two possible causes of rating conservatism through a numerical example. Section II presents the sample data and descriptive statistics. Section III compares RMBS rating levels between the four CRAs, and Section IV analyzes and compares RMBS rating accuracy. Section V concludes. [1] One study disputes the claim of increased rating conservatism (Krystyniak and Staneva, 2024). I. Rating Conservatism: Stringency vs. Accuracy Using a numerical example, we illustrate two different potential reasons for rating conservatism - more stringent rating standards versus more accurate default risk estimation - and demonstrate their distinctive testable empirical implications. Suppose there are three hundred bond issues that can be characterized in two dimensions: default risk and information transparency. Half of the bonds have a default probability of 25%, and the other half have a default probability of 5%. Two-thirds of the bonds are informationally transparent (i.e., the CRA can readily detect default probability), and the others are opaque. The two characteristics are independent of each other; that is, half of the transparent (opaque) bonds have a higher default risk, and the other half a lower default risk. The CRA cannot distinguish between low-risk and riskier opaque bond issues but can estimate their average default probability (15%). The CRA adopts a binary rating system based on its estimated bond default risk, 1 (0) indicating Non-default (Default) when the estimated default probability is less than (exceeds) a cutoff point. If the estimated default probability equals the cutoff point, the CRA randomly assigns a Default/Non-default rating. The cutoff point is subjective and can be interpreted as the CRA’s rating standard: a higher (lower) cutoff indicates looser (stricter) rating standards. A. Rating Standards and Tradeoff between Default Detection and False Alarms To illustrate the effects of rating standards, consider the following three cases. Under the Generous rating standard, the CRA sets the default probability cutoff at 20%, assigning a Non-default rating to low-risk transparent bonds and all opaque bonds, and a Default rating to high-risk transparent bonds. Under the Moderate rating standard, the CRA sets the default probability cutoff at 15%, assigning a Non-default rating to low-risk transparent bonds and half of the opaque bonds, and a Default rating to the remaining opaque bonds and high-risk transparent bonds. Under the Stringent rating standard, the CRA sets the default probability cutoff at 12%, assigning a Non-default rating only to low-risk transparent bonds and a Default rating to all opaque bonds and high-risk transparent bonds. The first three columns of Table 1 summarize the average ratings and default rates under the three different rating standards. The Stringent rating standard obviously leads to lower average ratings, or rating conservatism. In addition, the default rates of Non-default ratings decrease with the rating stringency, showing that tighter rating standards can reduce default detection failures. However, the default rates of Default rating also decrease with rating stringency, reflecting more false alarms (i.e., non-default bonds receiving a Default rating). Rating standard stringency entails a tradeoff between predicting actual defaults and avoiding false alarm rates. This tradeoff is commonly evaluated by two metrics: the Hit Rate (HR) and the False Alarm Rate (FAR). 2 The HR, defined as the percentage of defaulted bonds that were initially assigned a Default rating, measures the ratings’ ability to correctly identify true defaults. The FAR is defined as the percentage of surviving bonds that were incorrectly assigned a Default rating, reflecting the system’s tendency to raise false alarms. An accurate and informative rating system maximizes HR while minimizing FAR. The fourth and fifth rows of Table 1 report the HR and FAR values under different rating standards. As expected, tighter standards raise the HR but also increase the FAR. Because none of the three standards achieves both a higher HR and a lower FAR, no single approach dominates. B. Rating Conservatism and Improved Default Risk Estimation Instead of adjusting rating standards (i.e., the cutoff point), the CRA may improve its rating technology to enhance the rating information content and resolve the opacity problem. In this case, the CRA accurately estimates the individual default probability of the opaque bond issues and assigns a Default (Non-default) rating to all high-risk (low-risk) bonds. Under this Refined rating system, the average rating is 0.5, and the last column of Table 1 reports its default rates, HR, and FAR. Although the Refined ratings may appear more conservative when compared to the Generous rating standard, this arises from more accurate default probability estimation and not from rating stringency. This distinction highlights a common misconception: that lower average ratings or rating conservatism necessarily indicate more stringent standards. As first noted by Afik and Galil ( 2025 ), rating conservatism may also result from improved estimation accuracy. Furthermore, higher average ratings, often perceived as “loose” or “inflated”, are not necessarily less accurate. Although the Refined rating system yields a higher average rating than the Stringent standard, we later demonstrate that it offers superior predictive power for default risk. While the average ratings under the Refined and Moderate Ratings are identical, the Refined ratings clearly outperform the Moderate ratings by achieving both a higher HR and a lower FAR. When compared to the other two standards, however, the performance of the Refined ratings is more nuanced. Relative to the Generous (Stringent) standard, the Refined ratings generate a higher (lower) HR, but also a higher (lower) FAR. C. Rating Conservatism and Rating Accuracy Regardless of the underlying reason, rating conservatism inevitably leads to an increase in FAR when compared to the Generous rating standard. Therefore, it is problematic to conclude that conservative ratings are less informative or accurate solely based on higher FAR, as posited by Dimitrov et al. ( 2015 ). To examine which rating system best balances early warnings with false alarm avoidance, we use a statistic called the Area Under the Curve (AUC) to measure ratings’ discriminatory power of default risk. 3 Based on HR and FAR, the AUC statistic has the following probabilistic interpretation. Suppose two bonds are randomly drawn: one from the distribution of surviving bonds and the other from the distribution of default bonds. Given the ratings of the two bonds, an otherwise uninformed investor naturally guesses that the bond with a better rating is a survivor and the lower-rated one is drawn from the pool of default bonds. In the case of two bonds with the same rating, the investor makes a random guess. Bamber ( 1975 ) and Engelmann et al. ( 2003a ) show that the probability of a correct guess based solely on ratings exactly equals the AUC statistic. Thus, the AUC statistic ranges from 0.5 for a non-informative rating system with no discriminatory power of default risk to 1 for a perfect rating system. Qin and Zhou ( 2025 ) provide a detailed description of the AUC statistic, its statistical properties, and estimation techniques for a multi-level rating system. Adapting the estimation technique to a binary rating system, we can easily show that AUC = 0.5 + 0.5*(HR – FAR). The last row of Table 1 reports the AUC statistics for the four rating systems. The Refined rating system has the highest AUC statistic, demonstrating its higher discriminatory power of default risk. Interestingly, the AUC statistics do not vary with rating standard cutoff points. This is not surprising because tightening rating standards does not make the rating more informative. More stringent rating standards can effectively increase the HR, but the corresponding increase in FAR results in no net improvement of rating accuracy or discriminatory power of default risk. Thus, tightening rating standards does not improve overall rating accuracy. II. Data Collection and Sample Description We use two regulatory disclosure data sources. The first is the 2018 and 2020 NRSRO Forms, retrieved from the SEC’s Edgar platform. Exhibit 1 of the NRSRO Form contains the one-, three-, and ten-year single-cohort rating transition and default matrices. The transition matrix reports the aggregate number and distribution of outstanding ratings at year-end, as well as percentages of issuers/issues that moved from one rating category to another within the specified horizon. The second data source is the granular SEC Rule 17g-7 data. SEC Rule 17g-7 requires registered CRAs to publicly disclose, with a one-year lag, rating actions taken on or after June 15, 2012. Hence, the January 2024 Rule 17g-7 data update downloaded from each CRA website includes a decade (i.e., mid-2012 or earlier through the end of 2022) of the four CRAs’ rating actions. The Rule also requires rated debt instruments to be identified by 9-digit CUSIP numbers whenever available, facilitating the matching of RMBS rated by different CRAs. RMBS not identified by CUSIP numbers are eliminated, as are preliminary or provisional ratings, commercial paper and national/regional-specific ratings, other specialized ratings, and any RMBS identified as in default by at least one CRA before 2013. Our main sample consists of 44,451 RMBS with outstanding ratings from at least one CRA on January 1, 2013. The first column of Table 2 reports the distributions of RMBS rated by CRAs, with S&P rating almost three-quarters of the RMBS in the sample, Moody’s and Fitch each rating about 40%, and DBRS rating less than 10%. The second and third columns report the number of RMBS rated solely by one CRA (about 44% of the sample) and the number of RMBS rated jointly by two or more CRAs, respectively. Columns 4 to 7 present the numbers of RMBS jointly rated by pairs of CRAs. There are significant overlaps among the three legacy CRAs. In addition, a substantial portion of RMBS rated by DBRS are also rated by S&P. On the other hand, only 310 RMBS are jointly rated by Moody’s and DBRS. An important aspect of the study is the analysis of credit ratings’ predictive power for defaults. S&P, Fitch, and DBRS assign a D rating when a bond defaults. On the other hand, instead of a D rating, Moody’s C rating indicates RMBS ‘typically in default with little prospect for recovery of principal and interest.’ Therefore, defaults are identified based on rating withdrawals due to default, C ratings from Moody’s, or D ratings from S&P, Fitch, or DBRS. If default rating assignment dates differ across CRAs, we use the earliest date. From 2013 to 2022, more than a quarter of the sample RMBS (i.e., 12,031) defaulted. Panel A of Figure I reports the annual number of defaults. The default frequency decreased over the sample period, with more than a third occurring in 2013. Panel B shows the number of defaulted RMBS rated by each CRA at the beginning of the sample period. Almost 80% of defaulted RMBS were rated by S&P, consistent with its larger market share. III. Rating Conservatism To investigate rating conservatism across the four CRAs, we first examine the average RMBS rating levels shortly before and after the 2008–2009 financial crisis. Using the 2018 and 2020 NRSRO Forms’10-year rating transition matrices, we calculate the average RMBS ratings at the end of 2007 and 2009. The alphanumeric rating symbols are converted to a numerical rating variable as follows: RATING = 1 for AAA (Aaa), 2 for AA (Aa), up to 8 for CC/C rated RMBS. 4 We group CC and C ratings together since not all CRAs assign both ratings: S&P (DBRS) does not assign C (CC) ratings. 5 Panel A of Table 3 reports average outstanding ratings and the number of RMBS ratings by CRAs. At the end of 2007, the average ratings from the three legacy CRAs cluster around 2.2, approximately 0.4 letters higher than the average DBRS ratings of 2.62. While all four CRAs massively downgraded their RMBS ratings during the financial crisis by at least two letter grades by the end of 2009, DBRS-rated RMBS experienced larger downgrades than S&P-rated RMBS: an average downgrade of 2.65 letter grades by DBRS vs. 2.14 by S&P. This suggests that DBRS increased its rating conservatism relative to S&P during the financial crisis. Panel B reports the proportion of sample RMBS assigned the top (AAA or AA, RATING = 1 or 2) and bottom ratings (CC/C, RATING = 8) by each of the four CRAs. Before the financial crisis, more than two-thirds of RMBS rated by the three legacy CRAs received a AAA or AA rating. In contrast, only 55% of RMBS rated by DBRS fell into the top rating categories, indicating a more conservative approach by the smaller CRA during the pre-crisis period. Following the crisis, the proportion of RMBS receiving the lowest CC/C rating rose substantially across all CRAs (e.g., Moody’s share of ratings in this category increased from less than 2% to 20%). Notably, DBRS-rated RMBS have the highest proportion of CC/C ratings (46%). In contrast, the percentage of RMBS rated by S&P with the lowest rating is significantly lower at less than 13%. These findings suggest that DBRS ratings were more conservative both before and after the financial crisis. Rather than penalizing DBRS’s tougher stance, market share shifts suggest an increased preference towards rating conservatism in the wake of the crisis. Consistent with Livingston et al. ( 2021 ), the number of RMBS rated by DBRS increased from 4,821 to 5,600 in the two years, contrasting sharply with the decline of 20% or more experienced by S&P and Moody’s. However, differences in average ratings may reflect variation in the composition of RMBS rated by each CRA. If legacy CRAs avoided riskier deals, such as those backed by subprime loans, then DBRS would appear more conservative simply because it rated disproportionately risky RMBS. To address this possibility, we use SEC Rule 17g-7 data to perform pairwise ratings comparisons of RMBS jointly rated by two CRAs at the beginning of 2013 and present the results in Table 4 . The CRAs in the unique pair are listed in the first two columns, and the number of RMBS jointly rated by the pair is provided in the third column. Columns 4 and 5 report the CRAs’ mean RATING s, and column 6 reports the difference in the mean RATING s. The last five columns report the distribution of rating disagreement: the percentages of RMBS with the two ratings differing by multiple letter grades, one letter grade, or the same. Results in Table 4 show that, when evaluating the same RMBS, DBRS, on average, assigns lower ratings than the legacy CRAs. This is most pronounced relative to S&P and Moody’s. Among the 1,393 RMBS jointly rated by S&P and DBRS, 44.08% (28.36%) received an S&P rating one (multiple) letter grade(s) higher than DBRS. In contrast, fewer than 3% were rated higher by DBRS than by S&P. These comparisons, based on identical securities at the same point in time, underscore DBRS’s more conservative approach. Conversely, S&P appears to be the least conservative of the four CRAs, with its ratings, on average, one letter grade better than its competitors. IV. Rating Accuracy Since the 2008–2009 financial crisis, investors and financial regulators have become increasingly concerned about rating inflation and loose rating standards. However, as illustrated in Section I, conservative ratings are not equivalent to accurate ratings. On the one hand, improved default risk estimation may result in both more conservative and accurate ratings. On the other hand, rating conservatism driven by more stringent standards alone does not improve rating accuracy. To determine whether stringent standards or more accurate risk estimation drives the conservatism of DBRS ratings, we empirically compare the accuracy and false alarm/default detection tradeoffs of RMBS ratings issued by DBRS and the three legacy CRAs. A. AUC Statistic Comparisons Following Qin and Zhou ( 2025 ), we first use the AUC statistic to quantify each CRA’s ability to discriminate between defaulting and surviving RMBS. As noted earlier, AUC statistics are determined by both the accuracy of CRAs’ default probability estimations and the composition of the rated bond portfolios. To remove the effects of different bond portfolios, we focus on RMBS jointly rated by two CRAs. Table 5 reports the one-, three-, and ten-year AUC statistics of RMBS jointly rated by pairs of legacy CRAs, and Table 6 extends the analysis to RMBS jointly rated by DBRS and the legacy CRAs. First, note that the four CRAs all have AUC statistics greater than 0.5, indicating that their ratings have varying degrees of discriminatory power for default risk. Second, for all three performance evaluation windows, S&P ratings consistently achieve the highest AUC statistics, ranging from 0.81 to 0.92. The differences in AUC statistics between S&P and the other three CRAs are statistically significant at the 1% level. At the other spectrum, the AUC statistics of DBRS, 0.65 or less, are significantly lower than those of the three legacy CRAs. Third, the differences in AUC statistics between Moody’s and Fitch, while statistically significant, are relatively modest. The empirical evidence strongly indicates that DBRS ratings, the most conservative, have the lowest overall accuracy, whereas S&P ratings, the least conservative, have the highest. B. Tradeoff between Default Detection and False Alarm Investors rely on ratings to assess creditworthiness, expecting lower ratings to signal higher default risk. When a low rating precedes a default, it serves as a useful early warning. However, if no default follows, the low rating becomes a false alarm. An informative rating system should provide credible early warnings and minimize false alarm risk. To evaluate this tradeoff, we adapt HR and FAR for multi-level rating systems. For rating classification X, HR is the proportion of bonds that ultimately default that were rated X or below (worse). A high HR indicates that the rating category captures a high percentage of default bonds, resulting in credible early warnings of future defaults. FAR is the proportion of surviving bonds that were rated X or below (worse). A high FAR means that the rating category predicts a high percentage of non-default bonds to default, resulting in a high risk of false alarms. As discussed in Section I, CRAs with better estimations of bond default risks have higher probabilities of assigning better ratings to creditworthy bonds and lower ratings to riskier bonds, leading to a higher HR and a lower FAR than their counterparts with less precise default risk estimations. Rating standards also affect the tradeoffs, but in a different way. CRAs with stringent rating standards are more likely to assign lower ratings than their competitors with more generous rating standards. As a result, CRAs with stringent rating standards are more likely to have both a higher HR and a higher FAR. The opposition is true for CRAs with generous rating standards. Table 7 presents the ten-year HR and FAR statistics of each rating category for RMBS jointly rated by pairs of legacy CRAs, while Table 8 compares the two ratios between DBRS and each of the three legacy CRAs. Several patterns emerge. 6 First, no CRA has both a higher HR and a lower FAR consistently in every rating category, suggesting that no single CRA clearly dominates its counterparts. Second, S&P generally produces lower HR and FAR statistics than the other CRAs, especially compared to DBRS at the CC/C level. S&P’s HR at CC/C is 0.471 versus DBRS’s 0.977, while S&P’s FAR of 0.053 is much lower than DBRS’s FAR of 0.691. The findings highlight the expected tradeoff: S&P’s more generous ratings reduce false alarms but yield fewer early warnings, whereas DBRS’s conservative ratings provide more early warnings but also false positives. Thus, the higher overall accuracy of S&P ratings is likely due to a more optimal balance between early warnings and false alarms. C. Marginal Predictive Power of Default While earlier results suggest that conservative DBRS ratings have lower overall accuracy and higher false alarm rates, they may still add value as a corrective check against legacy CRAs’ overly optimistic ratings. To investigate whether DBRS ratings offer marginal power in predicting default, we construct probit models of ten-year outcomes (i.e., default versus non-default) of RMBS jointly rated by DBRS and the legacy CRAs. The explanatory variables are numerical ratings (= 1 for AAA/Aaa, 2 for AA/Aa, up to 8 for CC/C rated RMBS) assigned by DBRS and the corresponding legacy CRA in 2013. If each rating conveys unique information concerning bond default risk, both rating coefficients should be statistically significant. Conversely, if the DBRS rating does not provide additional information beyond that of the legacy CRA’s assessment, its coefficient should be insignificant (and vice versa). 7 In each of Table 9 ’s probit regression models, the coefficients on the legacy CRA ratings are significantly positive, indicating that lower ratings from these CRAs, holding the DBRS rating constant, are associated with higher RMBS default probability. In contrast, the DBRS rating coefficients are either negative or insignificant, suggesting little to no marginal predictive power beyond other agencies’ assessments. Overall, empirical evidence suggests that DBRS ratings provide little incremental information regarding default risk beyond that of the legacy CRAs, while the three legacy CRA ratings provide much stronger marginal predictive power even when DBRS ratings are included. Finally, the legacy CRAs' marginal predictive power is particularly evident amongst the riskiest assets. Based on ten-year outcomes, each RMBS rated CC/C by DBRS at the beginning of 2013 is classified into five categories: Defaulted if any CRA assigned the RMBS a default rating before the end of 2022; Paid-off if any CRA withdrew its rating due to pay-off; Remain CC/C Rated if the DBRS rating outstanding at the end of 2022 is CC/C; Upgraded to Other Junk Ratings if the DBRS rating outstanding at the end of 2022 is CCC, B, or BB; and Upgraded to Investment Grade if the DBRS rating outstanding at the end of 2022 is BBB or higher. 8 Panel A of Table 10 shows that nearly 18% of DBRS’s lowest-rated RMBS were paid off within 10 years, and another 6% were upgraded to investment-grade ratings, implying that roughly one quarter of the lowest-rated RMBS were actually creditworthy. Panels B to D break down the outcome of those RMBS that were also rated by S&P, Moody’s, and Fitch, respectively. As shown by the first columns of the three panels, a similar percentage (23% to 39%) of those bonds were either paid off or upgraded to investment grade. The remaining columns demonstrate the marginal predictive power of legacy CRA ratings. The panels’ rightmost columns report outcomes by the legacy CRA’s 2013 rating (i.e., CC/C, CCC, B, and BB or higher). When a legacy CRA was more optimistic and assigned a rating of B or higher, the Defaulted and Remained CC/C percentages are much lower, and the Paid-off and Upgraded to Investment Grade percentages are much higher. Conversely, when the legacy CRA agreed with DBRS’s pessimistic outlook, the Defaulted rates are substantially higher. These findings confirm the strong marginal predictive power of legacy CRA ratings beyond DBRS ratings. The findings further indicate that DBRS’s overly stringent rating standards misclassified many creditworthy RMBS, resulting in unnecessary and undesirable false alarms. D. Robustness Check The above rating accuracy analyses focus on RMBS jointly rated by pairs of CRAs. However, joint-rated RMBS account for only 37% (65%) of issues rated by DBRS (S&P). This limited coverage raises two concerns: the validity of the empirical results based on a portion of the sample and potential selection bias. To address these issues, we investigate the rating conservatism and accuracy of RMBS solely rated by S&P or DBRS relative to those with additional ratings. 9 First, we compute and compare the average ratings and rating distributions of single- and joint-rated RMBS. The average rating of RMBS solely rated by S&P (5.16) is comparable to that of jointly rated by S&P and at least one other CRA (5.00). However, the average rating of RMBS solely rated by DBRS (3.04) significantly differs from that of RMBS jointly rated by DBRS and at least one other CRA (7.03). Figure II further reveals the differences in the underlying rating distributions, with Panel A (B) depicting the S&P (DBRS) rating distributions for both single- and joint-rated RMBS. The distributions of S&P ratings are similar between single- and joint-rated issues, suggesting that there is no significant credit quality difference between issues rated only by S&P and those jointly rated by other CRAs. In contrast, ratings for RMBS solely and jointly rated by DBRS display substantial distribution differences (Panel B). RMBS solely rated by DBRS generally show an overall higher credit quality (three quarters are rated A or above) than those jointly rated by DBRS and other CRAs (three quarters received the lowest CC/C rating from DBRS). However, a sizeable portion of DBRS single-rated issues (17%) is rated CC/C. These distribution differences suggest that RMBS with extremely low DBRS ratings are likely to acquire an additional rating (i.e., 73% of RMBS with a CC/C rating from DBRS are also rated by other CRAs), while those rated A or higher by DBRS tend to forego a second opinion (i.e., only 7% of RMBS with an A or higher rating from DBRS have an additional rating). Given the selection bias in DBRS ratings, it is important to examine the rating conservatism and accuracy of single-rated RMBS relative to joint-rated RMBS. However, as noted earlier, standard measures like AUC statistics are not appropriate when comparing ratings across different bond portfolios. Furthermore, the underlying default risk is not observable. To overcome these methodological challenges, we instead investigate the default rates of single- and joint-rated RMBS. As illustrated in Table 1 , a more informative rating system should assign higher (lower) ratings to more (less) creditworthy bonds; therefore, high (low) rating categories should have lower (higher) default rates than those of a less informative rating system. In contrast, a more stringent rating system tends to be cautious, assigning high ratings only to the most creditworthy RMBS and erring on the side of false alarms by giving low ratings to relatively safe RMBS; consequently, default rates are expected to be lower across both high and low rating categories than those of a less stringent rating system. Table 11 reports the ten-year default rates of RMBS rated by S&P and DBRS. 10 The first (second) column shows the default rates by letter rating category of RMBS rated solely by S&P (jointly rated by S&P and other CRAs). Similarly, columns three and four report the default rates of DBRS-rated RMBS. The overall default rates between RMBS solely rated by S&P and those jointly rated by other CRAs are similar. Further, across the individual rating categories (except CCC rating), default rates do not substantially differ between single- and joint-rated RMBS. In aggregate, the empirical findings suggest that there is neither a systemic difference in the credit quality nor a significant variation in rating stringency/accuracy between the two groups. In contrast, the final two columns of Table 11 reveal stark differences in default rates between RMBS solely and jointly rated by DBRS, supporting the early findings of systematic differences between the two groups. The overall default rate of RMBS rated only by DBRS (0.23%) is significantly lower than that of issues jointly rated with other CRAs (30.57%). More strikingly, single-rated RMBS default rates are substantially lower than those of joint-rated issues in 5 of the 7 rating categories. Indeed, the default rate is zero for all rating categories of single-rated RMBS other than CC/C, which has a ten-year default rate of just 1.32%. In comparison, the default rate of CC/C rating for joint-rated RMBS is 38.12%. The sharp differences in default rates between the single- and joint-rated subsamples suggest that DBRS ratings on single-rated issues are even more stringent than those of joint-rated issues. Furthermore, the 1.32% default rate of RMBS rated C solely by DBRS is similar to the default rates of RMBS rated A or higher by S&P, suggesting that DBRS erroneously assigned extremely low ratings to many creditworthy single-rated issues and created a significant number of false alarms. 11 Overall, the empirical evidence indicates that DBRS ratings on both single- and joint-rated RMBS are more conservative and less accurate than S&P ratings. V. Conclusion Using regulatory disclosure data, we compare rating conservatism, relative accuracy, and early warning versus false alarm trade-off of RMBS ratings by legacy CRAs and DBRS. We find that DBRS applies the most stringent rating standards, assigning lower ratings than its peers. However, conservatism does not equate to accuracy. DBRS’s rating stringency results in significantly higher rates of false alarms and, consequently, lower overall ability to discriminate between defaulting and surviving RMBS. In contrast, S&P ratings are the most generous yet achieve the highest discriminatory power. Although inflated ratings were widely blamed for contributing to the 2008–2009 financial crisis, and DBRS’s increased post-crisis market share suggests an ensuing demand for conservatism, our results show that conservatism does not necessarily produce more accurate ratings. This study underscores the importance of distinguishing between rating toughness and informativeness. Investors and regulators may prefer more stringent rating standards when the benefit of default detection is greater than the cost of false alarms. Yet, an effective rating system must enhance discrimination between good and bad credit risk, not merely appear more cautious. Instead of focusing solely on rating standards, emphasis should be placed on achieving an optimal balance between early warning provision and false alarm avoidance to enhance rating informativeness. Declarations Author Contribution G.N. and L.Z contributed equally to the work. Data Availability We use two regulatory disclosure data sources. The first is the 2018 and 2020 NRSRO Forms, retrieved from the SEC’s Edgar platform. The second data source is the SEC Rule 17g-7 data (January 2024 updates), downloaded from the websites of the credit rating agencies. References Afik Z, Galil K (2025) Have ratings become more accurate? J Banking Finance 170:107337 Alp A (2013) Structural shifts in credit rating standards. 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Manage Sci 64(4):1672–1692 Hamerle A, Rauhmeier R, Rösch D (2003) Uses and misuses of measures for credit rating accuracy. Available SSRN 2354877 Hung M, Kraft P, Wang S, Yu G (2022) Market power and credit rating standards: Global evidence. J Account Econ 73(2–3):101474 Krystyniak K, Staneva V (2024) The myth of tightening credit rating standards in the market for corporate debt. J Banking Finance 162:107122 Livingston M, Nicolosi G, Zhou L (2021) A Bird’s-eye view of the US credit rating industry. J Fixed Income 31(2):68–99 Lu J, Nicolosi G, Zhou L (2023) Regulation of the US credit rating industry and regulatory data disclosures. Law Financial Markets Rev 17(2):71–87 Lucchetti A, Ng S (2007) How rating firms’ calls fueled subprime mess. The Wall Street Journal , August 15, 2007 Opp CC, Opp MM, Harris M (2013) Rating agencies in the face of regulation. J Financ Econ 108(1):46–61 Qin N, Zhou L (2025) Are investor-paid credit ratings superior? Financ Manage 54(1):53–87 Skreta V, Veldkamp L (2009) Ratings shopping and asset complexity: A theory of ratings inflation. J Monet Econ 56(5):678–695 United States Financial Crisis Inquiry Commission (2011) The Financial Crisis Inquiry Report: Final Report of the National Commission on the Causes of the Financial and Economic Crisis in the United States. Government Printing Office White LJ (2009) The credit-rating agencies and the subprime debacle. Crit Rev 21(2–3):389–399 Xia H (2014) Can investor-paid credit rating agencies improve the information quality of issuer-paid rating agencies? J Financ Econ 111(2):450–468 Footnotes One study disputes the claim of increased rating conservatism (Krystyniak and Staneva, 2024 ). The two ratios are often used to measure rating accuracy/quality (Cheng and Neamtiu, 2009 ; Dimitrov et al., 2015 ; Hung et al., 2022 ). Other prior studies use an alternative statistic, Accuracy Ratio or Gini Index, to measure ratings’ discriminatory power of default risk (Cheng and Neamtiu, 2009 ; Cornaggia et al., 2017 ; Livingston et al., 2021 ). Engelmann et al. ( 2003a ) show that the AUC statistic and the Accuracy Ratio are mathematically equivalent, with AUC = (1 + Accuracy Ratio)/2. We use the AUC statistic because its statistical properties and estimation techniques have been developed by previous literature (Bamber, 1975 ; DeLong et al., 1988 ; Engelmann et al., 2003b ), allowing us to test for the statistical significance of the differences in AUC statistics between two rating systems. We focus on letter grade ratings because most RMBS ratings do not have plus or minus modifiers. For example, 99% (69%) of Fitch (S&P) sample ratings do not have plus or minus modifiers. This rating category does not include Moody’s C rating, which effectively indicates default status. See the earlier discussion. We observe similar patterns from one- and three-year HR and FAR statistics, which are available upon request. The probit models include deal fixed effects. The deal information, obtained from the Bloomberg, was unavailable for three RMBS jointly rated by DBRS and legacy CRAs, resulting in slight differences in the numbers of observations used in the probit models from the joint-rated RMBS numbers reported in Table 2 . Results based on models without deal fixed effects are qualitatively similar. For RMBS whose DBRS ratings were withdrawn for non-payoff or default reasons, we use its last rating before the rating withdrawal to determine its status. Only a small percentage of RMBS are solely rated by Moody’s or Fitch. As a result, we do not analyze those issues separately. Results based on one- and three-year default rates are similar. They are available upon request. It might seem puzzling that issuers of those RMBS did not seek an additional, potentially higher, rating given DBRS’s extremely pessimistic assessment. A likely explanation relates to the nature of our sample: outstanding ratings rather than new rating initiations. These issues may have received a high DBRS rating at initial issuance, only to be unjustifiably downgraded to CC/C rating during or after the financial crisis. Since RMBS issuers have less incentive to pay for additional ratings on bonds already issued and outstanding, they likely chose not to pursue a second opinion. Tables Table 1 Rating Conservatism: Rating Standard vs. Rating Accuracy This Table presents the performance of a hypothetical binary rating system (Default Rating = 0 and Non-default Rating = 1) on a hypothetical pool of high-risk and low-risk bond issues. The details of the hypothetical example are presented in Section I. The rating performance statistics (HR, FAR, and AUC Statistics) under three different rating standards (Generous, Moderate, and Stringent standards) are compared with those of a Refined Rating system with more accurate default probability estimation. Generous Rating Moderate Rating Stringent Rating Refined Rating Average Rating 0.67 0.5 0.33 0.5 Default Rate of Non-default Rating 10% 8.33% 5% 5% Default Rate of Default Rating 25% 21.67% 20% 25% Hit Rate (HR) 0.5556 0.7222 0.8889 0.8333 False Alarm Rate (FAR) 0.2941 0.4608 0.6275 0.4412 AUC Statistic 0.6307 0.6307 0.6307 0.6961 Table 2 Distributions of Rated RMBS in 2013 This Table reports distributions of RMBS rated by CRAs on January 1, 2013. Column 1 reports the numbers of RMBS rated by CRAs. Columns 2 and 3 report the numbers of RMBS rated solely by one CRA and jointly rated by other CRAs, respectively. Columns 4 to 7 report the numbers of RMBS jointly rated by pairs of CRAs. CRAs No. of RMBS Rated Rated Solely by the CRA Joint-Rated by Other CRAs Joint-Rated by S&P Joint-Rated by Moody’s Joint-Rated by Fitch Joint-Rated by DBRS S&P 33,262 11,636 21,626 - 10,063 12,695 1,393 Moody’s 16,433 2,810 13,623 10,063 - 5,141 310 Fitch 18,407 2,124 16,283 12,695 5,141 - 873 DBRS 4,196 2,639 1,557 1,393 310 873 - Total 44,451 19,209 25,242 Table 3 RMBS Ratings and Rating Changes During the 2008–2009 Financial Crisis This Table reports the RMBS rating distributions and changes during the 2008–2009 financial crisis by CRAs. Panel A reports the average outstanding RMBS ratings at the end of 2007 and 2009 by CRAs. The alphanumeric rating symbols are converted to a numerical rating variable as follows: RATING = 1 for AAA (Aaa), 2 for AA (Aa), up to 8 for CC/C rated RMBS. Parenthetical values indicate the number of RMBS ratings. Panel B reports the percentage of RMBS in the top two rating categories (AAA/AA) and the bottom rating category (CC/C) at the end of 2007 and 2009. S&P Moody’s Fitch DBRS Panel A. Average Outstanding RMBS Ratings 12/31/2007 2.25 (85,420) 2.25 (62,918) 2.19 (49,179) 2.62 (4,821) 12/31/2009 4.39 (67,515) 4.75 (47,769) 4.85 (43,884) 5.26 (5,600) Change 2.14 2.50 2.67 2.65 Panel B. Percentages of Top and Bottom Rating Categories % of AAA/AA 12/31/2007 66.65% 67.20% 68.92% 55.11% 12/31/2009 37.43% 26.51% 34.26% 22.71% % of CC/C 12/31/2007 0.00% 1.91% 2.34% 1.24% 12/31/2009 12.55% 20.25% 35.12% 45.66% Table 4 Pairwise Comparisons of Outstanding Ratings on Joint-Rated RMBS in 2013. This Table reports the average outstanding ratings on RMBS jointly rated by pairs of CRAs. Alphanumerical ratings are converted to a numerical scale: RATING = 1 for AAA (Aaa), 2 for AA (Aa), up to 8 for CC/C rated RMBS. The first two columns list the CRA names in the pair, and the third column reports the number of joint-rated RMBS. Columns 4 and 5 report the mean RATING from the CRAs, and column 6 reports the mean rating difference. The last five columns show the percentages of RMBS with the two ratings differing by multiple letter grades, one letter grade, or the same. CRA A CRA B Number of RMBS Mean A Rating Mean B Rating A - B CRA A Multi-Letter Lower CRA A One-Letter Lower Same Rating CRA A One-Letter Higher CRA A Multi-Letter Higher Panel A. Pairwise Comparisons of Legacy CRAs S&P Moody’s 10,063 4.73 5.43 -0.70*** 3.62% 18.21% 28.90% 22.38% 26.91% S&P Fitch 12,695 5.25 6.11 -0.86*** 2.34% 5.74% 35.53% 32.42% 23.96% Moody’s Fitch 5,141 5.67 6.01 -0.34*** 8.11% 13.03% 27.74% 39.37% 11.75% Panel B. Pairwise Comparisons of DBRS with Legacy CRAs S&P DBRS 1,393 5.73 6.98 -1.25*** 0.14% 2.30% 25.13% 44.08% 28.36% Moody’s DBRS 310 6.37 7.30 -0.93*** 4.84% 1.29% 17.74% 55.16% 20.97% Fitch DBRS 873 6.97 7.12 -0.15*** 3.44% 8.36% 67.01% 14.78% 6.41% *** indicates that the mean difference is statistically significant at the 1% level. Table 5 AUC Statistics of RMBS Jointly Rated by Pairs of Legacy CRAs This Table reports the one-, three-, and ten-year Area Under the Curve (AUC) statistics of RMBS jointly rated by pairs of legacy CRAs. No. of Non-defaults (No. of Defaults) is the number of RMBS that survived (defaulted) within one-, three-, and ten-year horizons. S&P and Moody’s Joint-rated S&P and Fitch Joint-rated Moody’s and Fitch Joint-rated S&P Moody’s S&P Fitch Moody’s Fitch Panel A. One-Year AUC Statistics AUC Statistics 0.9062 0.7993*** 0.8825 0.7981*** 0.8111 0.8565*** No. of Non-defaults 9,149 11,342 4,280 No. of Defaults 914 1,353 861 Panel B. Three-Year AUC Statistics AUC Statistics 0.9172 0.8160*** 0.8867 0.8209*** 0.8448 0.9076** No. of Non-defaults 8,252 10,216 3,686 No. of Defaults 1,811 2,479 1,455 Panel C. Ten-Year AUC Statistics AUC Statistics 0.9187 0.8352*** 0.8802 0.8142*** 0.8681 0.9102*** No. of Non-defaults 7,190 8,924 3,081 No. of Defaults 2,873 3,771 2,060 ***, **, * indicate that the difference in AUC statistics between the two CRAs is statistically significant at the 1%, 5%, or 10% levels, respectively. Table 6 AUC Statistics of RMBS Jointly Rated by DBRS and Legacy CRAs This Table reports the one-, three-, and ten-year Area Under the Curve (AUC) statistics of RMBS jointly rated by DBRS and legacy CRAs. No. of Non-defaults (No. of Defaults) is the number of RMBS that survived (defaulted) within one-, three-, and ten-year horizons. S&P and DBRS Joint-rated Moody’s and DBRS Joint-rated Fitch and DBRS Joint-rated S&P DBRS Moody’s DBRS Fitch DBRS Panel A. One-Year AUC Statistics AUC Statistic 0.8333 0.6153*** 0.6554 0.5635*** 0.7226 0.6223*** No. of Non-defaults 1,220 244 728 No. of Defaults 173 66 145 Panel B. Three-Year AUC Statistics AUC Statistic 0.8345 0.6259*** 0.6823 0.5903*** 0.7464 0.6376*** No. of Non-defaults 1,108 206 653 No. of Defaults 285 104 220 Panel C. Ten-Year AUC Statistics AUC Statistic 0.8489 0.6442*** 0.7072 0.6017*** 0.7520 0.6454*** No. of Non-defaults 994 190 610 No. of Defaults 399 120 263 ***, **, * indicate that the difference in AUC statistics between the two CRAs is statistically significant at the 1%, 5%, or 10% levels, respectively. Table 7 Ten-Year Hit Rates and False Alarm Rates of RMBS Jointly Rated by Legacy CRAs This Table reports the ten-year Hit Rate (HR) and False Alarm Rate (FAR) of joint-rated RMBS by each legacy CRA’s rating category at the beginning of 2013. HR is defined as the number of RMBS rated at or below the rating category that defaulted within ten years as a fraction of the total number of defaults. FAR is defined as the number of RMBS rated at or below the rating category that did not default within ten years as a fraction of the total number of non-defaulting RMBS. S&P Moody’s S&P Fitch Moody’s Fitch Rating HR FAR HR FAR HR FAR HR FAR HR FAR HR FAR AAA 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 AA 0.997 0.849 1.000 0.946 0.996 0.862 1.000 0.932 0.999 0.949 1.000 0.925 A 0.990 0.651 0.999 0.868 0.990 0.712 0.997 0.840 0.998 0.898 1.000 0.836 BBB 0.980 0.473 0.996 0.739 0.983 0.584 0.990 0.736 0.996 0.781 0.999 0.724 BB 0.966 0.329 0.982 0.578 0.968 0.465 0.985 0.629 0.989 0.603 0.998 0.559 B 0.951 0.252 0.958 0.403 0.955 0.372 0.976 0.555 0.967 0.407 0.988 0.446 CCC 0.902 0.156 0.842 0.231 0.929 0.280 0.944 0.435 0.830 0.161 0.941 0.238 CC/C 0.440 0.026 0.164 0.060 0.564 0.073 0.857 0.284 0.127 0.041 0.827 0.114 Table 8 Ten-Year Hit Rates and False Alarm Rates of RMBS Jointly Rated by Legacy CRAs and DBRS This Table reports the ten-year Hit Rate (HR) and False Alarm Rate (FAR) of joint-rated RMBS by each CRA’s rating category at the beginning of 2013. HR is defined as the number of RMBS rated at or below the rating category that defaulted within ten years as a fraction of the total number of defaults. FAR is defined as the number of RMBS rated at or below the rating category that did not default within ten years as a fraction of the total number of non-defaulting RMBS. S&P DBRS Moody’s DBRS Fitch DBRS Rating HR FAR HR FAR HR FAR HR FAR HR FAR HR FAR AAA 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 AA 1.000 0.899 1.000 0.933 0.953 1.000 0.947 1.000 1.000 0.975 1.000 0.959 A 0.992 0.815 0.992 0.889 0.921 1.000 0.916 1.000 1.000 0.943 1.000 0.918 BBB 0.992 0.716 0.992 0.856 0.895 1.000 0.889 1.000 1.000 0.890 1.000 0.887 BB 0.985 0.616 0.990 0.801 0.853 0.992 0.832 0.992 1.000 0.846 1.000 0.823 B 0.982 0.539 0.987 0.737 0.695 0.983 0.789 0.983 0.996 0.782 1.000 0.757 CCC 0.965 0.422 - - 0.500 0.950 - - 0.985 0.669 - - CC/C 0.471 0.053 0.977 0.691 0.116 0.133 0.768 0.967 0.951 0.457 0.992 0.703 Table 9 Marginal Predictive Power for Default of Joint-rated RMBS This Table reports the results from three probit regressions of ten-year defaults of joint-rated RMBS. The sample includes RMBS jointly rated by DBRS and one legacy CRA at the beginning of 2013. The dependent variable, Default, equals 1 for RMBS that defaulted between 2013 and 2022, and 0 otherwise. The explanatory variables are the 2013 numerical RATING (= 1 for AAA, 2 for AA, up to 8 for CC/C rated RMBS) by DBRS and the legacy CRAs. S&P and DBRS Joint-rated Moody’s and DBRS Joint-rated Fitch and DBRS Joint-rated Intercept -10.814*** -20.797*** -16.289*** DBRS Ratings -1.595*** 1.335 0.024 S&P Ratings 3.116*** Moody’s Ratings 1.022*** Fitch Ratings 2.111*** Deal Fixed Effect Yes Yes Yes No. Obs. 1,390 308 873 *** indicates that the coefficient is statistically significant at the 1% level. Table 10 Outcomes of CC/C-Rated RMBS by DBRS This Table reports the ten-year outcomes of RMBS that were rated CC/C by DBRS at the beginning of 2013. Panel A reports the percentages of RMBS with five different outcomes: Defaulted, Remained CC/C Rated, Upgraded to Other Junk Ratings, Upgraded to Investment Grade Ratings, or Paid-off. Panels B to D report the outcomes of those RMBS jointly rated by DBRS and legacy CRAs, breaking down the outcomes in the rightmost columns by the legacy CRAs’ 2013 ratings. Panel A. All CC/C-Rated RMBS by DBRS. Defaulted 28.14% Remain CC/C Rated 40.99% Upgraded to Other Junk Ratings 6.54% Upgraded to Investment Grade 6.54% Paid-off 17.79% Number of RMBS 1,681 Panel B. Jointly rated by S&P. All CC/C CCC B > B Defaulted 36.21% 78.01% 34.82% 4.27% 1.26% Remained CC/C Rated 25.63% 9.13% 41.25% 13.68% 4.40% Upgraded to Other Junk Ratings 8.73% 3.73% 8.21% 16.24% 12.58% Upgraded to Investment Grade 9.94% 0.41% 6.25% 22.22% 28.30% Paid-off 19.50% 8.71% 9.46% 43.59% 53.46% Number of RMBS 1,077 241 560 117 159 Panel C. Jointly Rated by Moody’s All CC/C CCC B > B Defaulted 44.27% 45.71% 58.33% 6.90% 0.00% Remain CC/C Rated 22.90% 37.14% 26.19% 3.45% 6.67% Upgraded to Other Junk Ratings 9.16% 5.71% 5.36% 37.93% 6.67% Upgraded to Investment Grade 9.16% 5.71% 2.38% 31.036% 30.00% Paid-off 14.50% 5.71% 7.71% 20.69% 56.67% Number RMBS 262 35 168 29 30 Panel D. Jointly Rated by Fitch All CC/C CCC B > B Defaulted 37.83% 47.35% 6.84% 6.90% 6.25% Remain CC/C Rated 25.65% 29.55% 15.38% 3.45% 12.50% Upgraded Other Junk Ratings 9.57% 6.82% 20.51% 20.69% - Upgraded to Investment Grade 8.26% 5.30% 9.40% 34.48% 50.00% Paid-off 18.70% 10.98% 47.86% 34.48% 31.25% Number of RMBS 690 528 117 29 16 Table 11 Ten-Year Default Rates of Single-Rated vs. Joint-Rated RMBS This Table reports S&P and DBRS ten-year default rates by letter rating at the beginning of 2013. Single-rated RMBS are rated by S&P or DBRS only. Joint-rated RMBS are rated by S&P (DBRS) and at least one other CRA. S&P Ratings DBRS Ratings Single-Rated Joint-Rated Single-Rate Joint-Rated AAA 1.61% (1,673) 0.96% (2,283) 0.00% (973) 0.00% (72) AA 3.30% (1,090) 1.66% (2,657) 0.00% (564) 6.38% (47) A 1.65% (908) 2.38% (2,315) 0.00% (428) 0.00% (37) BBB 3.89% (874) 4.12% (2,114) 0.00% (130) 1.67% (59) BB 7.27% (578) 6.12% (1,390) 0.00% (46) 1.56% (66) B 11.69% (907) 12.79% (1,697) 0.00% (42) 7.84% (51) CCC 35.33% (3,184) 47.05% (5,197) - - CC/C 81.42% (2,422) 79.71% (3,973) 1.32% (456) 38.12% (1,225) Total 28.85% (11,636) 28.30% (21,626) 0.23% (2,639) 30.57% (1,557) Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8595537","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":580075180,"identity":"7d52446a-f52d-42e7-866e-0b384267cd67","order_by":0,"name":"Gina Nicolosi","email":"","orcid":"","institution":"Northern Illinois University","correspondingAuthor":false,"prefix":"","firstName":"Gina","middleName":"","lastName":"Nicolosi","suffix":""},{"id":580075181,"identity":"e90fdf75-98bc-42ff-923e-ba2379e08a5a","order_by":1,"name":"Lei Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAnklEQVRIiWNgGAWjYBACPgYeIFkBJhkkiNLCBlZ8hmQtjG0QDpFa+M8efMw7746MwQHmg7d5iLPlXLIx77ZnPAYH2JKtidPC2GMmzbvtMFALj5k0cVqYgSp554C08H8jUgsbSEsD2BY2IrXw8Bgbzjl2mEfyMJux5RxitPDznzF88KbmsD3f8eaHN94QowUBmElTPgpGwSgYBaMAHwAA0p8lppjugwgAAAAASUVORK5CYII=","orcid":"","institution":"Northern Illinois University","correspondingAuthor":true,"prefix":"","firstName":"Lei","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2026-01-13 20:38:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8595537/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8595537/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101297496,"identity":"f65afa40-0f53-481e-984c-a8aed2a0ac65","added_by":"auto","created_at":"2026-01-28 09:27:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":71607,"visible":true,"origin":"","legend":"\u003cp\u003eRMBS Default Distributions by Years and CRAs\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8595537/v1/d4af9c5be981db6f9089330b.png"},{"id":101241625,"identity":"7475e38c-cd0e-4ed7-ac4b-2c2842925e6f","added_by":"auto","created_at":"2026-01-27 15:42:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":63059,"visible":true,"origin":"","legend":"\u003cp\u003eS\u0026amp;P and DBRS Rating Distributions\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8595537/v1/4a2e6963454eca43acb3f0a6.png"},{"id":101299403,"identity":"168ebc81-3c30-463b-96c0-34da0612494e","added_by":"auto","created_at":"2026-01-28 09:41:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1721506,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8595537/v1/2decbc8e-4a1b-4ff3-b668-27454f30cb55.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Are Conservative Ratings More Accurate? An Evaluation and Comparison of RMBS Ratings","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCritics, ranging from journalists and politicians to academics, blamed the US housing bubble and the subsequent financial crisis from 2008 to 2009 partly on severely inflated and grossly inaccurate credit ratings of residential mortgage-backed securities (RMBS) and other structured finance products (see, for example, Benmelech and Dlugosz, 2010; Lucchetti and Ng, 2007; United States Financial Crisis Inquiry Commission, 2011; and White, 2009). They argued that conflicts of interest in the issuer-pay model, lack of competition in the rating industry, issuers’ rating shopping behavior, investors’ capital arbitrage considerations, and the complexity of deal structures led to significantly inflated and inaccurate credit ratings of RMBS, particularly those backed by subprime mortgage loans. The overly optimistic ratings, in turn, resulted in capital misallocation and fueled the US housing bubble in the mid-2000s. The massive rating downgrades of large amounts of RMBS during the financial crisis lent support to this view.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn response to the financial crisis, the US Congress passed the Dodd-Frank Act in 2010. One major goal of the Act is to improve the accuracy and quality of credit ratings by reducing conflicts of interest, improving transparency, promoting competition, and increasing oversight of the US credit rating industry (Lu et al., 2023). In a study of the impact of the Act on credit ratings, Dimitrov et al. (2015) find a downward trend in US corporate bond ratings since its passage. The authors argue that CRAs became more protective of their reputational capital after the Act and tightened their rating standards to minimize legal/regulatory penalties due to default detection failures. Other studies find a similar increase in rating conservatism (i.e., lower ratings on bond issues with similar observable characteristics), suggesting that rating standards have tightened over time (Blume et al., 1998; Alp, 2013; Baghai et al., 2014).\u003csup\u003e[1]\u003c/sup\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough credit ratings on structured finance products like RMBS were at the eye of the financial storm of 2008-2009, most post-crisis empirical studies on rating standards and accuracy focus on corporate bond ratings. Using regulatory disclosure data from 2013 to 2022, we examine RMBS ratings from three legacy CRAs (S\u0026amp;P, Moody’s, Fitch) and DBRS instead for two reasons. First, prior studies suggest that structured finance products are more prone to rating inflation due to asset complexity and opacity (Skreta and Veldkamp, 2009; Opp et al., 2013). Furthermore, the market for RMBS credit rating has become more competitive since the financial crisis (Livingston et al., 2021). Theoretical and empirical studies indicate that increased competition has a significant impact on rating inflation/accuracy (Becker and Milbourn, 2011; Skreta and Veldkamp, 2009; Flynn and Ghent, 2017).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe find that DBRS ratings on RMBS are the most conservative, both before and after the financial crisis. On the other hand, S\u0026amp;P ratings are the most generous among the four CRAs. Interestingly, the conservatism of DBRS ratings did not result in a loss of market share for the small CRA. Rather, as reported by Livingston et al. (2021), DBRS increased its market share of RMBS ratings from about 1% in 2006 to almost 15% by 2019. Given the severity of the financial crisis, it is not surprising that there was a demand for more conservative ratings. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile there are both pushes from regulators and pulls from investors for conservative ratings, they are not unequivocally more desirable. Providing investors with early warnings of potential defaults and protecting CRAs from reputational loss and legal/regulatory penalties, conservative ratings likely also lead to high rates of false alarms and punish some low-risk bond issuers with unjustifiably pessimistic ratings and consequently higher financing costs. For example, Baghai et al. (2014) find evidence that the firms most affected by rating conservatism issue less debt and, consequently, experience lower growth. Furthermore, excessively pessimistic ratings also deter risk-averse investors from investing in otherwise creditworthy bonds. More importantly, it is not clear whether conservative ratings are more accurate, and the empirical evidence is mixed. On the one hand, Dimitrov et al. (2015) show that credit ratings issued after the Dodd-Frank Act, while more conservative, are less informative and more likely to trigger false alarms. On the other hand, a recent study by Afik and Galil (2025) finds that US corporate bond ratings have improved their accuracy while becoming more conservative over the last several decades. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUsing a simple numerical example, we demonstrate that there are two different possible causes of rating conservatism: tighter rating standards and more accurate default risk estimation. In the former case, CRAs make no improvement to their risk assessment frameworks but instead apply more stringent standards, resulting in lower, but not necessarily more accurate, average ratings. In the latter case, CRAs improve their risk estimation techniques and consequently more accurately assess default risk, which can result in lower average ratings in some cases (Afik and Galil, 2025).\u003c/p\u003e\n\u003cp\u003eWhile both stringent rating standards and improved rating methodology can lead to rating conservatism, the testable empirical implications differ. Stringent rating standards without more precise default risk estimation do not lead to an overall improvement in rating accuracy; the resulting increased default detection comes at the expense of a higher false alarm risk. Conversely, rating conservatism due to better rating methodology and information leads to an overall improvement in rating accuracy and thus an optimal balance between type I and type II errors.\u003c/p\u003e\n\u003cp\u003eTo determine whether stringent standards or more accurate risk estimation drives the conservatism of DBRS ratings, we empirically compare the accuracy and false alarm/default detection tradeoffs of RMBS ratings issued by DBRS and the three legacy CRAs. First, we find that DBRS ratings have the lowest predictive power of default risk among the four CRAs. In contrast, while S\u0026amp;P has the most generous ratings, its ratings are the most accurate for one-, three-, and ten-year horizons. Second, the most conservative DBRS ratings result in a significantly higher risk of false alarms. For example, the ten-year false alarm rate of the lowest rating category is almost 70% for DBRS, much higher than that of S\u0026amp;P (5%). Finally, DBRS ratings have no or weak marginal predictive power for defaults beyond that of the three legacy CRAs. On the other hand, ratings from the three legacy CRAs have strong additional predictive power for defaults, particularly for those in the lowest rating category. The findings strongly suggest that DBRS’s rating conservatism is driven by overly stringent rating standards rather than better default risk estimation. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe severity of the financial crisis unsurprisingly led to widespread criticism of loose rating standards and demands for more conservative ratings. Contrary to conventional wisdom, this study’s findings highlight the distinction between conservative ratings and accurate ratings. While an informative and accurate rating system must possess higher discriminatory powers of default risk, it is not necessarily ‘tougher.’ Consequently, over-emphasizing rating standard rigor might be counterproductive; indeed, Dimitrov et al. (2015) argued that ‘increasing the legal and regulatory costs of CRAs might have an adverse effect on the quality of credit ratings.’ \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study also contributes to the debate on the relationship between industry competition and rating inflation/quality. On the one hand, several studies argue and find evidence that increased competition among CRAs leads to rating inflation and/or lower rating quality due to intensive rating shopping by bond issuers and cratering behavior by CRAs (Becker and Milbourn, 2011; Bolton et al., 2012; Flynn and Ghent, 2018). On the other hand, Xia (2014) find that the entry of a new CRA increases rating information content and improves rating quality. Consistent with the mixed empirical evidence, our findings suggest that increased competition does not necessarily lead to rating inflation or improved rating accuracy. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOver the last two decades, the US Securities and Exchange Commission (SEC) has adopted a series of rules to implement the provisions of the Credit Rating Agency Reform Act and the Dodd-Frank Act. A major goal of these rules is to increase transparency and facilitate rating performance evaluation and comparison (Lu et al., 2023). For example, CRAs registered as a Nationally Recognized Statistical Rating Organization (NRSRO) are required to file an annual disclosure NRSRO Form. An important part of the NRSRO Form is the credit rating transition and default matrices. In addition, SEC Rule 17g-7 requires registered CRAs to publicly disclose their rating actions at the individual issuer/issue level. Taking advantage of the regulatory disclosure data, this study presents the first comprehensive and independent evaluation and comparison of RMBS ratings from four different CRAs over short, intermediate, and long horizons.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study is closely related to Dimitrov et al. (2015) and Afik and Galil (2025) but differs in several important aspects. First, different from those studies on corporate bond ratings, we examine ratings on RMBS, a more complex and opaque asset class that is harder to rate and more prone to rating inflation (Skreta and Veldkamp, 2009, Opp et al., 2013 ). The information opacity and issue complexity suggest that RMBS rating conservatism variations across CRAs might reflect differences in rating methodologies, which in turn affect rating accuracy. Second, earlier studies investigated changes in rating conservatism and accuracy over time, while we analyze cross-sectional differences between CRAs at a point in time. A major challenge in time-series studies is the potential for contemporaneous confounding factors. Indeed, whether rating conservatism has increased over time has been disputed by Krystyniak and Staneva (2024), who suggest that the average rating declines over time might result from increases in default risk. Additionally, when bond characteristics vary over time, portfolio-dependent measures of rating accuracy become unreliable for intertemporal comparisons. For example, Hamerle et al. (2003) demonstrate that the two most commonly used metrics, the Accuracy Ratio and AUC statistic, are influenced not only by the accuracy of CRAs’ default probability estimations but also by the composition of the rated bond portfolios. As a result, these measures are ill-suited for comparing rating accuracy across time. Instead, we compare ratings on RMBS that are jointly rated by pairs of CRAs at one point in time.\u003c/p\u003e\n\u003cp\u003eThis study is also related to Doherty et al. (2012) who examine S\u0026amp;P’s entry into the municipal bond rating market. Consistent with Doherty et al. (2012), we observe that new market entrants apply more stringent rating standards, alleviating the concern that increased competition may lead to the ‘race to the bottom’ problem. Different from Doherty et al. (2012), however, we find the new entrant into the RMBS market does not provide more informative ratings. \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe remainder of the paper is organized as follows. Section I illustrates two possible causes of rating conservatism through a numerical example. Section II presents the sample data and descriptive statistics. Section III compares RMBS rating levels between the four CRAs, and Section IV analyzes and compares RMBS rating accuracy. Section V concludes.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e[1]\u003c/sup\u003e One study disputes the claim of increased rating conservatism (Krystyniak and Staneva, 2024). \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"I. Rating Conservatism: Stringency vs. Accuracy","content":"\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eUsing a numerical example, we illustrate two different potential reasons for rating conservatism - more stringent rating standards versus more accurate default risk estimation - and demonstrate their distinctive testable empirical implications.\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eSuppose there are three hundred bond issues that can be characterized in two dimensions: default risk and information transparency. Half of the bonds have a default probability of 25%, and the other half have a default probability of 5%. Two-thirds of the bonds are informationally transparent (i.e., the CRA can readily detect default probability), and the others are opaque. The two characteristics are independent of each other; that is, half of the transparent (opaque) bonds have a higher default risk, and the other half a lower default risk. The CRA cannot distinguish between low-risk and riskier opaque bond issues but can estimate their average default probability (15%).\u003c/p\u003e\n\u003cp\u003eThe CRA adopts a binary rating system based on its estimated bond default risk, 1 (0) indicating Non-default (Default) when the estimated default probability is less than (exceeds) a cutoff point. If the estimated default probability equals the cutoff point, the CRA randomly assigns a Default/Non-default rating. The cutoff point is subjective and can be interpreted as the CRA\u0026rsquo;s rating standard: a higher (lower) cutoff indicates looser (stricter) rating standards.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eA. Rating Standards and Tradeoff between Default Detection and False Alarms\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo illustrate the effects of rating standards, consider the following three cases. Under the Generous rating standard, the CRA sets the default probability cutoff at 20%, assigning a Non-default rating to low-risk transparent bonds and all opaque bonds, and a Default rating to high-risk transparent bonds. Under the Moderate rating standard, the CRA sets the default probability cutoff at 15%, assigning a Non-default rating to low-risk transparent bonds and half of the opaque bonds, and a Default rating to the remaining opaque bonds and high-risk transparent bonds. Under the Stringent rating standard, the CRA sets the default probability cutoff at 12%, assigning a Non-default rating only to low-risk transparent bonds and a Default rating to all opaque bonds and high-risk transparent bonds.\u003c/p\u003e\n\u003cp\u003eThe first three columns of Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e summarize the average ratings and default rates under the three different rating standards. The Stringent rating standard obviously leads to lower average ratings, or rating conservatism. In addition, the default rates of Non-default ratings decrease with the rating stringency, showing that tighter rating standards can reduce default detection failures. However, the default rates of Default rating also decrease with rating stringency, reflecting more false alarms (i.e., non-default bonds receiving a Default rating).\u003c/p\u003e\n\u003cp\u003eRating standard stringency entails a tradeoff between predicting actual defaults and avoiding false alarm rates. This tradeoff is commonly evaluated by two metrics: the Hit Rate (HR) and the False Alarm Rate (FAR).\u003csup\u003e2\u003c/sup\u003e The HR, defined as the percentage of defaulted bonds that were initially assigned a Default rating, measures the ratings\u0026rsquo; ability to correctly identify true defaults. The FAR is defined as the percentage of surviving bonds that were incorrectly assigned a Default rating, reflecting the system\u0026rsquo;s tendency to raise false alarms. An accurate and informative rating system maximizes HR while minimizing FAR. The fourth and fifth rows of Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e report the HR and FAR values under different rating standards. As expected, tighter standards raise the HR but also increase the FAR. Because none of the three standards achieves both a higher HR and a lower FAR, no single approach dominates.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eB. Rating Conservatism and Improved Default Risk Estimation\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eInstead of adjusting rating standards (i.e., the cutoff point), the CRA may improve its rating technology to enhance the rating information content and resolve the opacity problem. In this case, the CRA accurately estimates the individual default probability of the opaque bond issues and assigns a Default (Non-default) rating to all high-risk (low-risk) bonds. Under this Refined rating system, the average rating is 0.5, and the last column of Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e reports its default rates, HR, and FAR.\u003c/p\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eAlthough the Refined ratings may appear more conservative when compared to the Generous rating standard, this arises from more accurate default probability estimation and not from rating stringency. This distinction highlights a common misconception: that lower average ratings or rating conservatism necessarily indicate more stringent standards. As first noted by Afik and Galil (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e), rating conservatism may also result from improved estimation accuracy. Furthermore, higher average ratings, often perceived as \u0026ldquo;loose\u0026rdquo; or \u0026ldquo;inflated\u0026rdquo;, are not necessarily less accurate. Although the Refined rating system yields a higher average rating than the Stringent standard, we later demonstrate that it offers superior predictive power for default risk.\u003c/p\u003e\n\u003cp\u003eWhile the average ratings under the Refined and Moderate Ratings are identical, the Refined ratings clearly outperform the Moderate ratings by achieving both a higher HR and a lower FAR. When compared to the other two standards, however, the performance of the Refined ratings is more nuanced. Relative to the Generous (Stringent) standard, the Refined ratings generate a higher (lower) HR, but also a higher (lower) FAR.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eC. Rating Conservatism and Rating Accuracy\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eRegardless of the underlying reason, rating conservatism inevitably leads to an increase in FAR when compared to the Generous rating standard. Therefore, it is problematic to conclude that conservative ratings are less informative or accurate solely based on higher FAR, as posited by Dimitrov et al. (\u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). To examine which rating system best balances early warnings with false alarm avoidance, we use a statistic called the Area Under the Curve (AUC) to measure ratings\u0026rsquo; discriminatory power of default risk.\u003csup\u003e3\u003c/sup\u003e Based on HR and FAR, the AUC statistic has the following probabilistic interpretation. Suppose two bonds are randomly drawn: one from the distribution of surviving bonds and the other from the distribution of default bonds. Given the ratings of the two bonds, an otherwise uninformed investor naturally guesses that the bond with a better rating is a survivor and the lower-rated one is drawn from the pool of default bonds. In the case of two bonds with the same rating, the investor makes a random guess. Bamber (\u003cspan class=\"CitationRef\"\u003e1975\u003c/span\u003e) and Engelmann et al. (\u003cspan class=\"CitationRef\"\u003e2003a\u003c/span\u003e) show that the probability of a correct guess based solely on ratings exactly equals the AUC statistic. Thus, the AUC statistic ranges from 0.5 for a non-informative rating system with no discriminatory power of default risk to 1 for a perfect rating system.\u003c/p\u003e\n\u003cp\u003eQin and Zhou (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) provide a detailed description of the AUC statistic, its statistical properties, and estimation techniques for a multi-level rating system. Adapting the estimation technique to a binary rating system, we can easily show that AUC\u0026thinsp;=\u0026thinsp;0.5\u0026thinsp;+\u0026thinsp;0.5*(HR \u0026ndash; FAR). The last row of Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e reports the AUC statistics for the four rating systems. The Refined rating system has the highest AUC statistic, demonstrating its higher discriminatory power of default risk. Interestingly, the AUC statistics do not vary with rating standard cutoff points. This is not surprising because tightening rating standards does not make the rating more informative. More stringent rating standards can effectively increase the HR, but the corresponding increase in FAR results in no net improvement of rating accuracy or discriminatory power of default risk. Thus, tightening rating standards does not improve overall rating accuracy.\u003c/p\u003e"},{"header":"II. Data Collection and Sample Description","content":"\u003cp\u003eWe use two regulatory disclosure data sources. The first is the 2018 and 2020 NRSRO Forms, retrieved from the SEC\u0026rsquo;s Edgar platform. Exhibit 1 of the NRSRO Form contains the one-, three-, and ten-year single-cohort rating transition and default matrices. The transition matrix reports the aggregate number and distribution of outstanding ratings at year-end, as well as percentages of issuers/issues that moved from one rating category to another within the specified horizon.\u003c/p\u003e \u003cp\u003eThe second data source is the granular SEC Rule 17g-7 data. SEC Rule 17g-7 requires registered CRAs to publicly disclose, with a one-year lag, rating actions taken on or after June 15, 2012. Hence, the January 2024 Rule 17g-7 data update downloaded from each CRA website includes a decade (i.e., mid-2012 or earlier through the end of 2022) of the four CRAs\u0026rsquo; rating actions. The Rule also requires rated debt instruments to be identified by 9-digit CUSIP numbers whenever available, facilitating the matching of RMBS rated by different CRAs. RMBS not identified by CUSIP numbers are eliminated, as are preliminary or provisional ratings, commercial paper and national/regional-specific ratings, other specialized ratings, and any RMBS identified as in default by at least one CRA before 2013.\u003c/p\u003e \u003cp\u003eOur main sample consists of 44,451 RMBS with outstanding ratings from at least one CRA on January 1, 2013. The first column of Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reports the distributions of RMBS rated by CRAs, with S\u0026amp;P rating almost three-quarters of the RMBS in the sample, Moody\u0026rsquo;s and Fitch each rating about 40%, and DBRS rating less than 10%. The second and third columns report the number of RMBS rated solely by one CRA (about 44% of the sample) and the number of RMBS rated jointly by two or more CRAs, respectively. Columns 4 to 7 present the numbers of RMBS jointly rated by pairs of CRAs. There are significant overlaps among the three legacy CRAs. In addition, a substantial portion of RMBS rated by DBRS are also rated by S\u0026amp;P. On the other hand, only 310 RMBS are jointly rated by Moody\u0026rsquo;s and DBRS.\u003c/p\u003e \u003cp\u003eAn important aspect of the study is the analysis of credit ratings\u0026rsquo; predictive power for defaults. S\u0026amp;P, Fitch, and DBRS assign a D rating when a bond defaults. On the other hand, instead of a D rating, Moody\u0026rsquo;s C rating indicates RMBS \u0026lsquo;typically in default with little prospect for recovery of principal and interest.\u0026rsquo; Therefore, defaults are identified based on rating withdrawals due to default, C ratings from Moody\u0026rsquo;s, or D ratings from S\u0026amp;P, Fitch, or DBRS. If default rating assignment dates differ across CRAs, we use the earliest date.\u003c/p\u003e \u003cp\u003eFrom 2013 to 2022, more than a quarter of the sample RMBS (i.e., 12,031) defaulted. Panel A of Figure I reports the annual number of defaults. The default frequency decreased over the sample period, with more than a third occurring in 2013. Panel B shows the number of defaulted RMBS rated by each CRA at the beginning of the sample period. Almost 80% of defaulted RMBS were rated by S\u0026amp;P, consistent with its larger market share.\u003c/p\u003e"},{"header":"III. Rating Conservatism","content":"\u003cp\u003eTo investigate rating conservatism across the four CRAs, we first examine the average RMBS rating levels shortly before and after the 2008\u0026ndash;2009 financial crisis. Using the 2018 and 2020 NRSRO Forms\u0026rsquo;10-year rating transition matrices, we calculate the average RMBS ratings at the end of 2007 and 2009. The alphanumeric rating symbols are converted to a numerical rating variable as follows: \u003cem\u003eRATING\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1 for AAA (Aaa), 2 for AA (Aa), up to 8 for CC/C rated RMBS.\u003csup\u003e4\u003c/sup\u003e We group CC and C ratings together since not all CRAs assign both ratings: S\u0026amp;P (DBRS) does not assign C (CC) ratings.\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePanel A of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reports average outstanding ratings and the number of RMBS ratings by CRAs. At the end of 2007, the average ratings from the three legacy CRAs cluster around 2.2, approximately 0.4 letters higher than the average DBRS ratings of 2.62. While all four CRAs massively downgraded their RMBS ratings during the financial crisis by at least two letter grades by the end of 2009, DBRS-rated RMBS experienced larger downgrades than S\u0026amp;P-rated RMBS: an average downgrade of 2.65 letter grades by DBRS vs. 2.14 by S\u0026amp;P. This suggests that DBRS increased its rating conservatism relative to S\u0026amp;P during the financial crisis.\u003c/p\u003e \u003cp\u003ePanel B reports the proportion of sample RMBS assigned the top (AAA or AA, \u003cem\u003eRATING\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1 or 2) and bottom ratings (CC/C, \u003cem\u003eRATING\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8) by each of the four CRAs. Before the financial crisis, more than two-thirds of RMBS rated by the three legacy CRAs received a AAA or AA rating. In contrast, only 55% of RMBS rated by DBRS fell into the top rating categories, indicating a more conservative approach by the smaller CRA during the pre-crisis period. Following the crisis, the proportion of RMBS receiving the lowest CC/C rating rose substantially across all CRAs (e.g., Moody\u0026rsquo;s share of ratings in this category increased from less than 2% to 20%). Notably, DBRS-rated RMBS have the highest proportion of CC/C ratings (46%). In contrast, the percentage of RMBS rated by S\u0026amp;P with the lowest rating is significantly lower at less than 13%.\u003c/p\u003e \u003cp\u003eThese findings suggest that DBRS ratings were more conservative both before and after the financial crisis. Rather than penalizing DBRS\u0026rsquo;s tougher stance, market share shifts suggest an increased preference towards rating conservatism in the wake of the crisis. Consistent with Livingston et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the number of RMBS rated by DBRS increased from 4,821 to 5,600 in the two years, contrasting sharply with the decline of 20% or more experienced by S\u0026amp;P and Moody\u0026rsquo;s.\u003c/p\u003e \u003cp\u003eHowever, differences in average ratings may reflect variation in the composition of RMBS rated by each CRA. If legacy CRAs avoided riskier deals, such as those backed by subprime loans, then DBRS would appear more conservative simply because it rated disproportionately risky RMBS. To address this possibility, we use SEC Rule 17g-7 data to perform pairwise ratings comparisons of RMBS jointly rated by two CRAs at the beginning of 2013 and present the results in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The CRAs in the unique pair are listed in the first two columns, and the number of RMBS jointly rated by the pair is provided in the third column. Columns 4 and 5 report the CRAs\u0026rsquo; mean \u003cem\u003eRATING\u003c/em\u003es, and column 6 reports the difference in the mean \u003cem\u003eRATING\u003c/em\u003es. The last five columns report the distribution of rating disagreement: the percentages of RMBS with the two ratings differing by multiple letter grades, one letter grade, or the same.\u003c/p\u003e \u003cp\u003eResults in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e show that, when evaluating the same RMBS, DBRS, on average, assigns lower ratings than the legacy CRAs. This is most pronounced relative to S\u0026amp;P and Moody\u0026rsquo;s. Among the 1,393 RMBS jointly rated by S\u0026amp;P and DBRS, 44.08% (28.36%) received an S\u0026amp;P rating one (multiple) letter grade(s) higher than DBRS. In contrast, fewer than 3% were rated higher by DBRS than by S\u0026amp;P. These comparisons, based on identical securities at the same point in time, underscore DBRS\u0026rsquo;s more conservative approach. Conversely, S\u0026amp;P appears to be the least conservative of the four CRAs, with its ratings, on average, one letter grade better than its competitors.\u003c/p\u003e"},{"header":"IV. Rating Accuracy","content":"\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eSince the 2008\u0026ndash;2009 financial crisis, investors and financial regulators have become increasingly concerned about rating inflation and loose rating standards. However, as illustrated in Section I, conservative ratings are not equivalent to accurate ratings. On the one hand, improved default risk estimation may result in both more conservative and accurate ratings. On the other hand, rating conservatism driven by more stringent standards alone does not improve rating accuracy. To determine whether stringent standards or more accurate risk estimation drives the conservatism of DBRS ratings, we empirically compare the accuracy and false alarm/default detection tradeoffs of RMBS ratings issued by DBRS and the three legacy CRAs.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eA. AUC Statistic Comparisons\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eFollowing Qin and Zhou (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e), we first use the AUC statistic to quantify each CRA\u0026rsquo;s ability to discriminate between defaulting and surviving RMBS. As noted earlier, AUC statistics are determined by both the accuracy of CRAs\u0026rsquo; default probability estimations and the composition of the rated bond portfolios. To remove the effects of different bond portfolios, we focus on RMBS jointly rated by two CRAs.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e reports the one-, three-, and ten-year AUC statistics of RMBS jointly rated by pairs of legacy CRAs, and Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e extends the analysis to RMBS jointly rated by DBRS and the legacy CRAs. First, note that the four CRAs all have AUC statistics greater than 0.5, indicating that their ratings have varying degrees of discriminatory power for default risk. Second, for all three performance evaluation windows, S\u0026amp;P ratings consistently achieve the highest AUC statistics, ranging from 0.81 to 0.92. The differences in AUC statistics between S\u0026amp;P and the other three CRAs are statistically significant at the 1% level. At the other spectrum, the AUC statistics of DBRS, 0.65 or less, are significantly lower than those of the three legacy CRAs. Third, the differences in AUC statistics between Moody\u0026rsquo;s and Fitch, while statistically significant, are relatively modest. The empirical evidence strongly indicates that DBRS ratings, the most conservative, have the lowest overall accuracy, whereas S\u0026amp;P ratings, the least conservative, have the highest.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eB. Tradeoff between Default Detection and False Alarm\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eInvestors rely on ratings to assess creditworthiness, expecting lower ratings to signal higher default risk. When a low rating precedes a default, it serves as a useful early warning. However, if no default follows, the low rating becomes a false alarm. An informative rating system should provide credible early warnings and minimize false alarm risk.\u003c/p\u003e\n\u003cp\u003eTo evaluate this tradeoff, we adapt HR and FAR for multi-level rating systems. For rating classification X, HR is the proportion of bonds that ultimately default that were rated X or below (worse). A high HR indicates that the rating category captures a high percentage of default bonds, resulting in credible early warnings of future defaults. FAR is the proportion of surviving bonds that were rated X or below (worse). A high FAR means that the rating category predicts a high percentage of non-default bonds to default, resulting in a high risk of false alarms.\u003c/p\u003e\n\u003cp\u003eAs discussed in Section I, CRAs with better estimations of bond default risks have higher probabilities of assigning better ratings to creditworthy bonds and lower ratings to riskier bonds, leading to a higher HR and a lower FAR than their counterparts with less precise default risk estimations. Rating standards also affect the tradeoffs, but in a different way. CRAs with stringent rating standards are more likely to assign lower ratings than their competitors with more generous rating standards. As a result, CRAs with stringent rating standards are more likely to have both a higher HR and a higher FAR. The opposition is true for CRAs with generous rating standards.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e presents the ten-year HR and FAR statistics of each rating category for RMBS jointly rated by pairs of legacy CRAs, while Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e compares the two ratios between DBRS and each of the three legacy CRAs. Several patterns emerge.\u003csup\u003e6\u003c/sup\u003e First, no CRA has both a higher HR and a lower FAR consistently in every rating category, suggesting that no single CRA clearly dominates its counterparts. Second, S\u0026amp;P generally produces lower HR and FAR statistics than the other CRAs, especially compared to DBRS at the CC/C level. S\u0026amp;P\u0026rsquo;s HR at CC/C is 0.471 versus DBRS\u0026rsquo;s 0.977, while S\u0026amp;P\u0026rsquo;s FAR of 0.053 is much lower than DBRS\u0026rsquo;s FAR of 0.691. The findings highlight the expected tradeoff: S\u0026amp;P\u0026rsquo;s more generous ratings reduce false alarms but yield fewer early warnings, whereas DBRS\u0026rsquo;s conservative ratings provide more early warnings but also false positives. Thus, the higher overall accuracy of S\u0026amp;P ratings is likely due to a more optimal balance between early warnings and false alarms.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eC. Marginal Predictive Power of Default\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWhile earlier results suggest that conservative DBRS ratings have lower overall accuracy and higher false alarm rates, they may still add value as a corrective check against legacy CRAs\u0026rsquo; overly optimistic ratings. To investigate whether DBRS ratings offer marginal power in predicting default, we construct probit models of ten-year outcomes (i.e., default versus non-default) of RMBS jointly rated by DBRS and the legacy CRAs. The explanatory variables are numerical ratings (=\u0026thinsp;1 for AAA/Aaa, 2 for AA/Aa, up to 8 for CC/C rated RMBS) assigned by DBRS and the corresponding legacy CRA in 2013. If each rating conveys unique information concerning bond default risk, both rating coefficients should be statistically significant. Conversely, if the DBRS rating does not provide additional information beyond that of the legacy CRA\u0026rsquo;s assessment, its coefficient should be insignificant (and vice versa).\u003csup\u003e7\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eIn each of Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e\u0026rsquo;s probit regression models, the coefficients on the legacy CRA ratings are significantly positive, indicating that lower ratings from these CRAs, holding the DBRS rating constant, are associated with higher RMBS default probability. In contrast, the DBRS rating coefficients are either negative or insignificant, suggesting little to no marginal predictive power beyond other agencies\u0026rsquo; assessments. Overall, empirical evidence suggests that DBRS ratings provide little incremental information regarding default risk beyond that of the legacy CRAs, while the three legacy CRA ratings provide much stronger marginal predictive power even when DBRS ratings are included.\u003c/p\u003e\n\u003cp\u003eFinally, the legacy CRAs' marginal predictive power is particularly evident amongst the riskiest assets. Based on ten-year outcomes, each RMBS rated CC/C by DBRS at the beginning of 2013 is classified into five categories: \u003cem\u003eDefaulted\u003c/em\u003e if any CRA assigned the RMBS a default rating before the end of 2022; \u003cem\u003ePaid-off\u003c/em\u003e if any CRA withdrew its rating due to pay-off; \u003cem\u003eRemain CC/C Rated\u003c/em\u003e if the DBRS rating outstanding at the end of 2022 is CC/C; \u003cem\u003eUpgraded to Other Junk Ratings\u003c/em\u003e if the DBRS rating outstanding at the end of 2022 is CCC, B, or BB; and \u003cem\u003eUpgraded to Investment Grade\u003c/em\u003e if the DBRS rating outstanding at the end of 2022 is BBB or higher.\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003ePanel A of Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e shows that nearly 18% of DBRS\u0026rsquo;s lowest-rated RMBS were paid off within 10 years, and another 6% were upgraded to investment-grade ratings, implying that roughly one quarter of the lowest-rated RMBS were actually creditworthy. Panels B to D break down the outcome of those RMBS that were also rated by S\u0026amp;P, Moody\u0026rsquo;s, and Fitch, respectively. As shown by the first columns of the three panels, a similar percentage (23% to 39%) of those bonds were either paid off or upgraded to investment grade.\u003c/p\u003e\n\u003cp\u003eThe remaining columns demonstrate the marginal predictive power of legacy CRA ratings. The panels\u0026rsquo; rightmost columns report outcomes by the legacy CRA\u0026rsquo;s 2013 rating (i.e., CC/C, CCC, B, and BB or higher). When a legacy CRA was more optimistic and assigned a rating of B or higher, the \u003cem\u003eDefaulted\u003c/em\u003e and \u003cem\u003eRemained CC/C\u003c/em\u003e percentages are much lower, and the \u003cem\u003ePaid-off\u003c/em\u003e and \u003cem\u003eUpgraded to Investment Grade\u003c/em\u003e percentages are much higher. Conversely, when the legacy CRA agreed with DBRS\u0026rsquo;s pessimistic outlook, the \u003cem\u003eDefaulted\u003c/em\u003e rates are substantially higher.\u003c/p\u003e\n\u003cp\u003eThese findings confirm the strong marginal predictive power of legacy CRA ratings beyond DBRS ratings. The findings further indicate that DBRS\u0026rsquo;s overly stringent rating standards misclassified many creditworthy RMBS, resulting in unnecessary and undesirable false alarms.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eD. Robustness Check\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eThe above rating accuracy analyses focus on RMBS jointly rated by pairs of CRAs.\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eHowever, joint-rated RMBS account for only 37% (65%) of issues rated by DBRS (S\u0026amp;P). This limited coverage raises two concerns: the validity of the empirical results based on a portion of the sample and potential selection bias. To address these issues, we investigate the rating conservatism and accuracy of RMBS solely rated by S\u0026amp;P or DBRS relative to those with additional ratings.\u003csup\u003e9\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eFirst, we compute and compare the average ratings and rating distributions of single- and joint-rated RMBS. The average rating of RMBS solely rated by S\u0026amp;P (5.16) is comparable to that of jointly rated by S\u0026amp;P and at least one other CRA (5.00). However, the average rating of RMBS solely rated by DBRS (3.04) significantly differs from that of RMBS jointly rated by DBRS and at least one other CRA (7.03).\u003c/p\u003e\n\u003cp\u003eFigure II further reveals the differences in the underlying rating distributions, with Panel A (B) depicting the S\u0026amp;P (DBRS) rating distributions for both single- and joint-rated RMBS. The distributions of S\u0026amp;P ratings are similar between single- and joint-rated issues, suggesting that there is no significant credit quality difference between issues rated only by S\u0026amp;P and those jointly rated by other CRAs. In contrast, ratings for RMBS solely and jointly rated by DBRS display substantial distribution differences (Panel B). RMBS solely rated by DBRS generally show an overall higher credit quality (three quarters are rated A or above) than those jointly rated by DBRS and other CRAs (three quarters received the lowest CC/C rating from DBRS). However, a sizeable portion of DBRS single-rated issues (17%) is rated CC/C. These distribution differences suggest that RMBS with extremely low DBRS ratings are likely to acquire an additional rating (i.e., 73% of RMBS with a CC/C rating from DBRS are also rated by other CRAs), while those rated A or higher by DBRS tend to forego a second opinion (i.e., only 7% of RMBS with an A or higher rating from DBRS have an additional rating).\u003c/p\u003e\n\u003cp\u003eGiven the selection bias in DBRS ratings, it is important to examine the rating conservatism and accuracy of single-rated RMBS relative to joint-rated RMBS. However, as noted earlier, standard measures like AUC statistics are not appropriate when comparing ratings across different bond portfolios. Furthermore, the underlying default risk is not observable. To overcome these methodological challenges, we instead investigate the default rates of single- and joint-rated RMBS.\u003c/p\u003e\n\u003cp\u003eAs illustrated in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, a more informative rating system should assign higher (lower) ratings to more (less) creditworthy bonds; therefore, high (low) rating categories should have lower (higher) default rates than those of a less informative rating system. In contrast, a more stringent rating system tends to be cautious, assigning high ratings only to the most creditworthy RMBS and erring on the side of false alarms by giving low ratings to relatively safe RMBS; consequently, default rates are expected to be lower across both high and low rating categories than those of a less stringent rating system.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e reports the ten-year default rates of RMBS rated by S\u0026amp;P and DBRS.\u003csup\u003e10\u003c/sup\u003e The first (second) column shows the default rates by letter rating category of RMBS rated solely by S\u0026amp;P (jointly rated by S\u0026amp;P and other CRAs). Similarly, columns three and four report the default rates of DBRS-rated RMBS. The overall default rates between RMBS solely rated by S\u0026amp;P and those jointly rated by other CRAs are similar. Further, across the individual rating categories (except CCC rating), default rates do not substantially differ between single- and joint-rated RMBS. In aggregate, the empirical findings suggest that there is neither a systemic difference in the credit quality nor a significant variation in rating stringency/accuracy between the two groups.\u003c/p\u003e\n\u003cp\u003eIn contrast, the final two columns of Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e reveal stark differences in default rates between RMBS solely and jointly rated by DBRS, supporting the early findings of systematic differences between the two groups. The overall default rate of RMBS rated only by DBRS (0.23%) is significantly lower than that of issues jointly rated with other CRAs (30.57%). More strikingly, single-rated RMBS default rates are substantially lower than those of joint-rated issues in 5 of the 7 rating categories. Indeed, the default rate is zero for all rating categories of single-rated RMBS other than CC/C, which has a ten-year default rate of just 1.32%. In comparison, the default rate of CC/C rating for joint-rated RMBS is 38.12%. The sharp differences in default rates between the single- and joint-rated subsamples suggest that DBRS ratings on single-rated issues are even more stringent than those of joint-rated issues. Furthermore, the 1.32% default rate of RMBS rated C solely by DBRS is similar to the default rates of RMBS rated A or higher by S\u0026amp;P, suggesting that DBRS erroneously assigned extremely low ratings to many creditworthy single-rated issues and created a significant number of false alarms.\u003csup\u003e11\u003c/sup\u003e Overall, the empirical evidence indicates that DBRS ratings on both single- and joint-rated RMBS are more conservative and less accurate than S\u0026amp;P ratings.\u003c/p\u003e"},{"header":"V. Conclusion","content":"\u003cp\u003eUsing regulatory disclosure data, we compare rating conservatism, relative accuracy, and early warning versus false alarm trade-off of RMBS ratings by legacy CRAs and DBRS. We find that DBRS applies the most stringent rating standards, assigning lower ratings than its peers. However, conservatism does not equate to accuracy. DBRS\u0026rsquo;s rating stringency results in significantly higher rates of false alarms and, consequently, lower overall ability to discriminate between defaulting and surviving RMBS. In contrast, S\u0026amp;P ratings are the most generous yet achieve the highest discriminatory power.\u003c/p\u003e \u003cp\u003eAlthough inflated ratings were widely blamed for contributing to the 2008\u0026ndash;2009 financial crisis, and DBRS\u0026rsquo;s increased post-crisis market share suggests an ensuing demand for conservatism, our results show that conservatism does not necessarily produce more accurate ratings. This study underscores the importance of distinguishing between rating toughness and informativeness. Investors and regulators may prefer more stringent rating standards when the benefit of default detection is greater than the cost of false alarms. Yet, an effective rating system must enhance discrimination between good and bad credit risk, not merely appear more cautious. Instead of focusing solely on rating standards, emphasis should be placed on achieving an optimal balance between early warning provision and false alarm avoidance to enhance rating informativeness.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eG.N. and L.Z contributed equally to the work.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eWe use two regulatory disclosure data sources. The first is the 2018 and 2020 NRSRO Forms, retrieved from the SEC\u0026rsquo;s Edgar platform. The second data source is the SEC Rule 17g-7 data (January 2024 updates), downloaded from the websites of the credit rating agencies.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAfik Z, Galil K (2025) Have ratings become more accurate? J Banking Finance 170:107337\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlp A (2013) Structural shifts in credit rating standards. J Finance 68(6):2435\u0026ndash;2470\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaghai RP, Servaes H, Tamayo A (2014) Have rating agencies become more conservative? Implications for capital structure and debt pricing. J Finance 69(5):1961\u0026ndash;2005\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBamber D (1975) The area above the ordinal dominance graph and the area below the receiver operating characteristic graph. 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J Financ Econ 106(2):308\u0026ndash;330\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEngelmann B, Hayden E, Tasche D (2003a) Testing rating accuracy. Risk 16(1):82\u0026ndash;86\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEngelmann B, Hayden E, Tasche D (2003b) Measuring the discriminative power of rating systems (No. 2003, 01). \u003cem\u003eDeutsche Bundesbank Discussion Paper Series 2: Banking and Financial Supervision.\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlynn S, Ghent A (2018) Competition and credit ratings after the fall. Manage Sci 64(4):1672\u0026ndash;1692\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHamerle A, Rauhmeier R, R\u0026ouml;sch D (2003) Uses and misuses of measures for credit rating accuracy. Available SSRN \u003cem\u003e2354877\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHung M, Kraft P, Wang S, Yu G (2022) Market power and credit rating standards: Global evidence. J Account Econ 73(2\u0026ndash;3):101474\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrystyniak K, Staneva V (2024) The myth of tightening credit rating standards in the market for corporate debt. J Banking Finance 162:107122\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLivingston M, Nicolosi G, Zhou L (2021) A Bird\u0026rsquo;s-eye view of the US credit rating industry. J Fixed Income 31(2):68\u0026ndash;99\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu J, Nicolosi G, Zhou L (2023) Regulation of the US credit rating industry and regulatory data disclosures. Law Financial Markets Rev 17(2):71\u0026ndash;87\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLucchetti A, Ng S (2007) How rating firms\u0026rsquo; calls fueled subprime mess. \u003cem\u003eThe Wall Street Journal\u003c/em\u003e, August 15, 2007\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOpp CC, Opp MM, Harris M (2013) Rating agencies in the face of regulation. J Financ Econ 108(1):46\u0026ndash;61\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQin N, Zhou L (2025) Are investor-paid credit ratings superior? Financ Manage 54(1):53\u0026ndash;87\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSkreta V, Veldkamp L (2009) Ratings shopping and asset complexity: A theory of ratings inflation. J Monet Econ 56(5):678\u0026ndash;695\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnited States Financial Crisis Inquiry Commission (2011) The Financial Crisis Inquiry Report: Final Report of the National Commission on the Causes of the Financial and Economic Crisis in the United States. Government Printing Office\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhite LJ (2009) The credit-rating agencies and the subprime debacle. Crit Rev 21(2\u0026ndash;3):389\u0026ndash;399\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXia H (2014) Can investor-paid credit rating agencies improve the information quality of issuer-paid rating agencies? J Financ Econ 111(2):450\u0026ndash;468\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e One study disputes the claim of increased rating conservatism (Krystyniak and Staneva, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The two ratios are often used to measure rating accuracy/quality (Cheng and Neamtiu, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Dimitrov et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hung et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Other prior studies use an alternative statistic, Accuracy Ratio or Gini Index, to measure ratings\u0026rsquo; discriminatory power of default risk (Cheng and Neamtiu, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Cornaggia et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Livingston et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Engelmann et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2003a\u003c/span\u003e) show that the AUC statistic and the Accuracy Ratio are mathematically equivalent, with AUC = (1\u0026thinsp;+\u0026thinsp;Accuracy Ratio)/2. We use the AUC statistic because its statistical properties and estimation techniques have been developed by previous literature (Bamber, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1975\u003c/span\u003e; DeLong et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Engelmann et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2003b\u003c/span\u003e), allowing us to test for the statistical significance of the differences in AUC statistics between two rating systems.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e We focus on letter grade ratings because most RMBS ratings do not have plus or minus modifiers. For example, 99% (69%) of Fitch (S\u0026amp;P) sample ratings do not have plus or minus modifiers.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e This rating category does not include Moody\u0026rsquo;s C rating, which effectively indicates default status. See the earlier discussion.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e We observe similar patterns from one- and three-year HR and FAR statistics, which are available upon request.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The probit models include deal fixed effects. The deal information, obtained from the Bloomberg, was unavailable for three RMBS jointly rated by DBRS and legacy CRAs, resulting in slight differences in the numbers of observations used in the probit models from the joint-rated RMBS numbers reported in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Results based on models without deal fixed effects are qualitatively similar.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e For RMBS whose DBRS ratings were withdrawn for non-payoff or default reasons, we use its last rating before the rating withdrawal to determine its status.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Only a small percentage of RMBS are solely rated by Moody\u0026rsquo;s or Fitch. As a result, we do not analyze those issues separately.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Results based on one- and three-year default rates are similar. They are available upon request.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e It might seem puzzling that issuers of those RMBS did not seek an additional, potentially higher, rating given DBRS\u0026rsquo;s extremely pessimistic assessment. A likely explanation relates to the nature of our sample: outstanding ratings rather than new rating initiations. These issues may have received a high DBRS rating at initial issuance, only to be unjustifiably downgraded to CC/C rating during or after the financial crisis. Since RMBS issuers have less incentive to pay for additional ratings on bonds already issued and outstanding, they likely chose not to pursue a second opinion.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eRating Conservatism: Rating Standard vs. Rating Accuracy\u003c/b\u003e This Table presents the performance of a hypothetical binary rating system (Default Rating\u0026thinsp;=\u0026thinsp;0 and Non-default Rating\u0026thinsp;=\u0026thinsp;1) on a hypothetical pool of high-risk and low-risk bond issues. The details of the hypothetical example are presented in Section I. The rating performance statistics (HR, FAR, and AUC Statistics) under three different rating standards (Generous, Moderate, and Stringent standards) are compared with those of a Refined Rating system with more accurate default probability estimation.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenerous\u003c/p\u003e \u003cp\u003eRating\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate Rating\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStringent\u003c/p\u003e \u003cp\u003eRating\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRefined\u003c/p\u003e \u003cp\u003eRating\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage Rating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDefault Rate of Non-default Rating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.33%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDefault Rate of\u003c/p\u003e \u003cp\u003eDefault Rating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.67%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHit Rate\u003c/p\u003e \u003cp\u003e(HR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFalse Alarm Rate (FAR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4412\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUC Statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6961\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eDistributions of Rated RMBS in 2013\u003c/b\u003e This Table reports distributions of RMBS rated by CRAs on January 1, 2013. Column 1 reports the numbers of RMBS rated by CRAs. Columns 2 and 3 report the numbers of RMBS rated solely by one CRA and jointly rated by other CRAs, respectively. Columns 4 to 7 report the numbers of RMBS jointly rated by pairs of CRAs.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRAs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. of RMBS Rated\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRated Solely by the CRA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eJoint-Rated by Other CRAs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eJoint-Rated\u003c/p\u003e \u003cp\u003eby S\u0026amp;P\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJoint-Rated\u003c/p\u003e \u003cp\u003eby Moody\u0026rsquo;s\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJoint-Rated\u003c/p\u003e \u003cp\u003eby Fitch\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eJoint-Rated by DBRS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS\u0026amp;P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33,262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11,636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21,626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10,063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12,695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1,393\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoody\u0026rsquo;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16,433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13,623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10,063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5,141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e310\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFitch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18,407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16,283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12,695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5,141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e873\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBRS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44,451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19,209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25,242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eRMBS Ratings and Rating Changes During the 2008\u0026ndash;2009 Financial Crisis\u003c/b\u003e This Table reports the RMBS rating distributions and changes during the 2008\u0026ndash;2009 financial crisis by CRAs. Panel A reports the average outstanding RMBS ratings at the end of 2007 and 2009 by CRAs. The alphanumeric rating symbols are converted to a numerical rating variable as follows: \u003cem\u003eRATING\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1 for AAA (Aaa), 2 for AA (Aa), up to 8 for CC/C rated RMBS. Parenthetical values indicate the number of RMBS ratings. Panel B reports the percentage of RMBS in the top two rating categories (AAA/AA) and the bottom rating category (CC/C) at the end of 2007 and 2009.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS\u0026amp;P\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMoody\u0026rsquo;s\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFitch\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDBRS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePanel A. Average Outstanding RMBS Ratings\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12/31/2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.25 (85,420)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.25 (62,918)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.19 (49,179)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.62 (4,821)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12/31/2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.39 (67,515)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.75 (47,769)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.85 (43,884)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.26 (5,600)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePanel B. Percentages of Top and Bottom Rating Categories\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% of AAA/AA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12/31/2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.65%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68.92%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55.11%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12/31/2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.51%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.26%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.71%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% of CC/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12/31/2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.91%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.34%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.24%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12/31/2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.55%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.66%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003ePairwise Comparisons of Outstanding Ratings on Joint-Rated RMBS in 2013.\u003c/b\u003e This Table reports the average outstanding ratings on RMBS jointly rated by pairs of CRAs. Alphanumerical ratings are converted to a numerical scale: \u003cem\u003eRATING\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1 for AAA (Aaa), 2 for AA (Aa), up to 8 for CC/C rated RMBS. The first two columns list the CRA names in the pair, and the third column reports the number of joint-rated RMBS. Columns 4 and 5 report the mean \u003cem\u003eRATING\u003c/em\u003e from the CRAs, and column 6 reports the mean rating difference. The last five columns show the percentages of RMBS with the two ratings differing by multiple letter grades, one letter grade, or the same.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRA\u003c/p\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCRA\u003c/p\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of RMBS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean A Rating\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean B\u003c/p\u003e \u003cp\u003eRating\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA - B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCRA A Multi-Letter Lower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCRA A One-Letter Lower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSame Rating\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCRA A One-Letter Higher\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eCRA A Multi-Letter Higher\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePanel A. Pairwise Comparisons of Legacy CRAs\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS\u0026amp;P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMoody\u0026rsquo;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.70***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.62%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18.21%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e28.90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e22.38%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e26.91%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS\u0026amp;P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFitch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12,695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.86***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.34%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.74%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e35.53%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e32.42%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e23.96%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoody\u0026rsquo;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFitch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.34***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13.03%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e27.74%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e39.37%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e11.75%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePanel B. Pairwise Comparisons of DBRS with Legacy CRAs\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS\u0026amp;P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDBRS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.25***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e25.13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e44.08%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e28.36%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoody\u0026rsquo;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDBRS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.93***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.29%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17.74%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e55.16%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e20.97%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFitch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDBRS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.15***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.44%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.36%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e67.01%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14.78%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6.41%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*** indicates that the mean difference is statistically significant at the 1% level.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eAUC Statistics of RMBS Jointly Rated by Pairs of Legacy CRAs\u003c/b\u003e This Table reports the one-, three-, and ten-year Area Under the Curve (AUC) statistics of RMBS jointly rated by pairs of legacy CRAs. No. of Non-defaults (No. of Defaults) is the number of RMBS that survived (defaulted) within one-, three-, and ten-year horizons.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eS\u0026amp;P and Moody\u0026rsquo;s\u003c/p\u003e \u003cp\u003eJoint-rated\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eS\u0026amp;P and Fitch\u003c/p\u003e \u003cp\u003eJoint-rated\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eMoody\u0026rsquo;s and Fitch\u003c/p\u003e \u003cp\u003eJoint-rated\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS\u0026amp;P\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMoody\u0026rsquo;s\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS\u0026amp;P\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eFitch\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMoody\u0026rsquo;s\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eFitch\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePanel A. One-Year AUC Statistics\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUC Statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7993***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.7981***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.8111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.8565***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of Non-defaults\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e9,149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e11,342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e4,280\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of Defaults\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e1,353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e861\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePanel B. Three-Year AUC Statistics\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUC Statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8160***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.8209***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.8448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.9076**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of Non-defaults\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e8,252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e10,216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e3,686\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of Defaults\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1,811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e2,479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e1,455\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePanel C. Ten-Year AUC Statistics\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUC Statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8352***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.8802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.8142***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.8681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.9102***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of Non-defaults\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e7,190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e8,924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e3,081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of Defaults\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2,873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e3,771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e2,060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e***, **, * indicate that the difference in AUC statistics between the two CRAs is statistically significant at the 1%, 5%, or 10% levels, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eAUC Statistics of RMBS Jointly Rated by DBRS and Legacy CRAs\u003c/b\u003e This Table reports the one-, three-, and ten-year Area Under the Curve (AUC) statistics of RMBS jointly rated by DBRS and legacy CRAs. No. of Non-defaults (No. of Defaults) is the number of RMBS that survived (defaulted) within one-, three-, and ten-year horizons.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eS\u0026amp;P and DBRS\u003c/p\u003e \u003cp\u003eJoint-rated\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eMoody\u0026rsquo;s and DBRS\u003c/p\u003e \u003cp\u003eJoint-rated\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eFitch and DBRS\u003c/p\u003e \u003cp\u003eJoint-rated\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eS\u0026amp;P\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDBRS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMoody\u0026rsquo;s\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eDBRS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eFitch\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eDBRS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePanel A. One-Year AUC Statistics\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUC Statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.8333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6153***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.5635***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.7226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.6223***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of Non-defaults\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e1,220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e728\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of Defaults\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePanel B. Three-Year AUC Statistics\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUC Statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.8345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6259***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.5903***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.7464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.6376***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of Non-defaults\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e1,108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e653\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of Defaults\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePanel C. Ten-Year AUC Statistics\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUC Statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.6442***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.7072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.6017***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.7520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.6454***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of Non-defaults\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e610\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of Defaults\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e263\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e***, **, * indicate that the difference in AUC statistics between the two CRAs is statistically significant at the 1%, 5%, or 10% levels, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eTen-Year Hit Rates and False Alarm Rates of RMBS Jointly Rated by Legacy CRAs\u003c/b\u003e This Table reports the ten-year Hit Rate (HR) and False Alarm Rate (FAR) of joint-rated RMBS by each legacy CRA\u0026rsquo;s rating category at the beginning of 2013. HR is defined as the number of RMBS rated at or below the rating category that defaulted within ten years as a fraction of the total number of defaults. FAR is defined as the number of RMBS rated at or below the rating category that did not default within ten years as a fraction of the total number of non-defaulting RMBS.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eS\u0026amp;P\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMoody\u0026rsquo;s\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eS\u0026amp;P\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eFitch\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003eMoody\u0026rsquo;s\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003eFitch\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRating\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eFAR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eHR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eFAR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eHR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eFAR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eHR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eFAR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003eHR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cem\u003eFAR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003eHR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cem\u003eFAR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAAA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.836\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBBB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.724\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.559\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCCC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCC/C\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eTen-Year Hit Rates and False Alarm Rates of RMBS Jointly Rated by Legacy CRAs and DBRS\u003c/b\u003e This Table reports the ten-year Hit Rate (HR) and False Alarm Rate (FAR) of joint-rated RMBS by each CRA\u0026rsquo;s rating category at the beginning of 2013. HR is defined as the number of RMBS rated at or below the rating category that defaulted within ten years as a fraction of the total number of defaults. FAR is defined as the number of RMBS rated at or below the rating category that did not default within ten years as a fraction of the total number of non-defaulting RMBS.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eS\u0026amp;P\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eDBRS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eMoody\u0026rsquo;s\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eDBRS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003eFitch\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003eDBRS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRating\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eFAR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eHR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eFAR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eHR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eFAR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eHR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eFAR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003eHR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cem\u003eFAR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003eHR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cem\u003eFAR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAAA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.959\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBBB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCCC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCC/C\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eMarginal Predictive Power for Default of Joint-rated RMBS\u003c/b\u003e This Table reports the results from three probit regressions of ten-year defaults of joint-rated RMBS. The sample includes RMBS jointly rated by DBRS and one legacy CRA at the beginning of 2013. The dependent variable, Default, equals 1 for RMBS that defaulted between 2013 and 2022, and 0 otherwise. The explanatory variables are the 2013 numerical \u003cem\u003eRATING\u003c/em\u003e (=\u0026thinsp;1 for AAA, 2 for AA, up to 8 for CC/C rated RMBS) by DBRS and the legacy CRAs.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS\u0026amp;P and DBRS\u003c/p\u003e \u003cp\u003eJoint-rated\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMoody\u0026rsquo;s and DBRS\u003c/p\u003e \u003cp\u003eJoint-rated\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFitch and DBRS\u003c/p\u003e \u003cp\u003eJoint-rated\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-10.814***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-20.797***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-16.289***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBRS Ratings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.595***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS\u0026amp;P Ratings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.116***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoody\u0026rsquo;s Ratings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.022***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFitch Ratings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.111***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeal Fixed Effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. Obs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e873\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*** indicates that the coefficient is statistically significant at the 1% level.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eOutcomes of CC/C-Rated RMBS by DBRS\u003c/b\u003e This Table reports the ten-year outcomes of RMBS that were rated CC/C by DBRS at the beginning of 2013. Panel A reports the percentages of RMBS with five different outcomes: Defaulted, Remained CC/C Rated, Upgraded to Other Junk Ratings, Upgraded to Investment Grade Ratings, or Paid-off. Panels B to D report the outcomes of those RMBS jointly rated by DBRS and legacy CRAs, breaking down the outcomes in the rightmost columns by the legacy CRAs\u0026rsquo; 2013 ratings.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePanel A. All CC/C-Rated RMBS by DBRS.\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDefaulted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003e28.14%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemain CC/C Rated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003e40.99%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpgraded to Other Junk Ratings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003e6.54%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpgraded to Investment Grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003e6.54%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaid-off\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003e17.79%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of RMBS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003e1,681\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePanel B. Jointly rated by S\u0026amp;P.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAll\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eCC/C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eCCC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026gt; B\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDefaulted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.21%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.01%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.82%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.27%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.26%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemained CC/C Rated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.63%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.68%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.40%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpgraded to Other Junk Ratings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.73%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.73%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.21%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.58%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpgraded to Investment Grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.94%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.41%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.22%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.30%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaid-off\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.71%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.46%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.59%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53.46%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of RMBS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e159\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePanel C. Jointly Rated by Moody\u0026rsquo;s\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAll\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eCC/C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eCCC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026gt; B\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDefaulted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.27%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.71%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.33%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemain CC/C Rated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.19%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.45%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.67%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpgraded to Other Junk Ratings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.16%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.71%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.36%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.93%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.67%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpgraded to Investment Grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.16%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.71%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.38%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.036%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaid-off\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.71%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.71%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.69%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e56.67%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber RMBS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePanel D. Jointly Rated by Fitch\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAll\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eCC/C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eCCC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026gt; B\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDefaulted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.83%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.35%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.25%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemain CC/C Rated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.65%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.55%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.38%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.45%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.50%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpgraded Other Junk Ratings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.57%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.82%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.51%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.69%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpgraded to Investment Grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.26%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.48%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaid-off\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.86%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.48%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.25%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of RMBS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eTen-Year Default Rates of Single-Rated vs. Joint-Rated RMBS\u003c/b\u003e This Table reports S\u0026amp;P and DBRS ten-year default rates by letter rating at the beginning of 2013. Single-rated RMBS are rated by S\u0026amp;P or DBRS only. Joint-rated RMBS are rated by S\u0026amp;P (DBRS) and at least one other CRA.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eS\u0026amp;P Ratings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eDBRS Ratings\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSingle-Rated\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJoint-Rated\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" 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"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8595537/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8595537/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe compare the levels and accuracies of residential mortgage-backed securities (RMBS) ratings from S\u0026amp;P, Moody’s, Fitch, and DBRS and find the following. While DBRS ratings are the most conservative, they have the lowest discriminatory power of default risk in short-, intermediate-, and long-term horizons. In addition, DBRS ratings have little or no incremental predictive power for default beyond that of other CRAs on joint-rated RMBS. In contrast, S\u0026amp;P ratings are the most generous yet have the highest discriminatory power of default risk. The superior performance of S\u0026amp;P ratings can be attributed to a balanced trade-off between early warnings of default and false alarms. The findings highlight the differences between conservative ratings and accurate ratings.\u003c/p\u003e","manuscriptTitle":"Are Conservative Ratings More Accurate? An Evaluation and Comparison of RMBS Ratings","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-27 15:42:31","doi":"10.21203/rs.3.rs-8595537/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e0093f66-c760-47ae-a0aa-def87f015aea","owner":[],"postedDate":"January 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T13:54:55+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-27 15:42:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8595537","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8595537","identity":"rs-8595537","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}