Greater China corporate defaults increased to nine in 2019 from three defaults in 2018, surpassing the previous record of six defaults in 2015 (since 2000). This is not necessarily a sign of significant credit degradation as the speculative-grade population also rose, to 167 in 2019 from 90 in 2015. The global default tally reached 118 in 2019, up from 82 in 2018, but remained lower than the peak of 268 defaults during the subprime financial crisis in 2009.

There have been very few SOE (state-owned enterprise) defaulters in our rated portfolio in recent years. Having said that, more defaults are likely to come, and credit polarization is likely to continue among our rated SOEs because of the potential acceleration of SOE deleveraging and reform as the economy recovers from the pandemic.

For newly rated corporate issuers in Greater China, 68 came to market in 2019, compared with 124 the previous year. The tally of companies rated by S&P Global Ratings in Greater China (China, Hong Kong, Macau, and Taiwan) was 577 at year-end 2019, down by 3% from 593 issuers at year-end 2018.

In this study, S&P Global Ratings Research examines the ratings performance of 1,001 Greater China-based issuers rated by S&P Global Ratings. Entities included in this study are those with business operations in Greater China, regardless of the market in which they are incorporated. In a number of instances, entities included in this study are incorporated in foreign tax havens like the Cayman Islands. While S&P Global Ratings rated issuers in Greater China prior to 2000, we limited the scope of analysis to issuers rated from 2000-2019. The statistics we present in this study refer only to the corporate ratings universe, which includes financial and nonfinancial entities in Greater China. Our methodology and the definitions of the terms we use in this study are in Appendix I.

Our study of corporate defaults in Greater China identified a clear negative correspondence between ratings and defaults: The higher the issuer credit rating, the lower the observed default frequency.

The one-year average Gini ratio--a measure of the relative ability of ratings to differentiate risk--was 89.08% in Greater China. This signifies a very strong ability of ratings to differentiate relative credit risk across the ratings spectrum. The three-year Gini ratio for Greater China was 83.73%. By comparison, the global one-year Gini ratio was 82.53%, and the global three-year Gini ratio was 75.21% (see table 1). Gini ratios are measures of the rank-ordering power of ratings over a given time horizon. They show the ratio of actual rank-ordering performance to theoretically perfect rank ordering (for details on the Gini methodology, refer to Appendix III).

Table 1

Our study found that higher ratings correspond with stronger ratings stability in Greater China for both SOEs and non-SOEs. The Greater China SOE 2000-2019 one-year Gini coefficient was 99.06%, three-year Gini ratio was 98.95%, and five-year Gini was 98.31%. For the same period, the Greater China non-SOE one-year Gini ratio was 85.49%, three-year Gini ratio was 79.36%, and five-year Gini ratio was 71.26% (see table 2).

Table 2

Considering Greater China's comparatively high proportion of investment-grade companies, along with its strong Gini ratio, we believe issuers rated by S&P Global Ratings will continue to perform well, with low default tallies and high rating stability. Although increased downgrades and defaults in the region are still possible if economic and geopolitical conditions deteriorate, issuers in the investment-grade categories are overall better equipped for such conditions than those in the lower rating categories like 'B' and 'CCC', where the majority of downgrades and defaults would likely be concentrated.

While the COVID-19 pandemic and knock-on impact on global economic growth become major dampers in 2020, default risk for issuers in Greater China and Asia overall has remained lower than for issuers globally. Nevertheless, riskier borrowers in Asia are still vulnerable to the global issues (see "2019 Annual Asia Corporate Default And Rating Transition Study," Oct. 13, 2020)

China Leads Global Recovery After Its Rebalancing In 2019

In 2019, China continued to balance its economic growth with the significant increase in its debt burden. On Sept. 30, 2019, S&P Global Ratings affirmed its 'A+' long-term and 'A-1' short-term sovereign credit ratings on China, and the outlook on the long-term ratings is stable as policy changes have helped to rein in credit growth and reduce the reliance of economic growth on public investment (see "China Rating Affirmed At 'A+/A-1'; Outlook Stable").

On June 29, 2020, notwithstanding the COVID-19 pandemic, S&P Global Ratings again affirmed its sovereign credit ratings on China, with a stable outlook, as China is likely to maintain above-average economic growth relative to other middle-income economies in the next few years. However, headwinds are likely to continue from the COVID-19 pandemic, U.S.-China tensions, and ongoing efforts to restructure the economy and reduce financial risks (see "China Ratings Affirmed At 'A+/A-1'; Outlook Stable").

Across regions, we see variation in leverage trends among economies, as well as differences in sovereigns' abilities to absorb debt without a hit to credit quality.

Although China was hit by COVID-19 first, the health and economic effects have been limited compared with the rest of the world. This has led to a comparatively subdued increase in leverage for Asia-Pacific and capped the growth in leverage for emerging markets.

Our global GDP forecast for full-year 2020 is for a contraction of around 4%. The outlook for advanced economies has generally improved on the back of a stronger-than-expected rebound in the third quarter. We have also raised our forecast for China to GDP growth of 2.1% for 2020 and 6.9% for 2021 (see "Global Debt Leverage: Risks Rise, But Near-Term Crisis Unlikely," Oct. 27, 2020).

Rated Issuers In Greater China

At the end of 2019, the share of investment grade in Greater China rose to 71.1% in 2019 from 67.5% in 2018 (see chart 1), compared with 50.1% globally, 42.7% in the U.S., and 56.6% in Europe in 2019. This distinction is particularly important as ratings also correspond strongly to the cost of debt: The higher the rating, the lower the cost of debt.

In Greater China, the percentage of unchanged ratings was 76.2% in 2019 (versus 77.9% in 2018 and 73.5% in 2017). By comparison, the percentage of unchanged ratings globally was 75.6% in 2019, 73.1% in 2018, and 72.1% in 2017.

The new issuers in Greater China in 2019 were predominantly rated in the 'BBB' rating category, which is similar to the trend in 2018. In 2019, the pool of rated issuers for corporations in Greater China shrank by roughly 3%. The pool increased 16% in 2018 and 12% in 2017 year over year.

For 2019, the median rating for Greater China in 2019 was 'BBB' (higher than the global median rating of 'BBB-' in 2019), with a median rating of 'A-' for financial issuers and 'BBB-' for nonfinancial issuers. By comparison, the U.S. median rating in 2019 was 'BB', with a financial median rating of 'A-' and nonfinancial median rating of 'BB-'. The global median ratings were 'BBB+' for financials and 'BB' for nonfinancials.

Downgrades Outpaced Upgrades While Defaults Reached A Peak

In 2019, downgrades (37) outpaced upgrades (25) in Greater China (see chart 2), yielding a downgrade-to-upgrade ratio of 1.5 to 1. The continued trade war between the U.S. and China and other economic headwinds may have contributed to this. By comparison, the downgrade-to-upgrade ratio was 0.7 to 1 in 2018 and an average of 1.9 to 1 during 2000-2019. Financial institutions had a downgrade-to-upgrade of 0.29 to 1 in 2019.

The number of rated issuers in Greater China grew rapidly--from 69 at the beginning of 2000 to 577 by the end of 2019--particularly in the 'A' and 'BBB' categories.

Default counts in Greater China were generally in line with global trends from 2000-2019. Greater China defaults totaled nine in 2019, triple the 2018 total, and reached a peak (for 2000-2019). The prior highest default count was six in 2015.

All of S&P Global Ratings Research's default studies have found a clear correlation between ratings and defaults. Over every time horizon, lower ratings correspond to higher default rates (see chart 3). We found the same is true when examining the data by rating as well as by region. As the Gini ratios show, the ability of corporate ratings to serve as effective measures of relative risk remains intact over time, particularly in low-default years.

For all rated entities in Greater China, the default rate was 1.01% in 2019, higher than 0.39% in 2018 (see table 3). This is similar to the global trend--the global default rate for all rated corporate entities increased to 1.3% in 2019 (with a total of 118 defaults) from 1.03% (with a total of 82 defaults) in the prior year.

The 2019 speculative-grade corporate default rate for Greater China increased to 3.1% from 1.3% in 2018, but was lower than the speculative-grade default rate of 7.2% in 2015. Globally, the speculative-grade corporate default rate also increased, to 2.54% in 2019 from 2.1% in 2018. Similarly, the U.S. speculative-grade corporate default rate rose to 3.11% from 2.42% (see chart 5).

Table 3

Table 4

Table 5

Table 6

Higher Ratings Are Consistent With Fewer Defaults

We generally have observed that higher-rated entities take a longer time to default than lower-rated entities do. We have also found this to be true for issuers in Greater China, using the original rating and last rating as the reference points (see table 7). For example, Greater China entities rated 'B' took an average of 3.5 years to default--less than the average of 5 years to default for 'BB' rated entities. The exception to this pattern for Greater China is in the 'BBB' rating category, which had an average of 3.5 years to default, due to a small sample size of defaulted issuers (just two) that were ever rated in the 'BBB' category.

94.9% of the defaulters from 2000-2019 in Greater China were initially rated speculative grade, and they averaged 3.7 years from initial rating to default. This is a faster rate of deterioration than globally, with an average of 5.8 years from initial rating to default (see table 7). Moreover, we observe the same gap in the average time to default between rated global issuers and rated Greater China issuers in each rating category. The smaller sample size in Greater China (and a relative scarcity of defaults) is largely responsible for this disparity.

Defaults in Greater China total just 39 from 2000-2019 (compared with 2,186 defaults globally from 2000-2019). As a result, idiosyncratic factors could be at play when analyzing Greater China defaulters.

Table 7

Default rates by rating modifier (the plus or minus after the rating) show that lower rating categories historically experience higher default rates on average, though variability is possible in any given year (see table 8). Nevertheless, the data from past default cycles indicate that most defaults stemmed from the lowest ratings.

Table 8

Industry Profile

In 2019, out of the nine defaults (two were publicly rated) in Greater China, six were rated at the beginning of the year. These six defaulters raised the annual corporate default rate for telecommunications and forest and building products/homebuilders to 8.33% each; health care and chemicals to 3.45%; energy and natural resources to 2.04%; and aerospace, automotive, capital goods, and metal to 1.47%. Other sectors' annual corporate default rates were zero. No SOEs defaulted in 2019.

The two publicly rated defaulters in 2019 are Shandong Yuhuang Chemical Co. Ltd. and MIE Holdings Corp. Shandong Yuhuang Chemical is a chemical products company that was rated 'B+' at the start of the year. MIE Holdings Corp. is an oil and gas exploration and production company that was rated 'CCC-' at the beginning of the year.

Nonfinancial entities accounted for all of the defaults in Greater China from 2000-2019. The highest default rate by sector was in aerospace, automotive, capital goods, and metals in 2009, at 37.5% (three out of the eight rated issuers defaulted), followed by the consumer and service sector's 33.3% default rate in 2008. Given the relatively small number of ratings in Greater China, these rates can vary substantially.

In 2019, most sectors (nine out of 13) saw the count of issuers either shrink or remain unchanged from the year before. The biggest drop was in telecommunications, which had a 25% decline in issuers in 2019. Leisure time and media had a 60% increase in count, though there are only eight issuers in this sector as of 2019.

Table 9

Table 10

Transition Tables And Cumulative Default Rates

Analysis of rating transitions in 2019 suggests that ratings behavior in Greater China continues to exhibit consistency with global trends, which have shown a negative correspondence between credit rating and default.

Overall, higher-rated issuers in Greater China showed greater ratings stability in 2019 than speculative-grade issuers. For example, 93.8% of Greater China issuers rated 'AA' at the beginning of 2019 (Jan. 1, 2019) were still rated 'AA' at the end of the year (Dec. 31, 2019), and the comparable share for issuers rated 'B' was only 52.6%. For 'CCC'/'C', it was 45.5%, though caution must be used in interpreting the low stability rates associated with the 'CCC'/'C' rating category in light of the small sample size in Greater China (see table 11).

We also track defaults even after the ratings on the issuers are withdrawn. For example, a hypothetical issuer that is rated in January 2019 undergoes a rating withdrawal in May 2019 and defaults in December 2019 will count in the 2019 trailing-12-month default rate. In the trailing-12-month default rate for June 2019, this hypothetical issuer will not count because it was not rated at the start of the period (June 2019). Please see Appendix I for more details.

Table 11

For average one-year transition rates, the long-term (2000-2019) trends of ratings behavior were similar for Greater China and global rated issuers (see table 12). After one year, the average percent of issuers that retained the same rating were the following for 'AAA', 'AA', and 'B':

• 'AAA': 85.7% for Greater China and 87.03% for global;
• 'AA': 86.6% for Greater China and 87.2% for global; and
• 'B': Only 64.1% for Greater China and 74.8% for global.

Based on the transition analysis for a two-year horizon, lower ratings also tend to display less stability than higher ratings (see table 13). Due to sample sizes, there are deviations from time to time. These deviations have lowered the stability rate for certain rating categories because of the relatively high withdrawal rate of these issuers.

Transitions at the rating modifier also display the same relationship mostly, though differences in sample size occasionally create slight variations between adjacent rating categories (see table 14).

Table 12

Table 13

Table 14

The negative correspondence between ratings and defaults in Greater China holds true over time, as the cumulative average default rates illustrate (see tables 15-16 and chart 7). On average, for 2000-2019, all rated Greater China issuers had a default rate of 0.7% in the first year after they were rated and of 1.3% in the second year. Issuers rated in the 'BB' category had a default rate of 0.46% in the first year after they were rated and 0.85% in the second year. Issuers rated in the 'B' category had a default rate of 2.19%, on average, in the first year and 5.4% in the second.

The stability rates of 'B-' rated entities are lower than those of both 'B' and 'CCC'/'C' rated entities because of relatively higher withdrawals and transitions to not rated. The cumulative average default rates in the 'CCC'/'C' rating category are repeated in years two to 10 because of a limited sample size and a limited number of 10-year periods in 2000-2019 in Greater China (ten 10-year samples), compared with a larger sample of 1981-2019 globally (29 10-year samples).

Table 15

The default rates in the 'CCC'/'C' category are much higher than those in the higher rating categories. The relatively small sample sizes of some rating categories in Greater China may add volatility to default rates and, at times, cause them to deviate from both global trends as well as expectations, as is the case in the 'BB-' through 'B' rating categories (see table 16).

Table 16

Gini Ratios And Lorenz Curves

A quantitative measure of ratings performance indicates that the relative rank ordering of ratings in Greater China is consistent across various time horizons. To measure ratings performance, or ratings accuracy, the cumulative share of issuers by rating is plotted against the cumulative share of defaulters in a Lorenz curve to render the accuracy of their rank ordering visually. For definitions and methodology, refer to Appendix III.

Our calculations indicate that the one-year transition to default in Greater China shows an average one-year Gini coefficient of 89.08%, a three-year Gini of 83.73%, and a five-year Gini of 76.13% (see table 1). For Greater China, where SOEs play a critical role in the Chinese economy, we also differentiated the Gini by SOE and non-SOE: The average one-year SOE Gini coefficient was 99.06%, and the average one-year non-SOE Gini was 85.49% (see table 2). These figures illustrate that SOE issuers show comparatively stronger rank ordering of ratings with respect to defaults, while non-SOE issuers follow global norms, albeit with slightly higher ratings performance.

If corporate ratings only randomly approximated default risk, the Gini coefficient would be zero. On the other hand, if corporate ratings were perfectly rank ordered so that all defaults occurred only among the lowest-rated entities, the Lorenz curve would capture all of the area on chart 11 above the diagonal, and its Gini coefficient would be 1.

As expected, the Gini coefficients decline as the time horizon lengthens because longer time horizons allow for more credit degradation among higher-rated entities. In the one-year Greater China Lorenz curve, for example, 100% of defaults occurred in the speculative-grade category, while speculative-grade ratings constituted only 33.48% of all Greater China corporate issuers (see chart 8). The three-year Lorenz curve shows that speculative-grade issuers constituted 98.21% of defaulters and 34.06% of the entire sample (see chart 9). The five-year Lorenz curve shows that speculative-grade issuers constituted 97.22% of defaulters and only 36.63% of the entire sample (see chart 10). If the rank ordering of ratings had little predictive value, the cumulative share of defaulting corporate entities and the cumulative share of all entities would be nearly the same.

Appendix I: 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 tables and charts in this study are the direct output of the CreditPro® application, while others are based on 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, its statutory preferences, or the legality and enforceability of the obligation. It is not necessary for a company to have rated debt to have an issuer credit rating.

Although the ratings on a company's very senior forms of secured debt, particularly ones with strong covenants, could occasionally be higher than the issuer credit rating on the company, specific issues are typically rated as high as or lower than these issuer ratings, depending on their relative priority within the company's debt structure. If they are speculative grade, issuer credit ratings are generally two notches higher than subordinated debt ratings. Otherwise, they are generally one notch higher. Therefore, although a 'BB+' issuer credit rating is generally paired with a 'BB-' subordinated debt rating, an 'AA' issuer credit rating usually corresponds to a 'AA-' subordinated rating.

S&P Global Ratings Research's 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 back to Dec. 31, 1980. Therefore, each annual default study is self-contained and effectively supersedes all previous versions.

Issuers included in this study

This study analyzes the rating histories of 1,001 Greater China entities that S&P Global Ratings rated from Jan. 1, 2000, through Dec. 31, 2019. These include industrials, utilities, financial institutions, and insurance entities with long-term local currency ratings. The analysis excludes public information ("pi") ratings and ratings based on the guarantee of another company. 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.

To avoid overcounting, 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 entities 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 that 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 is 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.

We deem 'D', 'SD', and 'R' issuer credit 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.  S&P Global Ratings Research conducts its 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 that the CreditPro® database covers. Each static pool is followed from that point forward. All entities included in the study are assigned to one or more static pools. When an issuer defaults, we assign that default back 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. This is to ensure that default rates account for rating migration and to allow for default rates to be calculated across multi-period 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 may calculate default rates using only the most recent year's default and rating data, which may 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 because the criteria for inclusion or exclusion of entities 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 new update revises results back to the same starting date so as to avoid continuity problems.

Entities that have had ratings withdrawn--that is, revised to not rated--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 entities 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 2000 static pool consists of all entities rated as of 12:01 a.m. on Jan. 1, 2000. Adding those entities first rated in 2000 to the surviving members of the 2000 static pool forms the 2001 static pool. All rating changes that took place are reflected in the newly formed 2001 static pool through the ratings on these entities as of 12:01 a.m. on Jan. 1, 2001. We used the same method to form static pools for 2000 through 2019.

Consider the following example: An issuer is originally rated 'BB' in mid-2006 and is downgraded to 'B' in 2008. This is followed by a rating withdrawal in 2010 and a default in 2013. We would include this hypothetical company in the 2007 and 2008 pools with the 'BB' rating, which was the rating on the issuer at the beginning of those years. Likewise, it would be included in the 2009 and 2010 pools with the 'B' rating. It would not be part of the 2006 pool because it was not rated as of the first day of that year, and it would not be included in any pool after the last day of 2010 because the rating had been withdrawn by then. Yet each of the four pools in which this company was included (2007-2010) would record its 2013 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 16 years covered by the study.

Issuer-weighted default rates.  All default rates that appear in this study are 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 utilize 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.  We derived cumulative default rates that average the experience of all static pools by calculating marginal default rates, conditional on survival (survivors being nondefaulters), for each possible time horizon and for each static pool, weight-averaging the conditional marginal default rates, and accumulating the average conditional marginal default rates. Conditional default rates are calculated 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. Weights are based on the number of issuers in each static pool. Cumulative default rates are one minus the product of the proportion of survivors (nondefaulters).

For instance, the hypothetical weighted-average first-year default rate for 'B' rated entities in Greater China for all 20 pools was 2%, meaning that an average of 98% survived one year. Similarly, the second- and third-year conditional marginal averages were 3% for the first 19 pools (98% of those entities that did not default in the first year survived the second year) and 3% for the first 18 pools (97% of those entities that did not default by the second year survived the third year). Multiplying 97.81% by 96.75% results in a 94.6% survival rate to the end of the second year, which yields a two-year cumulative average default rate of 5.4%. Multiplying 97.41% by 94.64% results in a 92.19% survival rate to the end of the third year, which yields a three-year cumulative average default rate of 7.81%.

Time sample.  This update limits the reporting of default rates in Greater China to the 20-year time horizon, and we based all calculations on the rating experience of that period. Global data is based on a 39-year time horizon. The maturities of most obligations are much shorter than 20 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' credit rating standards have been consistent over time. Adverse business conditions tend to coincide with default upswings for all pools. These upswings have hit speculative-grade issuers the hardest, but investment-grade default rates also increase during stressful periods.

Transition analysis

Transition rates compare issuer credit ratings at the beginning of a period with 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. An issuer that remained rated for more than one year was counted as many times as the number of years it was rated. For instance, an issuer continually rated from the middle of 2004 to the middle of 2011 would appear in the six consecutive one-year transition matrices from 2005-2010. All 2003 static pool members still rated on Dec. 31, 2015, had 12 one-year transitions, while entities first rated between Jan. 1, 2015, and Dec. 31, 2015, had only one.

Each one-year transition matrix displays all rating movements between letter categories 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." For instance, the first panel of table 11, which corresponds to the 2019 static pool, shows that out of all 'BBB' rated entities at the beginning of that year, 90.70% were rated the same at year-end, while S&P Global Ratings had upgraded 2.33% to 'A', and so on.

Average one-year transition matrices were calculated on the basis of the one-year transition matrix just described. 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.

Multiyear transitions.  We also calculated multiyear transitions for periods of two 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, average two-year transition matrices were the result of comparing ratings at the beginning of the years 2000-2018 with the ratings at the end of the years 2001-2019 (see table 13). Otherwise, the methodology was identical to that used for single-year transitions.

Comparing transition rates with default rates.  For more information on the differences between transition rates and default rates, please see the section "Comparing transition rates with default rates" in Appendix I of the "2019 Annual Global Corporate Default Study."

Appendix II: Additional Tables

Table 17

Table 18

Table 19

Table 20

Table 21

Table 22

Table 23

Appendix III: 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. The Lorenz curve was developed by Max O. Lorenz as a graphical representation of the proportionality of a distribution.

To build the Lorenz curve, the observations are ordered 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. Their Gini coefficient--which is a summary statistic of the Lorenz curve--would thus 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 its Gini coefficient would be 1 (see chart 11).

The procedure for calculating the Gini coefficients is to divide 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.

Appendix IV: Defaults In Profile

In 2019, nine companies, including seven confidential issuers, in Greater China defaulted on a total of $800 million of debt. Of the two publicly rated defaulters, Shandong Yuhuang Chemical Co. Ltd. had $300 million in outstanding debt and MIE Holdings Corp. had $500 million in outstanding debt. The seven confidential issuers had no cross-border U.S. dollar issuance outstanding in 2019.

MIE Holdings Corp.

• US$500 million 7.50% notes due April 25, 2019
• US$248.39 million 13.75% senior unsecured notes due March 26, 2022
• HK$340 million 5.00% notes due Jan. 26, 2021

On April 12, 2019, S&P Global Ratings lowered its issuer credit rating on Cayman Islands-based oil and gas exploration and production company MIE Holdings Corp. to 'SD' from 'CC' after MIE completed an exchange offer for its senior unsecured notes due April 25, 2019. We view the transaction as a distressed exchange because investors will receive significantly less than what they were promised for the original securities on the maturity date.

Subsequently, on April 25, 2019, we raised our issuer credit rating on the company to 'CCC-' from 'SD' upon completion of its exchange offer. This reflects our view that the company's nonrepayment risk in the next six months remains high due to the material liquidity deficit. MIE's capital structure is still unsustainable, despite the recent completion of its exchange offer.

Later, on May 14, 2019, the ratings on the company were withdrawn.

Table 24

Shandong Yuhuang Chemical Co. Ltd.

• US$300 million 6.625% notes due March 27, 2020
• CNY500 million 7.00% unsecured notes due Dec. 12, 2021
• CNY500 million 6.00% unsecured notes due Nov. 21, 2021

On Nov. 29, 2019, S&P Global Ratings lowered its issuer credit rating on Chinese chemical products company Shandong Yuhuang Chemical Co., Ltd. to 'D from 'CC' as the company failed to repay its RMB500 million onshore bond within the five-business-day imputed grace period.

Earlier, on June 14, 2019, S&P Global Ratings lowered its issuer credit rating on the company to 'B-' from 'B+' owing to high refinancing and liquidity risks. Subsequently, on Sept. 5, 2019, the rating was lowered to 'CCC+' and then to 'CC' on Nov. 22, 2019.

Later, on Jan. 9, 2020, S&P Global Ratings withdrew its issuer credit rating on the company.

Table 25

Related Research

• 2019 Annual Global Corporate Default And Rating Transition Study, April 29, 2020
• 2019 Annual Asia Corporate Default And Rating Transition Study, Oct. 13, 2020
• 2018 Annual Greater China Corporate Default And Rating Transition Study, Sept. 2, 2019