Six years of annual declines in the global structured finance default rate came to an end in 2019, with the headline figure unchanged from the year prior at 1.9% (see table 1). This was the second year the default rate has held under 2% since the 2009 financial crisis.

S&P Global Ratings had 32,326 ratings outstanding on global structured finance securities at the start of 2019. The sector's global average change in credit quality (see definition in Appendix I) declined but remained positive at +0.04 notches (see chart 1). The upgrade rate (the proportion of upgrades among all ratings outstanding) fell to 7.2% from a historical high of 13.1% the year before, while remaining well above the one-year average of 3.7%. The downgrade rate (measured as the proportion of downgrades, including defaults, among all ratings outstanding) was the lowest recorded since 2006, at 4.7% in 2019, well below the one-year average of 16.4%.

The European commercial-backed securities sector (CMBS) sector exhibited the highest downgrade rate once again. The U.S. single-name synthetics and U.S. residential mortgage-backed securities (RMBS) sectors also exhibited higher downgrade rates. Upgrade rates fell across most sectors in 2019. Outside the U.S. and Europe, there were no defaults, and overall credit performance remained positive in 2019.

Credit Performance: The Default Rate Holds Steady At A Postcrisis Low After Six Years Of Declines

The overall credit performance of global structured finance securities we rate remained positive in 2019, despite some deterioration from the year prior. During the year, we raised 7.2% of our ratings on global structured finance securities that were active at the beginning of the year, down from the historical high of 13.1% in 2018. The annual default rate in 2019 held at the postcrisis low, at 1.9%, following six years of declines, while downgrades fell overall.

The average change in credit quality for global structured finance was +0.04 rating notches at the end of 2019, down from a record high of +0.25 rating notches in 2018. This measure had been negative for several years since mid-2007--indicating that, on average, ratings were drifting lower--up until early 2016, when it turned positive. Once in positive territory, the average change in credit quality moved above +0.2 notches for the first time in August 2018 (see chart 1).

The 12-month trailing default rate of 1.9% in 2019 was virtually unchanged from the previous year. On a 12-month trailing basis, the default rate has been on a broad downtrend since mid-2016 (see chart 2).

The 12-month trailing default rate for investment-grade ratings ('BBB-' or higher) rose to 0.06% in 2019 from 0.01% in 2018. The speculative-grade ('BB+' or lower) 12-month trailing default rate was 5.2%, up from 4.5% in the year prior.The default rates for both investment- and speculative-grade structured finance securities have stabilized since their peaks in 2009 and resembled precrisis levels in 2019.

Region and sector comparisons

Structured finance credit performance in 2019 diverged significantly between sectors and regions. Globally, the European CMBS sector had the highest downgrade rate, at 9.0%, followed by the U.S. single-name synthetics sector at 8.7%, both of which held well below their respective one-year average downgrade rates (see chart 3). In fact, downgrade rates across all sectors held below their one-year averages. Upgrades were the most prominent in the European RMBS sector, with an upgrade rate of 37.6% in 2019, well above the one-year average of 8.22%. Upgrade rates in 2019 held above their respective one-year averages for all region-sector combinations.

The European CMBS sector also had the highest annual default rate in 2019, at 4.0%, well above its one-year average (see chart 4). Following European CMBS, annual default rates in 2019 were the highest in U.S. RMBS and U.S. CMBS, at 3.0% and 2.4%, respectively. There were no defaults among European asset-backed securities (ABS), U.S. single-name synthetics, Australian securities, Japanese securities, or securities outside the major regions. Aside from European CMBS, European structured credit was the only sector globally with a default rate in 2019 above its one-year average.

Table 1 shows a summary of 2019 credit performance for global structured finance, segmented in various ways, compared with the 1976-2019 one-year weighted average statistics.

By region, Europe had the highest upgrade rate in 2019, at 20.2%, well above its one-year average of 7.9% (see table 1). Upgrades were also prominent across Latin America and other emerging markets in 2019. The downgrade rate was the highest in the U.S., at 5.1%, holding below its one-year average. Conditions in Australasia deteriorated somewhat, with the upgrade rate falling to 5.3% and the downgrade rate rising to 4.7%. Japan's upgrade and downgrade rates were equivalent, at 0.27% each. For upgrades, this represented a decline from the year prior, while the downgrade rate increased from zero over the same period.

The average change in credit quality in regions outside the U.S., Europe, Australasia, and Japan broadly declined. These other regions reported an upgrade rate of 8.33%, an improvement from the year prior, while the downgrade rate improved to 0.46%. There were no defaults outside the U.S. and Europe in 2019 among nearly 1,636 ratings outstanding at the start of the year.

By sector, ABS had the highest upgrade rate globally in 2019, at 12.6%, well above its one-year average of 4.5%. The RMBS upgrade rate was also higher than its one-year average. Downgrades were the most prominent in the single-name synthetics sector, with a downgrade rate of 7.5% in 2019, followed by RMBS at 5.8%, still below the one-year average of 17.9%. Sovereign upgrades in Europe, which ultimately resulted in several RMBS upgrades in 2019, were offset by RMBS downgrades in regions outside of Europe. Further, downgrades continued to be prominent in CMBS, which had the second-highest downgrade rate of 2019, at 5.2%, below its one-year average.

Default rates across global sectors were all below their one-year averages in 2019. The highest default rate was recorded by RMBS, at 2.6%, followed closely by CMBS at 2.5%.

The region-sector combination with the highest upgrade rate in 2019 was European RMBS, where we raised 37.6% of the ratings that were active at the beginning of the year, partly due to country risk considerations and corresponding upgrades for related sovereigns, specifically Portugal and Spain. U.S. ABS upgrades were also prominent, with an upgrade rate of 12.7%, mainly attributed to elevated upgrades in the auto subsector, which accounts for one of the largest shares of U.S. ABS securities. Upgrades rates across all region-sector combinations were higher than their respective one-year averages with the exception of U.S. CMBS, U.S. structured credit, European structured credit, and Japanese securities.

The region-sector combination with the highest downgrade rate in 2019 was European CMBS, at 9%, still well below its one-year average. Downgrades were also prominent in U.S. single-name synthetics. All region-sector combinations had downgrade rates below their respective one-year averages with the exception of Australasian securities.

European CMBS also had the highest default rate across the sector-region combinations, at 4%. Following European CMBS, default rates were the highest in U.S. RMBS and U.S. CMBS, at 3.0% and 2.4%, respectively. Broadly speaking, region-sector default rates remained below their one-year averages, with the exception of European CMBS.

Globally, the 2009 vintage recorded the highest upgrade rate, at 14.1% in 2019. Some of the more recent vintages, specifically 2016 and 2017, have reported higher upgrade rates compared with older vintages. Most vintages had upgrade rates well above their respective one-year averages in 2019. The downgrade rate for the 2008 vintage was the highest in 2019. Downgrade rates of most vintages held below their respective one-year averages. The default rate was the highest among 2006-2008 vintages. Default rates across most vintages in 2019 were above their respective one-year averages.

Across rating categories globally, the 'BBB' category showed particularly strong performance, with a 2019 upgrade rate of 15.3%, well above its one-year average of 8.6%. Additionally, upgrades were prominent in the 'A' and 'BB' rating categories, with respective upgrade rates of 14.6% and 11.5%. These rating categories also saw some of the lowest downgrade and default rates in 2019.

The overall investment-grade upgrade rate was 8.1% in 2019, well above the one-year average of 3.4%, while the downgrade rate of 2.5% was well below the one-year average of 11.1%. The investment-grade default rate was well below its one-year average at 0.1% in 2019. The only investment-grade defaults were recorded in the 'BBB' and 'AA' rating categories.

Downgrades were higher among lower ratings; specifically, the 'B' rating category had a downgrade rate of 9.2% in 2019, identical to the downgrade rate for the 'CCC' rating category. Defaults were highest in the 'CCC' and 'CC' rating categories, at 8.0% and 8.1%, respectively. The overall speculative-grade upgrade rate was 5.6% in 2019, while the downgrade rate was 8.6% and the default rate was 5.1%--all improved compared with their respective one-year averages.

Structured credit default rates have held steady over the past five years, rising slightly to 0.68% in 2019. The upgrade rate for the sector has been steadily declining, falling to a 10-year low of 3.5% in 2019. The downgrade rate fell to the lowest since 1996 at 1%.

Table 1

Vintages

Structured finance credit performance continues to be differentiated by the year of issuance--or vintage--of securities. The fundamental credit characteristics and economic conditions surrounding a transaction's vintage can influence its subsequent credit behavior.

Segmenting global structured finance credit performance by transaction vintage reveals that the upgrade rates in 2019 among most vintages underperformed compared with the previous year. The highest upgrade rate was that of the 2009 vintage, at 14.1%. Only five vintages in 2019 displayed improved upgrade rates compared with the year prior.

For the 2008 vintage, the downgrade rate deteriorated to 15.2% in 2019 from 10.9% the year before. The downgrade rates for the 2006-2007 vintages have fallen into the single digits at 6.2% and 7.1%, respectively, following the weak credit performance exhibited by these transactions during the 2009 financial crisis (chart 5).

However, these same vintages continue to record some of the highest default rates. In 2019, the 2009 vintage reported the highest default rate, at 6.7%, well above its one-year average of 4.4%. The 2005-2007 vintages exhibit the highest one-year average default rates, given their performance during the 2009 financial crisis, but default rates for these vintages in 2019 were much lower and on par with those of other vintages (see chart 6). Nonetheless, most vintages recorded defaults rates higher than their respective one-year averages.

Ratings Performance: Ratings Have Historically Differentiated Default Rates

Our structured finance ratings express our opinions of securities' creditworthiness, which center on an assessment of default likelihood, rather than the likelihood of upgrade or downgrade. That said, our ratings also consider credit stability as a secondary factor.

Across global structured finance, upgrade and downgrade rates in 2019 were generally differentiated by the ratings at the beginning of the year. Downgrade rates were generally higher for lower rating categories, ranging from 9.2% for securities rated 'B' and 'CCC' to just 0.4% for securities rated 'AAA' (see chart 7). Downgrade rates across all structured finance securities were well below their one-year averages in every rating category. Upgrade rates were generally elevated for higher rating categories. Securities rated 'BBB' recorded the highest upgrade rate in 2019, at 15.3%.

We typically expect default rates to be lower for higher-rated securities, provided that observations are made over sufficiently long time horizons and large samples. Over shorter time frames or smaller samples, this relationship may not always hold, and default rates can also vary over time.

For global structured finance, higher-rated securities were clearly associated with lower weighted average one-year default rates between 1976 and 2019 (see chart 8). Default rates in 2019 were generally higher at lower rating categories. Each rating category witnessed at least two defaults in 2019, with the exception of the 'AAA' rating category, which had no defaults. Securities we rated in the 'AA' category at the beginning of the year had a default rate of 0.1%, contrasting with a default rate of 1.5% for those in the 'BB' category.

To further explore how well our structured finance ratings have rank-ordered creditworthiness over time, we can calculate cumulative default rates by rating category for various time horizons, as we do for our corporate default and transition studies. This approach constructs average multiyear cumulative default rates by first aggregating across static pools the period-to-period one-year marginal default rates that are conditional on the securities' survival (that is, nondefault) in the prior one-year period (see Appendix I for more details). We note that this approach differs from the calculations we use in constructing the weighted average multiyear default and transition rates elsewhere in this study, where default rates are not conditional on survival.

The cumulative default rates (conditional on survival) based on our full data history--from the beginning of 1976 to the end of 2019--show that lower ratings have generally been associated with higher default rates and vice versa (see table 2). In other words, ratings appear to have successfully rank-ordered creditworthiness over each time horizon.

Table 2

Default rates calculated on a rolling basis for a given horizon (for example, five years) further demonstrate that this ratings-based rank ordering has generally remained in place over time, even as absolute default rates have fluctuated with differing levels of credit stress. For example, the five-year trailing 'AAA' default rate for global structured finance was only 0.03% at the end of 2019, unchanged from 2018 and continuing an improving trend since the last peak in 2012. By contrast, the five-year trailing 'BBB' default rate was 1.2%, marginally down from 1.3% at the end of 2018 and well off the peak of 48.8% in 2012 (see chart 9).

We also calculate Gini coefficients to quantify the rank-ordering power of our ratings in relation to securities' observed default propensity. The Gini coefficient is a summary metric that can range from zero to 100%--the closer to 100%, the higher the rank-ordering power of the ratings under observation (see Appendix I for more details).

In 2019, the one-year Gini coefficient for defaults of rated global structured finance securities was 76.5%, down from 79.3% the previous year. The three-year Gini coefficient was lower as well, coming in at 77.6% for 2019, compared with 79% in 2018. The five-year Gini coefficient experienced its fourth consecutive year of declines in 2019, falling to 79.3% (see chart 10).

This follows the pronounced dip in Gini coefficients several years ago, amid the 2009 financial crisis. During this period, a relatively large number of defaults in certain sectors--such as U.S. RMBS and U.S. structured credit--coincided with fewer defaults among lower-rated securities from other sectors, lowering the Gini coefficients.

Finally, we also calculated sector-specific historical one-year Gini coefficients. For most sectors, the Gini coefficients have returned to 80%-100% since the end of 2011, following a drop in 2007 (see chart 11). The Gini coefficient for global RMBS remains lower, at about 66%, slightly down from 70% in the previous year. Although about 95% of the global RMBS defaults in 2019 were from the 'CCC' and 'CC' rating categories, some defaults still came from higher ratings, as high as the 'AA' category, lowering the Gini coefficient for this sector significantly. With the exception of RMBS, each sector reported an increase in the one-year Gini coefficient.

The Gini coefficient metric is useful but has some significant shortcomings. For example, a value of 100% would be attained only if all defaults occurred among securities rated below some critical rating threshold, with no defaults among securities rated higher than this threshold. However, in a diverse sample of rated securities--such as global structured finance securities--sector- and region-specific credit stresses of differing severity and likelihood may cause higher-rated securities in more stressed sectors or regions to default, while at the same time lower-rated securities in less stressed sectors or regions do not default. This effect would lower the reported Gini coefficient, despite reflecting the sample composition rather than a decline in ratings' accuracy.

Regions And Sectors In Detail

U.S. RMBS

In 2019, the overall average change in credit quality for U.S. RMBS ended the year in negative territory, at -0.07 notches, following a run in positive territory throughout most of 2018 and into the first half of 2019. The measure became negative once more in July, after ending 2018 at +0.3 notches (chart 12). On a 12-month trailing basis, the U.S. RMBS default rate rose slightly, to 3.1%, from 2.8% in 2018 (see chart 13).

Of the 16,837 securities outstanding within the U.S. RMBS sector in 2019, the annual default rate was 3.1%, representing 505 securities (see table 3).

The subprime subsector, which accounts for the largest share of U.S. RMBS securities, at 33%, had one of the lowest default rates in the sector during the year, at 2.1%. The subprime downgrade rate markedly improved to 6.1% in 2019 from 18.7% in 2018. However, the upgrade rate in the subsector--which was about the same as the downgrade rate in 2018, at 18.7%--fell to 5.2% at the end of 2019, far more in line with its one-year average of 2.1%.

For the prime subsector, which accounts for the second-largest portion of U.S. RMBS, the default rate improved to 2.4% in 2019 from 3.4% the year before. Downgrade activity improved as well, with the downgrade rate falling to 5.9%--still one of the highest rates among RMBS subsectors--from 8.5% the year before. The upgrade rate declined to 2.2% from a historical high of 12.9% in the year prior.

Among the remaining subsectors in U.S. RMBS, overall credit quality improved. Default rates for the reperforming and closed-end second-lien segments were above their one-year averages, at 7.3% and 22.2%, respectively. These subsectors represent just over 3% of all active ratings in U.S. RMBS.

Credit performance was mixed across transaction vintages in U.S. RMBS. Precrisis vintages, specifically those from 2001 through 2007, have exhibited generally weaker fundamentals. However, in 2019, some of the lower-performing vintages showed stabilizing downgrade rates. The 2006 and 2007 vintages saw their respective downgrade rates improve to 6% and 6.4% from 30% and 34.5%. Downgrade rates across most pre-2007 vintages also improved from the year prior. Default rates improved in almost every pre-2007 vintage, with the exception of 2004, 2006, and 2007. Nonetheless, in 2019, defaults came from postcrisis vintages, specifically 2011, whose default rate was 7.7%. In 2018, there were no defaults in vintages following 2009.

The vast majority (95%) of U.S. RMBS defaults in 2019 were from the 'CCC' and 'CC' rating categories, which constituted about 38% of our U.S. RMBS ratings at the beginning of 2019. Defaults rates increased in many of the rating categories in 2019, though all remained well below the one-year averages.

Table 3

U.S. structured credit

The overall change in credit quality for U.S. structured credit was unchanged at +0.05 notches in the final month of 2019. Average credit quality for the sector has remained positive since October 2012 (chart 14). The U.S. structured credit 12-month trailing default rate fell to its lowest year-end level since its most recent peak in 2011 at 0.5% (see chart 15).

U.S. structured credit's headline annual default rate declined in 2019 to 0.4% at year-end (see table 4). The downgrade rate increased to 1.6%, still well below the one-year average downgrade rate of 18.2%. The upgrade rate also increased in 2019, to 3.8%.

The U.S. structured credit default rate spiked in 2011 following the defaults of a large number of ratings on collateralized debt obligation (CDO) tranches backed by other structured finance securities, notably U.S. subprime RMBS. At that time, these securities had, in our view, minimal realistic prospects of repayment, and many were unlikely to receive any future principal payments due to the ongoing collateral deterioration of the RMBS and other securities backing the transactions.

The overall U.S. structured credit sector remains polarized between collateralized loan obligations (CLOs), which have historically performed very well, and other transactions that performed poorly after 2007 but now constitute only a small portion of our active ratings. CLOs have grown to more than 90.6% of the U.S. structured credit sector at the end of 2019 (see table 4). The subsector saw some deterioration in 2019 as the downgrade rate rose to 1.3%, the highest since the 2009 financial crisis, from 0.7% in 2018. There were also two CLO defaults last year--compared with none in 2018--yielding an annual default rate of 0.1%, equal to the one-year average for the subsector. The upgrade rate increased to 3.5% from 3.0%, offsetting some of the negative movement in ratings.

Structured finance CDOs make up just over 2.0% of overall ratings and had the highest default rate in 2019, at 15.0%, above the one-year average of 13.0%. Downgrades were also the highest in this sector, at 17.5%, while there were no upgrades.

Considering only vintages with more than 30 ratings outstanding at the beginning of 2019, the highest upgrade rate was in the 2013 vintage, where CLO transactions in particular are now amortizing, increasing the relative credit enhancement for the rated securities and therefore raising their creditworthiness, all else being equal. Downgrades were most prominent in the 2014 vintage, with a downgrade rate of 25.8%.

All 14 of the defaults in U.S. structured credit in 2019 were on securities that we rated in the 'CCC' or 'CC' categories at the beginning of the year.

Table 4

U.S. ABS

Credit performance for the U.S. ABS sector remained positive in 2019. The annual average change in credit quality for 2019 fell to +0.25 notches after reaching a high of +0.36 notches in 2018 (see chart 16). The 12-month trailing default rate has historically remained very low, declining to 0.1% in 2019 (see chart 17).

The U.S. ABS sector is the second-largest structured finance sector in the U.S. by issuer count--behind U.S. RMBS--accounting for 17.2% of U.S. securities at the start of 2019. The majority of these securities fall within the student loan and auto subsectors (see table 5). The upgrade rate was 12.7% in 2019, well above the one-year average of 4.6%. The downgrade rate increased to 2.9% in 2019, but remains well below the one-year average of 5.5%. The annual default rate for ABS was 0.1% in 2019, unchanged from 2018 and below its one-year average of 0.4%.

The only U.S. ABS defaults in 2019 occurred in the manufactured housing and "other" subsectors, and all were speculative grade (see table 4). Three defaults came from manufactured housing, yielding an annual default rate of 2.4%, above its one-year average of 2.3%. The only other default occurred in the other subsector.

The majority of downgrades were on speculative-grade transactions. Further, most downgrades were isolated within the tobacco settlement subsector. At the close of 2018, Altria Group Inc. was downgraded following an investment in Juul, coupled with lower-than-expected settlement payments based on lower cigarette consumption. This prompted S&P Global Ratings to place 30 tobacco settlement bonds on CreditWatch with negative implications. In May 2019, we placed an additional 238 tobacco securitization bonds on CreditWatch negative, reflecting our view of future tobacco shipment volumes, a key input in our analysis of tobacco settlement bonds. Many of these bonds were ultimately downgraded in 2019, leading to the continued rise in the downgrade rate for the subsector.

Otherwise, in 2019, the downgrade rates of U.S. ABS subsectors remained well below their one-year averages. U.S. student loan ABS had the second-highest downgrade rate, at 3.4%, marking the fifth consecutive year of increases and the highest rate for the subsector since 2012. S&P Global Ratings published the criteria "U.S. FFELP Student Loan ABS: Methodology And Assumptions" on April 4, 2019, and a related guidance article, which shows how we apply the criteria to evaluate the liquidity risk in our outstanding FFELP transactions. As a result, various credits issued under the FFELP program were assigned the "under criteria observation" identifier, and the majority of these were ultimately downgraded.

The U.S. ABS auto subsector experienced a wave of upgrades in 2019, yielding the highest upgrade rate of all the U.S. ABS subsectors, at 30.2%, well above the one-year average of 11.6%. However, most subsectors experienced upgrade rates well below their one-year averages.

Table 5

U.S. CMBS

In U.S. CMBS, the average change in credit quality turned negative at the end of 2019 at -0.06 notches, compared with +0.05 notches at the end of 2018 (see chart 18). The 12-month trailing default rate for U.S. CMBS rose to 2.4% at the end of 2019, well above the global rate of 1.9% (see chart 19).

The annual default rate for U.S. CMBS rose to 2.4% in 2019 from 1.8% in 2018 (see table 6). The upgrade rate declined to 3.9%, well below the one-year average of 6.1%. The downgrade rate increased to 4.9% in 2019 but remained well below the one-year average of 12.1%.

A total of 58 defaults occurred within U.S. CMBS in 2019, the majority within the conduit/fusion subsector (see table 6), whose annual default rate rose to 5.3%, the highest in the past four years and well above the one-year average of 3.8%. The single-borrower subsector, which accounted for the largest share of outstanding U.S. CMBS ratings in 2019, had its third-highest annual default rate on record at 0.3%, above its one-year average of 0.1%. Defaults were primarily concentrated in the 2006-2008 vintages, and all were speculative grade.

Similarly, the conduit/fusion downgrade rate was among the highest, at 8.3%, and the single-borrower downgrade rate rose to 1.5%. The U.S. CMBS large loan/floaters subsector had the highest downgrade rate, at 9.3%. Downgrade rates across all U.S. CMBS subsectors held below their respective one-year averages. Further, downgrades primarily occurred in the 2004-2009 vintages, and the majority were speculative grade.

Upgrade rates within U.S. CMBS subsectors underperformed with respect to one-year averages, with the exception of U.S. CMBS "other." Following a peak in 2018, the conduit/fusion subsector's upgrade rate fell to just 4%. Similarly, upgrades within the single-borrower subsector have been declining for the past five years, to 3.7% in 2019, compared with a one-year average of 5.2%.

Table 6

Europe

Credit performance across European structured finance sectors has historically been more positive than that of U.S. counterparts, and the same held true in 2019. The average change in credit quality improved to its highest postcrisis level at +0.37 notches (see chart 20). The 12-month trailing default rate was relatively unchanged at 0.7% in 2019 (see chart 21).

The annual default rate for European structured finance was unchanged in 2019 at 0.7%, below the one-year average of 1% (see table 7). The overall upgrade rate rose to 20.2%, well above its one-year average of 7.9%, primarily because of the upgrade of several RMBS credits following the sovereign upgrades of Portugal and Spain. The overall downgrade rate for European structured finance was 1.6% in 2019, down from 2.3% in 2018 and well below the one-year average of 15.2%.

The European structured finance sector consists of a diverse array of subsectors. In terms of defaults, the structured credit subsector was the worst-performing in 2019. Specifically, the structured credit CDO segment had the highest annual default rate of the year, at 13.7%. There was just a single default across European RMBS, while the annual default rate rose in European CMBS to 4% in 2019. Once again, there were no defaults within European ABS. All defaults across European structured finance occurred among speculative-grade ratings.

Downgrades fell across all European sectors in 2019, and all downgrade rates were well below their one-year averages. As with defaults, downgrades were the most prominent in the European structured credit CDO subsector, which recorded a downgrade rate of 15.7% in 2019. European RMBS prime recorded a downgrade rate of 0.3%, and the downgrade rate for European CMBS fell to 9%. The only downgrades in European ABS were in the nontraditional subsector, which had a downgrade rate of 15.4% in 2019, an improvement from 23.9% in 2018. The majority of downgrades in European structured finance occurred within the speculative-grade segment, specifically among 'CCC' and lower ratings.

Almost every European subsector reported upgrade rates above their respective one-year averages, with the exception of the European structured credit subsectors. Upgrades were the most prominent within European RMBS, partly due to upgrades of European sovereigns in 2019, specifically Portugal and Spain. As a result, all subsectors within European RMBS had upgrades rates above 30% in 2019. Upgrades increased within European CMBS as well, where the upgrade rate rose to 7.3%. Within European ABS, upgrades were the highest within the consumer subsector, where the upgrade rate was 31% in 2019, well above the one-year average of 6.5%.

Further, upgrade rates were the highest among the pre-2010 vintages and were about equally spread between investment- and speculative-grade ratings.

Table 7

Outside the U.S. and Europe

Outside the U.S. and Europe, the average change in credit quality remained positive in 2019 at +0.04 notches (see chart 22). The 12-month trailing default rate outside of the U.S. and Europe has been zero for the past three years (see chart 23).

Structured finance ratings outside the U.S. and Europe accounted for less than 5% of outstanding ratings at the start of 2019. As there were no defaults in ratings outside the U.S. and Europe for the past three years, the annual default rate was 0.0% once again in 2019 (see table 8). The upgrade rate declined to 4.7% in 2019, below its one-year average of 5.5%. The downgrade rate increased to 3.2%, which is still well below its one-year average of 5.2%.

The majority of ratings outstanding at the start of the year were concentrated in Australasia and Japan, specifically within the RMBS sector.

Downgrades in 2019 were primarily concentrated in Australasia, where the downgrade rate for RMBS increased to 4.8%, above the one-year average of 3.2% (see table 8). Further downgrades occurred in the Japanese RMBS sector, with a downgrade rate of 0.3%, below its one-year average of 0.4%. Emerging markets were the only other region outside the U.S. and Europe to report downgrades, with a downgrade rate at 2.2%, well below the one-year average of 12.9%. All downgrades in 2019 outside the U.S. and Europe occurred within the investment-grade segment, specifically within the 'A' and 'BBB' rating categories.

The highest upgrade rate in 2019 was in Australasian ABS, at 22.2%, compared with a one-year average of 8.7%. In Japan, upgrades decreased broadly, with both the Japanese RMBS and "other" sectors reporting upgrades rates well below their respective one-year averages. Outside of Australia and Japan, emerging markets had the highest upgrade rate, at 17.8%, well above the one-year average of 12.3%. The majority of upgrades in 2019 outside the U.S. and Europe were concentrated among speculative-grade ratings.

Table 8

Appendix I: Terminology, Data Selection, And Calculation Approaches

This appendix details the data selection, calculations, and terminology we use in this report.

This global structured finance default and rating transition study uses our database of long-term global scale issue credit ratings. We do not include national and regional scale ratings, such as those we may assign in Argentina, Brazil, Mexico, Taiwan, and Japan, which are not comparable between countries. The analysis also excludes public information ("pi") ratings, issuer credit ratings, and short-term issue ratings, such as those on asset-backed commercial paper conduits.

Our ongoing enhancement of the database used to generate this study occasionally leads to changes in the reported statistics from one edition of the study to the next. However, each study includes statistics for previous years, ensuring that the study is self-consistent and effectively supersedes all previous editions.

Issues included in this study

This study analyzes the rating histories of 232,950 global structured finance instruments that S&P Global Ratings first rated between 1973 and Dec. 31, 2019. The term "structured finance" in this report refers to ABS, CMBS, RMBS, structured credit, and single-name synthetic transactions. For some analyses, we break down these sectors further into subsectors.

Sector definitions

ABS includes underlying collateral types such as credit card receivables, student loans, and auto loans and leases. The sector also includes manufactured housing, franchise loans, 12b-1 transactions, and corporate securitizations.

RMBS includes transactions backed by subprime mortgage loans, as well as home equity loan transactions and re-REMICS.

Structured credit includes CLOs, both cash and synthetic CDOs backed by exposures to corporate credit or other structured finance securities, and market-value CDOs and other leveraged funds. We also include transactions backed by loans to small and midsize enterprises in the structured credit sector.

CMBS also includes re-REMICS, as well as some CDOs primarily collateralized by commercial real estate loans. Single-name synthetic transactions are also referred to as "repackaged transactions" (or "repacks"), especially in Europe. The definition of a repack in this instance is an issue backed by a single credit, where the rating on the note is directly linked to that on the underlying credit.

In this study, we no longer include structured covered bonds, which we previously treated as a separate sector.

Regional definitions

This study presents rating transitions for global structured finance transactions. In some analyses, we segment default and transition statistics by region. In defining a transaction's region, we primarily use the location where we perform surveillance. We may also consider the transaction's issuer country (unless this is a tax haven such as the Cayman Islands) or its domicile of assets. In some cases, the domicile of assets may fail to provide additional insights into the region, especially when a transaction is backed by assets from more than one country.

In table 1 of the study, the "Asia (excluding Japan)" region includes transactions with collateral from Hong Kong, Singapore, Korea, and Taiwan, as well as other countries in the region. The "other emerging markets" region includes emerging market countries that are not included in other regions, such as Turkey and South Africa.

Vintage definition

In this report, we classify a security's vintage based on the date when we first assigned a rating. Usually this is close to the security's original issuance date. However, in some cases, we may first assign a rating to a security some time after closing.

Rating transitions

Our rating transition statistics use a "static pool" approach. To calculate the transition statistics over a given period (or "transition window"), we consider the static pool of ratings outstanding at the beginning of that period. The transition statistics for that static pool of ratings are then based on the movements in ratings between the start and end of the transition window.

For instance, we calculate the 2019 transition rates by determining the ratings on each security outstanding at the start of 2019 and determining the ratings on those same securities at the end of 2019. We then calculate statistics such as upgrade, downgrade, and stability rates, equivalent to the proportions of securities in the static pool with ratings that moved up, down, or remained the same, respectively, over the transition window. During this process, we count each security only once, even if the security experienced more than one rating change during the transition window being observed. In other words, we use the ratings at the start and end of the transition window to calculate the transition rates, disregarding any interim rating changes.

Rating modifiers

We use rating modifiers ("+" and "-") to calculate the upgrade, downgrade, and stability rates quoted in the text, tables, and charts throughout this study. However, the transition matrices in Appendix II of this report show only the less granular, full rating categories for practical reasons. In other words, we count transitions such as to 'AA+' from 'AA' as an upgrade and to 'BBB-' from 'BBB+' as a downgrade in the transition statistics we cite in this report. However, in the corresponding transition matrices, these transitions would appear in the cells corresponding to a stable rating category classification, such as 'AA' to 'AA' or 'BBB' to 'BBB'.

Rating discontinuance or withdrawal

We may discontinue ratings when, for example, a rated obligation's payments have been made in full in accordance with its terms or when a rated issue matures. Ratings may also be withdrawn because of a lack of sufficient information of satisfactory quality or at the issuer's request. In these cases, the rating may change to not rated (NR). When we withdraw or discontinue ratings within the transition window under consideration, we may either derive our reported statistics by classifying the rating transition as a move to NR (the "NR-included" approach) or--for some other analyses--we may classify the transition as a move to the last rating before withdrawal or discontinuance (the "NR-adjusted" approach).

In the text of this report, when we refer to upgrade and downgrade rates, for example, we use the latter approach. In the tables and charts, we clarify the approach used in the footnotes. We do not include a security with a withdrawn rating at the beginning of a transition window in the transition and default rate calculations for that period.

Treatment of 'AAA' ratings

In this study, we include the ratings history of all securities with original ratings of 'AAA', which we term the "uncollapsed" approach. This represents a change from editions of the study published before 2018. In these previous editions, for each structured finance transaction, we generally included only one of the securities that we originally rated 'AAA', terming this the "collapsed" approach. This was because historically, some transactions had a disproportionate number of 'AAA' rated tranches that were likely to exhibit similar or identical credit performance. By including only one such security per transaction, we aimed to avoid overweighting their behavior in aggregate performance statistics.

However, we now include all securities' rating histories, regardless of the original ratings, for completeness and consistency with some of our other reporting on ratings performance--for example, in regulatory submissions. The effects of altering the treatment of 'AAA' ratings on some example summary statistics are shown in Appendix III.

Treatment of 'D' ratings

Default counts and default rate statistics in this report are based on securities with ratings we lowered to 'D'. For the purposes of this report, when a rating has moved to 'D', we consider this a terminal state and do not, for example, include such a security in any transition windows that start on a subsequent date. In practice, however, some securities with ratings that have migrated to 'D' may later be assigned a different rating. This can occur, for example, if the defaulted security is subsequently restructured to different terms, such as a lower coupon. In these cases, we treat the security's postdefault ratings history as if it were for a new security, beginning from the date that the rating changed from 'D'. Where we segment statistics by vintage, however, we continue to base the vintage on the date we originally assigned a rating to the security.

Average change in credit quality calculation

Certain analyses in this study refer to the "average change in credit quality" of a set of structured finance securities over a given transition window. We define the average change in credit quality as the average number of notches by which ratings changed during the stated transition window, where we take the average across all ratings in the set under consideration (for example, a particular region or sector). In this averaging, we count downgrades as a negative number of notches, whereas we count upgrades as a positive number. We consider stable ratings to have undergone a transition of zero notches. We believe this measure acts as a useful summary of the credit performance of, for example, a given sector, since it combines the relative number of ratings undergoing transition with the severity of that transition.

Weighted average transition and default rate calculation

For weighted average transition rates (including default rates), we calculate the individual transition rates for different static pools. We then calculate a single averaged transition rate, weighted by the number of ratings in each static pool. We use this technique, for example, to determine the five-year weighted average transition rates, by analyzing the transition rates for different static pools over different five-year periods and then aggregating them.

Average cumulative default rate calculation

In this report, we also calculate and present average cumulative default rates for different time horizons. For example, table 2 shows that the one-year average cumulative default rate for 'A' rated global structured finance has been 0.43% and the three-year average has been 6.35%.

We first consider the static pool of ratings at the beginning of each calendar year. For each static pool, we calculate the marginal default rates for each calendar year after the static pool's formation. These one-year marginal default rates are "conditional on survival." For example, the marginal default rate for the third year is the number of defaults during the third year divided by the number of ratings from the static pool that had "survived" (that is, not moved to 'D') by the beginning of the third year. We then average the marginal default rates for each time horizon across static pools, weighting by the number of surviving ratings at the beginning of each time horizon, to give an average marginal default rate per time horizon, as well as average marginal survival rates (equal to one minus the average marginal default rate). Finally, the average cumulative default rate of each time horizon is calculated as one minus the product of marginal survival rates up to that time horizon.

We note that this approach differs from the calculations we use in constructing the weighted average multiyear default and transition rates included elsewhere in this study (and as described above), where default rates are not conditional on survival.

Gini coefficient calculation

We calculate Gini coefficients as one way to quantify our ratings' power to rank-order creditworthiness, based on observed default rates.

We first construct a Lorenz curve, which plots the cumulative proportion of all structured finance ratings per rating level on the x-axis, against the cumulative proportion of defaults per rating level on the y-axis (see chart 24). For both axes of the Lorenz curve, the observations are ordered from the low end of the ratings scale ('C') to the high end ('AAA').

If our structured finance ratings only randomly approximated default propensity, the Lorenz curve would fall along the diagonal, shown as the random curve in chart 24. On the other hand, if all defaults occurred only among the lowest-rated securities, with no defaults among the higher-rated securities, the Lorenz curve would lie along the ideal curve in chart 24. Typically, the observed Lorenz curve falls between the ideal and random curves, and we use the Gini coefficient as a summary statistic to quantify its proximity to the ideal curve.

The procedure for calculating the Gini coefficient is illustrated in chart 24. Area B is bounded by the random curve and the Lorenz curve, while area A is bounded by the Lorenz curve and the ideal curve. The Gini coefficient is defined as area B divided by the sum of area A and area B. The Gini coefficient can therefore range from zero (if the Lorenz curve follows the random curve) to 100% (if the Lorenz curve follows the ideal curve). In general, therefore, the higher the Gini coefficient, the greater the link between our ratings and the securities' observed default propensity.

Appendix II: Detailed Default And Transition Statistics

Tables 9-52 provide various default and transition rate statistics for global structured finance securities and major structured finance subsectors.

Global structured finance

Table 9

Table 10

Table 11

Table 12

U.S. RMBS

Table 13

Table 14

Table 15

Table 16

U.S. structured credit

Table 17

Table 18

Table 19

Table 20

U.S. ABS

Table 21

Table 22

Table 23

Table 24

U.S. CMBS

Table 25

Table 26

Table 27

Table 28

Europe

Table 29

Table 30

Table 31

Table 32

Other regions

Table 33

Table 34

Table 35

Table 36

Global RMBS

Table 37

Table 38

Table 39

Table 40

Global structured credit

Table 41

Table 42

Table 43

Table 44

Global ABS

Table 45

Table 46

Table 47

Table 48

Global CMBS

Table 49

Table 50

Table 51

Table 52

Appendix III: Comparison Of Alternative Treatments For 'AAA' Ratings

This edition of our global structured finance default and rating transition study adopts a new approach to the treatment of securities with original ratings of 'AAA'. As detailed in Appendix I, this study uses the "uncollapsed" approach to the treatment of securities with original ratings of 'AAA', rather than the "collapsed" approach, which was used in studies published before 2018. Charts 25-28 illustrate the resulting differences in some example summary statistics.

Related Publications

Related Criteria

• U.S. Structured Finance Asset Isolation And Special-Purpose Entity Criteria, May 15, 2019
• U.S. FFELP Student Loan ABS: Methodology And Assumptions, April 4, 2019
• Credit Stability Criteria, May 3, 2010
• Understanding S&P Global Ratings' Rating Definitions, June 3, 2009

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