In 2022, the number of defaults among companies rated by S&P Global Ratings in emerging and frontier markets rose to 25--18 from companies with active ratings at the start of the year--from 14 in 2021 (see chart 1). Liquidity challenges hit several issuers as global inflation accelerated and financing conditions tightened.

The large majority of defaults came from Asia-Pacific, particularly the real estate/homebuilders sector in Greater China (including China, Hong Kong, Macao, Taiwan, and Red Chip companies, or those headquartered in Greater China but incorporated elsewhere), where the government tightened regulations on developers' leverage, greatly affecting issuers in this sector with high leverage and high-asset-turnover business models. Moreover, the government tightened the escrow rule compliance requirements on presale proceeds. The liquidity crunch for developers has deepened since the second half of 2021, spurring investors to brace for liquidity and insolvency risk.

Only one issuer from frontier markets defaulted: Marshall Islands energy company Altera Infrastructure L.P.

All of the defaults were from companies rated speculative-grade ('BB+' or lower), and roughly half of these were rated 'B-' or lower.

Key Conclusions

We reached the following key conclusions regarding corporate ratings performance in emerging and frontier markets in 2022:

• Emerging and frontier market defaults represented 30% of global corporate defaults in 2022--the highest percentage in the 1997-2022 period covered by this study--up from 19% in 2021.
• Nine of the 25 emerging and frontier market defaulters in 2022 were confidentially rated. Of the total, 17 were from emerging Asia and four each from Latin America and Eastern Europe, the Middle East, and Africa (EEMEA) (see Appendix 1 for a regional breakdown of countries).
• Distressed exchanges were the leading cause of default, with 40%, followed by missed interest and principal payments with 36%.
• Among nonfinancial entities, the aggregate one-year default rate in 2022 was 1.75%, in line with the 1997-2022 median of 1.78%.
• The financial sector's default rate in 2022 was 0.6%, slightly above the 1997-2022 median of 0.45%.
• The average time to default--the average number of years elapsed between the initial rating date and the default date--over the long term (1997-2022) was 4.2 years for emerging markets and 4.9 years for frontier markets, below the global average (1981-2022) of 5.9 years.
• The shorter time to default may be attributable to more volatile economic and financial conditions, emerging markets' smaller number of data points, and their rating distribution, which skews lower than that of developed markets.
• The issuer with the highest debt amount defaulting in 2022 was Mexico-based nonbank financial institution Credito Real S.A.B. de C.V. SOFOM E.N.R. (Futu Iem S.A.P.I. de C.V.), with $1.94 billion.
• Ratings stability, measured by the proportion of unchanged ratings, dipped to 72.54% in 2022 from 76.6% in 2021, remaining above the 68% average.
• The speculative-grade corporate default rate in emerging markets rose to 2.29% in 2022 from 1.91% in 2021, higher than the 2.02% median for 1997-2022 and the 1.92% 2022 global corporate speculative-grade default rate (see chart 2).
• Among rated corporate issuers based in emerging markets, the one-year Gini coefficient was 76.19% in 2022, lower than the global average of 82.37%; global defaulters began the year with lower ratings on average than emerging market defaulters.
• Rating transitions were similar between emerging and frontier market companies and global companies: Downgrades inched up to 5.44% in 2022 from 5.18% in 2021, and upgrades decreased to 7.34% from 9.52%.
• Consequently, the downgrade-to-upgrade ratio for emerging and frontier markets increased to 0.74x in 2022 from 0.54x in 2021.
• We withdrew 13% of the ratings in emerging and frontier markets in 2022.
• S&P Global Ratings assigned ratings to 106 new emerging and frontier market issuers in 2022, down from 143 in 2021. We rated 70 of these entities, or 66%, speculative-grade (see table 4).
• Of all 1,365 actively rated emerging and frontier issuers at the end of 2022, 48.8% were rated speculative-grade, lower than the long-term average of 57% (1997-2022). Globally, active speculative-grade ratings at the end of 2022 totaled 3,485, or 49.5% of total rated entities.

Defaults Rose In 2022 As Downgrades Held Relatively Stable

In the 26 years that we have tracked defaults in emerging markets, the region's speculative-grade corporate default rate has exceeded the global speculative-grade corporate default rate in six years: 1998, 1999, 2002, 2015, 2021, and 2022 (see chart 2). Last year the emerging and frontier market speculative-grade corporate default rate was 2.38%, 46 basis points above the global speculative-grade corporate default rate of 1.92%. Although in 2022 defaults were concentrated in Asia, large default cycles are generally aligned across regions.

Downgrades (excluding defaults) rose modestly during the year, which along with the decrease in upgrades brought the ratio of downgrades to upgrades in emerging and frontier markets to 0.74x in 2022--still lower than the 1997-2022 median of 1.06x.

The historically lower speculative-grade default rate in emerging markets owes to its smaller proportion of speculative-grade corporate issuers rated 'B+' or lower. Since 1997, this proportion has been, on average, 13 percentage points lower in emerging markets than globally, and it was 11 percentage points lower in 2022. This gap has grown since 2009 as accommodative financial conditions have led to larger shares of 'B+' or lower issuers among speculative-grade corporate issuers in developed markets, particularly the U.S. and Europe, while that same proportion has remained unchanged in emerging markets.

This divergence will likely persist even as capital markets in emerging market countries develop; investors in emerging and frontier markets generally have less appetite for lower-rated issuers because recoveries can be more complex in the event of default, given disparate legal and regulatory frameworks.

Table 1

Table 2

Unless noted otherwise, the statistics we present in this study refer to S&P Global Market Intelligence's CreditPro® corporate local currency ratings. Rated issuers include financial and nonfinancial companies in emerging and frontier markets but exclude sovereigns and public finance issuers. We calculated default and transition rates based on the number of issuers in the sample period. The weighted averages in this study use the number of issuers at the beginning of each year as the basis for each year's weight. The data we present in this study, unless labeled otherwise, refers to only public and confidential issuer credit ratings on nonfinancial and financial issuers and excludes credit estimates. For our full methodology and definitions of the terms used, as well as a full list of the emerging and frontier market countries included in this study, see Appendix 1.

The Upgrade Rate Declined After A 2021 Recovery

Last year's upgrades were concentrated in the banking sector, where margins improved on the rise in interest rates, improved asset quality and risk-adjusted capital ratios, and easing capital pressures. Meanwhile, high energy demand and prices contributed to strong ratings performance in the energy and utilities sectors, as evidenced by the historically low number of downgrades in 2022 (nine).

That said, the share of upgrades in emerging and frontier markets decreased to 7.34% in 2022 after hitting 9.5% in 2021. Ratings were unchanged for close to 73% of rated issuers in 2022, above the long-term average of 68%. The 2022 downgrade rate, at 5.44%, remained close to the 5.18% recorded in 2021 and mainly stemmed from financial corporates (banks in Russia and nonbank financial institutions in Mexico) and real estate (in China).

Table 3

Table 4

Issuer credit ratings in emerging markets are mostly between 'BB' and 'BBB' (60.8% of all rated issuers, compared with 46.4% globally). The concentration in the low investment-grade and high speculative-grade categories largely owes to lower sovereign credit ratings in emerging markets. Although sovereign ratings don't act as caps on issuer credit ratings, a corporate issuer rated higher than the sovereign must be able to withstand a sovereign default. For example, government-related entities are typically more subject to country risk and sovereign intervention risk than private-sector entities, since they usually have direct links to governments, and some of these issuers have explicit or implicit links to the respective sovereign or have an eligible guarantee from a parent or sovereign. But whether the entity is government related or not, the issuer credit rating is subject to sovereign-related considerations.

Frontier market issuer ratings are highly concentrated in the 'BB' and 'B' rating categories (73.3%), but inferences in this study based on our frontier market ratings are limited by the small sample size, with just 15 rated issuers in frontier markets at the end of 2022, compared with 1,350 in emerging markets and 7,042 globally.

Real Estate And Consumer/Service Led Defaults

The concentration of defaults by industry remained similar in 2022 to the concentration in 2021. Real estate/ building products/homebuilders (40%) and the consumer/service sector (28%) led defaults. Consumer products took a severe hit from raw material price inflation and supply-chain complexities, particularly EEMEA issuers that suffered the spillover effects of the Russia-Ukraine conflict.

Three industries' default rates in 2022 were above their long-term weighted averages: real estate (8.9% versus 1.6%), leisure time/media (5.9% versus 5.4%), and consumer/service (5% versus 2.6%).

Table 5

Table 6

Defaults Were Concentrated In Emerging Asia

We group emerging and frontier market countries into three subregions (see Appendix 1 for a breakdown of each subregion).

In 2022, emerging Asia led with 17 defaults (up from five in 2021), or 68% of all defaults in emerging and frontier markets. Real estate issuers accounted for 10 defaults from this region.

The Latin American defaults came from issuers in Mexico (four defaults, down from nine in 2021, all for missed payments) and were mainly the outcome of liquidity strains, exacerbated by accelerating global inflation and the fast monetary tightening by the Federal Reserve, which undermined investors' confidence and led issuers to strengthen their liquidity positions.

Four defaults came from EEMEA (up from zero defaults in 2021). Of these, 75% were triggered by the economic consequences of the Russia-Ukraine conflict, which brought business and financial disruption.

Of the 25 emerging and frontier markets defaults in 2022, nine were from confidentially rated entities (see table 7 for the list of publicly rated entities). These defaulters had a total of US$5.48 billion of debt outstanding (including confidential issuer debt).

Table 7  |  View Expanded Table

Historical Waves Of Defaults

Through the end of 2022, there have been five historical periods with relatively high emerging and frontier market default rates. The first one was associated with shocks from the Asian financial crisis of the late 1990s. In 1998, defaults were concentrated among Indonesian nonfinancial companies and Russian financial companies. The defaults in Indonesia came amid the political unrest that ensued from the financial crisis, and those in Russia came amid the collapse of its banking system. The following year, defaults in Asia-Pacific continued, but as the financial crisis spilled into other regions, the majority of defaults came from issuers in Latin America.

Then, in 2002, the emerging market corporate default rate spiked to its all-time high (11.96%) when the recession in Argentina, which began in 1998, deepened into a financial crisis, with nearly three-quarters of defaults that year coming from Argentine issuers.

Emerging and frontier market corporate default rates climbed again during the 2008-2009 financial crisis, to 1.41% in 2008 and 3.66% in 2009.

In 2015, most emerging market corporate defaults were from nonfinancial issuers in Brazil amid a worsening recession and from financial issuers in Russia amid a financial crisis, which in part stemmed from sanctions imposed on Russia following its 2014 invasion of Ukraine.

In 2020, the credit quality of corporate issuers in emerging and frontier markets sharply deteriorated as the COVID-19 pandemic pushed the world into recession. The proportion of total issuers that were downgraded (including defaults) jumped to the highest since 2016, and the proportion of issuers that were upgraded hit an all-time low.

Ratings Performance Was Generally Consistent With Global Trends

The sample sizes for emerging and frontier markets present a challenge in our analysis. For this study, there were 22,918 issuers in the global pool from 1981 to 2022, compared with 3,755 issuers in the combined emerging and frontier markets pool from 1997 to 2022. Emerging and frontier market defaults in that period totaled just 408 and 17, respectively--together, 13.1% of the 3,253 defaults globally during that time.

Additionally, while the study period for emerging and frontier markets is 1997-2022, more than two-thirds of these issuers were assigned ratings after 2005, and averages are more heavily influenced by recent years. The sample sizes for emerging and frontier markets should be considered in reviewing default trends.

Average times to default and default rates by rating modifier

In emerging markets, the average time to default across most rating categories is shorter than in the global pool, with the exception of the 'CCC' to 'C' rating categories. Higher-rated companies generally take longer to default, but this is less evident in emerging markets than in the global pool because of the limited number of investment-grade (rated 'BBB-' or higher) defaults (see table 8).

Similarly, the average time to default across rating categories in frontier markets should be considered with the limited sample size in mind.

The average time to default from the original rating for 'A' rating category issuers in emerging markets is an anomaly, with just one default in this rating category from 1997 to 2022; this issuer was first rated 'A+' confidentially in 2008 and was downgraded twice in 2009 (by a total of three notches) to 'BBB+', and then the rating was withdrawn before it defaulted later in 2009.

The rating categories with the largest sample sizes in emerging markets exhibit the same relationship that appears in the global data, with higher ratings exhibiting longer times to default: 'B' category emerging market corporate issuers took 3.7 years to default on average, while those rated in the 'BB' category took 5.6 years. The average time to default shortens when including post-original ratings--or the ratings each entity was assigned on its path to default excluding the original rating (see table 9)--suggesting initial ratings from S&P Global Ratings are effective indicators of credit risk on average.

Table 8

Table 9

Looking at default rates by modifier shows that lower ratings experience higher default rates on average historically, though variability is possible in any given year within a rating category (see table 10). For example, in 2009 'B+' rated companies had a higher default rate than 'B' rated companies. Nevertheless, data from past default cycles shows a preponderance of defaults from the lowest rating categories.

Table 10

Transition and cumulative default rate tables

Analysis of 2022 rating transitions suggests that rating activity in emerging and frontier markets is consistent with global trends, which have shown a clear correlation between low ratings and the probability of default.

Investment-grade-rated issuers in emerging markets tend to have greater ratings stability (as measured by the rate of transitions) than their speculative-grade counterparts (see table 11). For instance, about 93.75% of emerging market issuers in the 'A' rating category as of Jan. 1, 2022, were still rated in that category at the end of 2022 (versus 94.88% for global corporates), whereas the comparable metric for issuers rated in the 'B' category was 66.54% (versus 76.29%).

In 2022, emerging market ratings were less stable than the global trend, with the exception of 'CCC' to 'C' category rated companies. The difference is explained by low upward mobility from the 'B' rating category: Only 7.55% of 'CCC' to 'C' rated companies moved to 'B' in emerging markets, while 17.55% of global corporates did.

Table 11

Emerging market corporate rating transitions also remain similar to global corporate transitions over the long term. Of the emerging market issuers rated in the 'A' rating category from 1997 to 2022, 91.39% were still rated in that category after one year, whereas 71.24% of issuers rated in the 'B' rating category remained at that rating (see table 12). Transition rates for emerging markets are also generally consistent with global patterns in periods greater than one year (see table 13).

By modifier, rating transitions generally display the same relationship, but there is some variation between adjacent rating categories; this in part reflects the sample sizes for each rating (see table 14).

Table 12 and 13 shed further light on the 'CCC' to 'C' trend for 2022: Averaged over the 1997-2022 period, upward transitions are actually more frequent in emerging markets than globally over one and two years, while transitions to default are lower. The higher withdrawal rate in emerging markets also contributes to this effect.

Table 12

Table 13

Table 14

Lower ratings and defaults in emerging and frontier markets remain correlated over longer time horizons (see charts 4-5 and tables 15-16). In emerging markets, no issuers rated in the 'AAA' or 'AA' rating categories have ever defaulted from that rating. There was just one default from an original rating in the 'A' category from 1997 to 2022.

Default rates increase at lower ratings. For instance, emerging market issuers in the 'BB' rating category had an average default rate of 0.56% after one year and 6.81% after 10 years, while those in the 'B' rating category had an average default rate of 3.16% after one year and 16.91% after 10 years. Charts 4-5 evidence the nonlinearity of the rating scale: The point-in-time distance between the 'CCC'/'C' and 'B' curve is higher than between the 'B' and 'BB' one.

Table 15

Table 16

Gini ratios and Lorenz curves

As in our global default study, the default data in emerging markets indicates that issuer credit ratings are effective indicators of relative credit risk over time. We measure this relationship with movements in Gini ratios.

Gini ratios measure the rank-ordering power of ratings over a given time horizon and show the ratio of actual rank-ordering performance to theoretically perfect rank ordering. If corporate ratings only randomly approximated default risk, the Gini coefficient would be zero. If corporate ratings were perfectly rank ordered so that all defaults occurred only among the lowest-rated issuers, the Gini ratio would be 100%. A quantitative measure of ratings performance indicates that the relative rank ordering of ratings in emerging markets is consistent across various time horizons: Our calculations indicate that the one-year transition to default in emerging markets shows an average one-year Gini coefficient of 76.19%, a three-year of 62.34%, and a five-year of 55.18%.

Gini coefficients decline as the time horizon lengthens because longer time horizons allow for more credit degradation among higher-rated issuers. In the one-year emerging market Lorenz curve, for example, 97.7% of defaults occurred in the speculative-grade category, while speculative-grade ratings accounted for 56% of all emerging market corporate issuer ratings (see chart 6). The five-year Lorenz curve shows that speculative-grade issuers made up 90.2% of defaulters and 57.6% of the entire sample (see chart 10). If the rank ordering of ratings had little predictive value, the cumulative share of defaulting corporate issuers and the cumulative share of all issuers would be nearly the same.

Growth in the number of emerging and frontier markets corporate issuers rated by S&P Global Ratings will continue to make weighted average Gini ratios more meaningful, as well as lower the likelihood of data distortion by outliers, which affects long-term averages. (For definitions and more information on the Gini methodology, see Appendix 3.)

Table 17

Appendix 1: Default Methodology And Definitions

This long-term corporate default and rating transition study uses S&P Global Market Intelligence's CreditPro® database of long-term local currency issuer credit ratings. Most exhibits in this study are the direct output of the CreditPro® interface, while others reflect manipulation of the underlying database.

An issuer credit rating reflects S&P Global Ratings' forward-looking opinion of a company's overall creditworthiness. This opinion focuses on the obligor's capacity and willingness to meet its financial commitments as they come due. It does not apply to any specific financial obligation because it does not take into account the nature and provisions of the obligation, its standing in bankruptcy or liquidation, statutory preferences, or the legality and enforceability of the obligation. It is not necessary for a company to have rated debt to be assigned an issuer credit rating.

While the issue credit rating is an assessment of default risk, it may also incorporate an assessment of the relative seniority or ultimate recovery of the issue in the event of default. The junior obligations of a company are typically rated lower than the senior obligations to reflect the lower priority in bankruptcy and ultimate recovery expectations. Alternatively, secured debt may receive a rating that is above the issuer credit rating. Notching also applies to the structural subordination of debt issued by operating subsidiaries or holding companies that are part of an enterprise that we view as a single economic entity.

The ongoing enhancement of the CreditPro® database used to generate this study could lead to outcomes that differ, to some degree, from those reported in previous studies. However, this poses no continuity problem because each study reports statistics going back to Dec. 31, 1996. Therefore, each annual default study is self-contained and effectively supersedes all previous versions.

Issuers included in this study

Emerging markets in this study consist of the following subregions:

Asia-Pacific:  Bangladesh, Bhutan, British Indian Ocean Territory, China, Fiji, Hong Kong, India, Indonesia, Macao Special Administrative Region of China, Malaysia, Mongolia, Pakistan, Papua New Guinea, Philippines, Sri Lanka, Taiwan, Thailand, and Vietnam

EMEA:  Angola, Armenia, Azerbaijan, Bahrain, Belarus, Bosnia and Herzegovina, Bulgaria, Benin, Cote d'Ivoire, Croatia, Cyprus, Egypt, Estonia, Gabon, Georgia, Ghana, Hungary, Jordan, Kazakhstan, Kenya, Kuwait, Latvia, Lebanon, Lithuania, Mauritius, Montenegro, Morocco, Namibia, Nigeria, Oman, Poland, Qatar, Republic of Moldova, Russia, Saudi Arabia, Slovakia, South Africa, Tunisia, Turkiye, Ukraine, United Arab Emirates, and Uzbekistan

Latin America and Caribbean:  Argentina, Barbados, Belize, Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, El Salvador, Grenada, Guatemala, Honduras, Jamaica, Mexico, Panama, Paraguay, Peru, Trinidad and Tobago, and Uruguay

Frontier markets in this study consist of the following subregions:

Asia-Pacific:  Cambodia and Marshall Islands

EMEA:  Liberia, Syrian Arab Republic, and Togo

Latin America and Caribbean:  Aruba, Bahamas, Curacao, Netherlands Antilles*, Turks and Caicos Islands, and Venezuela

*The Netherlands Antilles was dissolved in 2010.

This study analyzes the rating histories of 3,755 emerging and frontier market corporate issuers with credit ratings from S&P Global Ratings between Jan. 1, 1997, and Dec. 31, 2022. These include industrials, utilities, financial institutions, and insurance companies with long-term local currency ratings. The analysis excludes public information (pi) ratings and ratings based on the guarantee of another company or government entity. Structured finance vehicles, public-sector issuers, and sovereign issuers are the subjects of separate default and transition studies, and we excluded these from this study.

In this study, the insurance industry includes life insurance, health insurance, property/casualty insurance, reinsurance, bond insurance, mortgage insurance, and title insurance. This study also groups insurance service providers (such as insurance brokers and third-party administrators that are rated according to corporate criteria) with the insurance industry.

To avoid double-counting, the CreditPro database excludes subsidiaries with debt that is fully guaranteed by a parent or with default risk that is considered identical to that of a parent. The latter are companies with obligations that are not legally guaranteed by a parent but that have operating or financing activities that are so inextricably entwined with those of the parent that it would be impossible to imagine the default of one and not the other. At times, however, some of these subsidiaries might not yet have been covered by a parent's guarantee, or the relationship that combines the default risk of parent and subsidiary might have come to an end or might not have begun. We included such subsidiaries for the period during which they had a distinct and separate risk of default.

Issuers with withdrawn ratings

S&P Global Ratings withdraws ratings when an entity's entire debt is paid off or when the program or programs rated are terminated and the relevant debt extinguished. For the purposes of this study, a rating may be withdrawn as a result of mergers and acquisitions. Others are withdrawn because of a lack of cooperation, particularly when a company is experiencing financial difficulties and refuses to provide all the information needed to continue surveillance on the ratings, or at the entity's request.

Definition of default

An obligor rated 'SD' (selective default) or 'D' (default) is in payment default on one or more of its financial obligations (rated or unrated) unless S&P Global Ratings believes that such payments will be made within five business days, irrespective of any grace period. S&P Global Ratings also lowers a rating to 'D' upon an issuer's filing for bankruptcy or taking a similar action that jeopardizes payments on a financial obligation.

A 'D' rating is assigned when S&P Global Ratings believes that the default will be a general default and that the obligor will fail to pay all or substantially all of its obligations as they come due. S&P Global Ratings assigns an 'SD' rating when it believes the obligor has selectively defaulted on a specific issue or class of obligations but will continue to meet its payment obligations on other issues or classes of obligations in a timely manner. A selective default includes the completion of a distressed exchange offer, whereby one or more financial obligations are either repurchased for an amount of cash or replaced by other instruments having a total value that is less than par.

'R' (regulatory intervention) indicates that an obligor is under regulatory supervision owing to its financial condition. This does not necessarily indicate a default event, but the regulator might have the power to favor one class of obligations over others or pay some obligations and not others. Preferred stock is not considered a financial obligation; thus, a missed preferred stock dividend is not normally equated with default. On July 5, 2019, we removed 'R' from all rating scales. That said, several historical defaults within our dataset were at 'R', so for the purposes of this study, we have maintained these as default events.

We deem 'D', 'SD', and 'R' issuer ratings to be defaults for the purposes of this study. A default is assumed to take place on the earliest of the date S&P Global Ratings revised the rating(s) to 'D', 'SD', or 'R'; the date a debt payment was missed; the date a distressed exchange offer was announced; or the date the debtor filed or was forced into bankruptcy.

When an issuer defaults, it is not uncommon for S&P Global Ratings to subsequently withdraw the 'D' rating. For the purposes of this study, if an issuer defaults, we end its rating history at 'D'. If any defaulting entity reemerges from bankruptcy or otherwise restructures its defaulted debt instruments, thereby reestablishing regular, timely payment of all its debts, we reenter this issuer into the database as a new entity. Its rating history after the default event is included in all calculations as entirely separate from its experience leading up to its earlier default.

Calculations

Static pool methodology.  We conduct our default studies on the basis of groupings called static pools. For the purposes of this study, we form static pools by grouping issuers by rating category at the beginning of each year covered by the study. Each static pool is followed from that point forward. All companies included in the study are assigned to one or more static pools. When an issuer defaults, we assign that default to all of the static pools to which the issuer belonged.

We use the static pool methodology to avoid certain pitfalls in estimating default rates, such as by ensuring that default rates account for rating migration and can be calculated across multiperiod time horizons. Some methods for calculating default and rating transition rates might charge defaults against only the initial rating on the issuer, ignoring more recent rating changes that supply more current information. Other methods calculate default rates using only the most recent year's default and rating data; these methods might yield comparatively low default rates during periods of high rating activity because they ignore prior years' default activity.

The pools are static in the sense that their membership remains constant over time. Each static pool can be interpreted as a buy-and-hold portfolio. Because errors, if any, are corrected by every new update and the criteria for inclusion or exclusion of companies in the default study are subject to minor revisions as time goes by, it is not possible to compare static pools across different studies. Therefore, every update revises results to the same starting date of Dec. 31, 1996, to avoid continuity problems.

Entities that have had ratings withdrawn--that is, revised to not rated (NR)--are surveilled with the aim of capturing a potential default. Because static pools only include entities with active ratings as of the beginning date of a given pool, we exclude companies with withdrawn ratings, as well as those that have defaulted, from subsequent static pools. If the rating on an entity is withdrawn after the start date of a particular static pool and the entity subsequently defaults, we will include it in that static pool as a default and categorize it in the rating category of which it was a member at that time.

For instance, the 1998 static pool consists of all companies rated as of 12:01 a.m. on Jan. 1, 1998, while the 1999 static pool consists of those companies first rated in 1998 and the surviving members of the 1998 static pool. All rating changes that took place in 1998 are reflected in the newly formed 1999 static pool. We used this same method to form static pools for each year in the study.

Consider the following example: S&P Global Ratings downgraded an issuer that was originally rated 'BB' in mid-1998 to 'B' in 2000 and then withdrew the rating (NR) in 2002; the company subsequently defaulted ('D') in 2005. This hypothetical company would be included in the 1999 and 2000 pools with the 'BB' rating, which was the rating at the beginning of those years. Likewise, the company would be included in the 2001 and 2002 pools with the 'B' rating. The company would not be part of the 1998 pool because it was not rated as of the first day of that year, and we would not include it in any pool after the last day of 2002 because S&P Global Ratings had withdrawn the rating by then. Yet each of the four pools in which this company was included (1999-2002) would record its 2005 default at the appropriate time horizon.

Default rates.  We calculated annual default rates for each static pool, first in units and then as percentages with respect to the number of issuers in each rating category. Finally, we combined these percentages to obtain cumulative default rates for the 26 years the study covers.

Issuer-weighted default rates.  All default rates that appear in this study are calculated based on the number of issuers rather than the dollar amounts affected by defaults or rating changes. Although dollar amounts provide information about the portion of the market that is affected by defaults or rating changes, issuer-weighted averages are more useful measures of the performance of ratings.

Many practitioners use statistics from this default study and CreditPro® to estimate "probability of default" and "probability of rating transition." It is important to note that S&P Global Ratings' credit ratings do not imply a specific probability of default.

Cumulative average default rates.  The cumulative default rates in this study average the experience of all static pools by first calculating marginal default rates for each possible time horizon and for each static pool, weight-averaging the marginal default rates conditional on survival (survivors being nondefaulters), and accumulating the average conditional marginal default rates. We calculate conditional default rates by dividing the number of issuers in a static pool that default at a specific time horizon by the number of issuers that survived (did not default) to that point in time. The weights are based on the number of issuers in each static pool. The cumulative default rate is one minus the product of the proportion of survivors (nondefaulters).

Standard deviations.  Many of the exhibits in this study display averages of default rates, transition rates, and Gini ratios. Often these are issuer-weighted averages. Prior studies have shown that fluctuations in default rates and transitions can vary greatly depending on many circumstances specific to particular time frames, industries, and geographic regions. Standard deviations are also shown to provide a gauge of the dispersion of the ranges of data behind these averages.

For the transition matrices in tables 12-14 and 24-27, the standard deviation for each cell in a given matrix is calculated using the data from each of the underlying cohort years that contribute to the averages. For example, in the average one-year emerging market transition matrix in table 12, each cell's standard deviation is calculated from the series of that particular cell in each of the 26 cohorts beginning with the 1997 cohort and ending with the 2022 cohort.

Time sample.  This study limits the reporting of default rates in emerging markets to the 26-year time horizon, and we based all calculations on the rating experience of that period. Global data is based on a 42-year time horizon. The maturities of most obligations are much shorter than 26 years. In addition, average default statistics become less reliable at longer time horizons because the sample size becomes smaller and the cyclical nature of default rates has a bigger effect on averages.

Default patterns share broad similarities across all static pools, suggesting that S&P Global Ratings' standards have been consistent over time. Adverse business conditions tend to coincide with default upswings for all pools. Speculative-grade issuers have been hit the hardest by these upswings, but investment-grade default rates also increase in stressful periods.

Transition analysis

Transition rates compare the issuer credit ratings at the beginning of a period with the ratings at the end of the period. To compute one-year rating transition rates by rating category, we compared the rating on each entity at the end of a particular year with the rating at the beginning of the same year. We counted an issuer that remained rated for more than one year as many times as the number of years it was rated.

For instance, an issuer continuously rated from mid-1999 to mid-2006 would appear in the six consecutive one-year transition matrices from 2000 to 2005. If the rating on the issuer was withdrawn in the middle of 2006, it would be included in the column representing transitions to NR in the 2006 transition matrix. Similarly, if it defaulted in the middle of 2006, it would be included in the column representing transitions to 'D' in the 2006 one-year transition matrix.

All 1997 static pool members still rated on Dec. 31, 2022, had 26 one-year transitions, while companies first rated on Jan. 1, 2022, had only one. Each one-year transition matrix displays all rating movements from the beginning of the year through year-end. For each rating listed in the matrix's leftmost column, there are nine ratios listed in the rows, corresponding to the ratings from 'AAA' to 'D', plus an entry for NR. The ratios represent the historical incidence of the ratings listed in the first column changing to the ones listed in the top row over the course of the reference period (see tables 12 and 13).

The only ratings considered in these calculations are those on entities at the beginning of each static pool and those at the end. All rating changes that occur in between are ignored. For example, if an entity was rated 'A' on Jan. 1, 2022, downgraded to 'BBB' in the middle of the year, and then upgraded to 'A' later in the year (with no other subsequent rating changes), this entity would be included only in the percentage of issuers that began the year as 'A' that ended the year as 'A'. This also applies to transition matrices that span longer time horizons. If an issuer defaults or the rating is withdrawn in the middle of the year, then it would be considered either rated 'D' or not rated as of Dec. 31 of that particular year.

Multiyear transitions

We also calculated multiyear transitions for periods of two years to five years. In this case, we compared the rating at the beginning of the multiyear period with the rating at the end. For example, we could calculate two-year transition matrices by comparing the ratings at the beginning of the years 1997-2020 with the ratings at the end of the years 1999-2022 (see table 13). Otherwise, the methodology was identical to that used for single-year transitions.

We calculated average transition matrices on the basis of the multiyear matrices just described. These average matrices are a true summary, the ratios of which represent the historical incidence of the ratings listed in the first column changing to the ones listed in the top row over the course of the multiyear period (see tables 13 and 24-27). Transition matrices that present averages over multiple time horizons are also calculated as issuer-weighted averages.

Comparing transition rates with default rates

Rating transition rates may be compared with the marginal and cumulative default rates. For example, the one-year default rate column of table 15 is equivalent to column 'D' of the average one-year transition matrix in table 12 and the cumulative average in the summary statistics of the year one column in table 20.

However, the two-year default rate column in table 15 is not the same as column 'D' of the average two-year transition matrix in table 13. This difference results from the different methods of calculating default rates. The default rates in table 13 are calculated as not conditional on survival, while those in table 15 are average default rates conditional on survival. The two-year default rates in table 15 are calculated in the same way as those in the cumulative average section for the year two column in table 20, while those in the 'D' column of table 21 are equivalent to the sum of all the defaults behind the year two column's annual default rates in table 20 divided by the sum of all the issuers in table 20.

Tables 18-23 best illustrate the links between transition matrices and average cumulative default rates. The default rates in the columns of these tables, associated with each static pool year, are calculated in the same way as they would be for each individual year's transition matrices. Tables 18-23 are broken out by the broadest rating classifications (all rated, investment-grade, and speculative-grade). These tables can also be constructed for each rating category.

As an example, the year two column of table 20 shows the two-year default rates (not conditional on survival) for each static pool. These are calculated in the same way as the default column in table 11, though table 11 shows the one-year default rates for each rating category for 2022 exclusively. In the summary section at the bottom of tables 18-23, the first row shows the issuer-weighted averages of the marginal default rates. These marginal averages are then used to calculate the cumulative average default rates in the row directly beneath them. These default rates are the same that appear in table 15 and are average cumulative default rates conditional on survival.

Appendix 2: Additional Tables

Table 18

Table 19

Table 20

Table 21

Table 22

Table 23

Table 24

Table 25

Table 26

Table 27

Appendix 3: Gini Methodology

To measure ratings performance, or ratings accuracy, we plotted the cumulative share of issuers by rating against the cumulative share of defaulters in a Lorenz curve to visually render the accuracy of their rank ordering. Max O. Lorenz developed the Lorenz curve as a graphical representation of the proportionality of a distribution.

To build the Lorenz curve, we ordered the observations from the low end of the ratings scale ('CCC'/'C') to the high end ('AAA'). If S&P Global Ratings' corporate ratings only randomly approximated default risk, the Lorenz curve would fall along the diagonal and the Gini coefficient--which is a summary statistic of the Lorenz curve--would be zero. If corporate ratings were perfectly rank ordered so that all defaults occurred only among the lowest-rated entities, the curve would capture all of the area above the diagonal on the graph and the Gini coefficient would be 100% (see chart 12).

We calculate the Gini coefficients by dividing area B by the total of area A plus area B. In other words, the Gini coefficient captures the extent to which actual ratings accuracy diverges from the random scenario and aspires to the ideal scenario.

Related Research

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