Static Angel Risk
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Static Angel Risk
A report that clearly prevents a serious security problem is a different story entirely and increases appetite. To succeed, therefore, analysis should embody the DevSecOps principle that security effort be grounded in actual and not speculative risk.
The Microsoft Security Response Center (MSRC) recently described a security review to me that integrated static analysis as a central component for the cold path (#2 above). It was striking how things lined up to make this deployment of static analysis an unqualified success.
As the Gini ratios show, corporate ratings also serve as effective measures of relative risk over time, particularly in low-default years. Many default studies, including this one, also look at transition rates, which gauge the degree to which ratings change--either up or down--over a particular period. Transition studies have repeatedly confirmed that higher ratings tend to be more stable and that speculative-grade ratings ('BB+' or lower) generally experience more volatility over a given time frame.
In table 13, the times to default are from the date that each entity received each unique rating in its path to default. In contrast, table 21 reports transition-to-default rates using the static pool methodology, which calculates movements to default from the beginning of each static pool year. This usually leads to shorter time frames from which to calculate default statistics. Data provided in table 13 also differ from default rates in table 24 owing to the use of the static pool methodology. (For more information on methodologies and definitions, see Appendix I.)
Since 1981, the 'B' rating category has accounted for 1,735 defaults (56% of the total from initial rating), well more than double the number of defaulters from the 'BB' category (see tables 10 and 12). Given this track record, monitoring the trends of newly assigned ratings could prove useful in anticipating future default activity, based on the observation that years with high numbers of new 'B-' and lower ratings will likely be followed by increased default risk.
Despite a rising default rate in 2020 (see chart 21), risk tolerance among lenders has remained near the post-financial crisis high. The share of newly assigned issuer credit ratings that are speculative grade has remained elevated in 2020: 78% of newly assigned issuer credit ratings globally were speculative grade. This is roughly in line with the annual average since 2010, which is 76.7%.
Over the long term (1981-2020), heightened ratings stability is broadly consistent with higher ratings (see table 21). A key consideration when analyzing transition matrices that present averages computed over multiple static pools is that the standard deviations associated with each transition point in the matrix are large relative to the averages (outside of stability rates).
The only exceptions to the correspondence between lower ratings and higher default rates occur when the number of defaults is low or when the underlying number of issuers is very small--such as at the rating modifier level among the higher rating categories (see table 26). Investment-grade-rated issuers seldom default, so the number of defaults among these rating categories is particularly low. This small sample size can, at times, result in historical default rates that seem counterintuitive. These default rates do not imply, however, that 'AAA' rated companies are riskier than 'AA+' rated companies, for example, but rather that both are highly unlikely to default.
A quantitative analysis of the performance of S&P Global Ratings' corporate ratings shows that they continue to correlate with default risk across several time horizons. As one measure of ratings performance, the cumulative share of defaulters was plotted against the cumulative share of issuers by rating in a Lorenz curve to visually render the accuracy of its rank ordering (for definitions and methodology, refer to Appendix II). Over the long term, the global weighted average Gini coefficient was 82.8% over the one-year horizon, 75.3% over three years, 71.5% over five years, and 69.2% over seven years (see table 27). If the rank ordering of ratings had little predictive value, the cumulative share of defaulting corporate entities and the cumulative share of all entities at each rating would be nearly the same, producing a Gini ratio of zero.
To avoid overcounting, we exclude 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.
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 (for example, by rating category) at the beginning of each year, quarter, or month that the database covers. 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 allowing for default rates to 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 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 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 back to the same starting date of Dec. 31, 1980, so as 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 include only 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 the entity in that static pool as a defaulter and categorize it in the rating category of which it was a member at that time.
For instance, the 1981 static pool consists of all companies rated as of 12:00:01 a.m. on Jan. 1, 1981. Adding those companies first rated in 1981 to the surviving members (those still actively rated and not in default) of the 1981 static pool forms the 1982 static pool. All rating changes that took place are reflected in the newly formed 1982 static pool through the ratings on these entities as of 12:00:01 a.m. on Jan. 1, 1982. We used the same method to form static pools for 1983-2020. From Jan. 1, 1981-Dec. 31, 2020, a total of 21,693 first-time-rated organizations were added to form new static pools, while we excluded 3,098 defaulting companies and 11,448 companies that are no longer assigned ratings (NR).
Default rate calculation. We calculated annual default rates for each static pool, first in units and later as percentages with respect to the number of issuers in each rating category. We combined these percentages to obtain cumulative default rates for the 40 years the study covers (see tables 24-26 and 30-32).
Average cumulative default rate calculation. 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 (see tables 24-26 and 30-32). We calculated 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. 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).
All 1981 static pool members still rated on Jan. 1, 2020, had 40 one-year transitions, while companies first rated on Jan. 1, 2020, had only one. Table 29 displays the summary of one-year transitions in the investment-grade and speculative-grade rating categories. 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 (see table 22). 041b061a72