# Statistics

# Statistical Significance of the A- Issuer Rating for Greensill Bank

Agencies, Statistics, Symbols## Instead of a low default risk, other reasons may have been decisive for the issuance of the extraordinarily good issuer rating.

Usually the question of whether a credit rating is right or wrong cannot simply be answered with yes or no. Only a few cases on the capital market are so-called nobrainers, which would immediately reveal a mistake. The occurrence of bankruptcy or default despite a good credit rating is by no means proof that the rating was wrong. Scientifically, the proof can only be made on the basis of the assumption of a certain distribution using a hypothesis test. Usually this requires a comparatively large number of cases. This is especially true when the probability of the occurrence of the event to be tested is very low.

According to the central limit theorem of statistics, the more cases can be observed, the more accurate the result. In probability theory, the central limit theorem establishes that, in many situations, when independent random variables are added, their properly normalized sum tends toward a normal distribution even if the original variables themselves are not normally distributed. The Scope Ratings scandal surrounding the issuer rating of the Greensill Bank can also be analyzed from this point of view.

At the time of Scope Ratings’ first isser rating on Greensill Bank AG on July 19, 2019, the bank was a German factoring bank based in Bremen. The bank was a 100% subsidiary of privately held Greensill Capital Pty Ltd (“Greensill”). The rating of Greensill Bank allegedly reflected the bank’s capitalisation and its high degree of integration with the Greensill group. The assets of Greensill Bank consisted predominantly of trade receivables from factoring and reverse factoring transactions originated by the Greensill group. The Greensill group had grown strongly in recent years, competing with major global banks as a specialised non-bank provider of supply chain finance (“SCF”). “The group had also attracted more than US$ 1 billion external investment”, admitted Scope Ratings.

**Scope Ratings’ Issuer Rating for Greensill Bank AG**

The following credit rating was assigned by Scope Ratings on July 19, 2019: “Issuer Rating of A-. The rating has a Stable Outlook.”

A number of Scope Ratings’ credit rating methodologies for various sectors make reference to Scope Ratings’ idealised expected loss and default probability tables. These tables are provided at the discretion of Scope Ratings. “Users of these tables should refer to Scope Ratings’ specific Credit Rating Methodologies to ensure all analytical considerations are addressed. These tables should only be used in conjunction with such Credit Rating Methodologies”, warns Scope Ratings:

Such tables are required for carrying out ratings in the area of structured finance. An explanation of these tables was also published in the year of the publication of the issuer rating for Greensill Bank (see Scope Ratings’ document “Idealised expected loss and default probability tables explained”).

When deriving the table, Scope Ratings uses data from the leading US credit rating agencies. However, there is still no evidence as to whether the conditions of these credit rating agencies with their experience of an entire century can also be transferred to the credit ratings of Scope Ratings.

This is not actual historical data. Rather, the tables are intended to help the investor understand the risk associated with a particular rating level. Scope’s idealised default probability table shows the maximum default probability reference that is generally consistent with a given rating level over a given risk horizon. The risk horizon is expressed in years.

Ratings are the universal expression for risk over a specific time horizon. Accordingly, with the issuer rating of A- given for the Greensill Bank, the investor could expect that issuers assessed in this way are on average after 30 years with a probability of 12.65 % in default. In other words, one eighth of the banks rated at this level are in financial difficulties after 30 years.

In the short term, however, the risk should be much lower than the risk after three decades. This is also shown consistently in Scope Ratings’ idealized table. Within two years the probability should only be 0.16 percent.

Scope Ratings had downgraded the issuer ratings from A- to BBB+ on Greensill Bank on September 17, 2020. Half a year before the default occurred, creditors could assume a very good risk, especially since the downgraded rating was still clearly in the investment grade range. In this respect, the following calculations could be carried out with the even lower risk of 0.07 percent resulting from the table above.

How low the short-term probability of a failure should be under these conditions cannot be intuitively grasped by looking at the graph or at the table above. To calculate the number of banks among which one single bank defaults when the probability of default for each A- rated bank of the group of A- rated banks is 0.16 percent, you divide 100 by 0.16% or 1 by 0.0016. This calculation results in the number 625.

According to Scope Ratings A- rating for Greensill Bank and taking into account the idealized default rates used in Structured Finance, the probability of the Greensill Bank failing within two years should be 1 in 625 (= 0.16 %).

Calculations carried out with the even lower risk of 0.07%, if you take into account the fact that at the beginning of September 2020 an investor could only see an issuer rating of A- for the Greensill Bank on the website of Scope Ratings, the probability of the Greensill Bank failing within the following year should be 1 in 1.429 (= 0.07 %). Regardless of the assumption made with regard to the time horizon, the default probability was given as very low and therefore the A- issuer rating had to encourage investors to take the risk.

It is according to Scope Ratings’ A- Issuer Rating for Greensill Bank highly unlikely that Greensill Bank, the bank of the shareholder and member of the advisory board of Scope, who is also chairman of Greensill Bank’s supervisory board, would default after only two years. Therefore it cannot be ruled out that reasons other than the actual credit risk of default were decisive for the provision of this remarkable good issuer rating for Greensill Bank.

# From Money Substitute to Lottery Substitute

Statistics## The average Bitcoin holding time for investors is estimated 3.1 years.

However, as a new Handelskontor infographic shows, there has recently been a change: More and more traders are appearing, while the relative proportion of long-term investors is falling.

According to research done by Handelskontor, at the beginning of March there were 5.41 million Bitcoin traders – more precisely, so many BTC addresses where Bitcoins were held for less than a month. In November of last year there were only 3.56 million.

The effect shown can be due to an increased use of cryptocurrency as a means of payment, as well as to increasing trading for speculative intentions. Judging by the advertising in the social media, however, when buying Bitcoin, the focus is on speculation about quick wins, and the payment function is hardly mentioned.

The addresses on which the Bitcoins are held for at least 12 months and are not transferred make up an ever smaller relative share of this popluation. This fell within the last 5 months from 64.79 to 58.88 percent.

In the social media these days, optimistic comments on the subject of Bitcoin predominate. In the last 7 days, 110,042 tweets with a positive connotation regarding the development of the crypto currency were posted, whereas the number of negative comments only amounts to 26,253. The vast majority, however, were neutral. This could be an indication that – in spite of all the highs – there is still no euphoria.

Google data shows that the demand for Bitcoin is extremely high, but that altcoins are also increasingly in demand. The search volumes for the crypto currencies Ethereum and IOTA also recently reached new highs.

# Israel and UAE lead the field

Statistics## Without a vaccination and without a face mask, the risks remain incalculable.

Israel is leading the race to reach the 60-70 percent threshold needed to suppress the spread of Covid-19 among the general population. Second-placed UAE’s 60.8 doses per 100 inhabitants and the UK’s 30.13 doses per 100 of its citizens are ahead of the United States, where the rate of vaccination stands at 21.77 jabs for every 100 people.

In the Federal Republic of Germany it has not been possible to achieve approximately the same level of protection for the population. Therefore, the pandemic is expected to continue to spread. The lack of success of the measures taken will therefore continue to burden the risk situation of many companies.

You will find more infographics at Statistafor most people in Germany there is no short-term vaccination appointment available. Therefore, protection with face masks is essential.

# Rating Evidence on the Effectiveness of Vaccines

Statistics## ‘CO’ stands for corona, ‘VI’ for virus, and ‘D’ for disease.

There is a lot of data circulating about the effectiveness of vaccines against the coronavirus. Even experts can skid when they have to translate the numbers from scientific studies into concrete, understandable information. Explaining the effectiveness numbers and their meaning is not easy.

The vaccines are effective, but how effective exactly? Especially when it comes to vaccination to prevent diseases with COVID-19, it is important to be absolutely clear about its effectiveness and effectiveness in order to counter doubts and questions with clear information.

Say the effectiveness of the vaccine on the basis of messenger ribonucleic acid is given as 94%. This number shows how many cases of symptomatic COVID-19 illnesses are prevented by the vaccinations. This is calculated with 100 × (1 – disease rate with vaccine / disease rate with placebo). This becomes concrete if one looks at a population group that is similar to the group examined in the clinical study. A cumulative COVID-19 disease rate over a period of 3 months is considered, which is, for example, around 1% without vaccine, as was seen in the placebo arms of the vaccination studies.

With vaccine, 94% of these diseases that would otherwise occur would not occur. So actually only about 0.06% of people vaccinated would get COVID-19. On the other hand, it is commonly interpreted that 94% effectiveness means that 6% of all vaccinated people still get sick – versus 0.06% sick people with the correct meaning.

Then what does 94% mean? Misunderstood 6000 patients out of 100 000, actually only 60 patients. This precise description of the findings from the vaccination studies is also important for further predictions, for example when it comes to risk reduction in populations that are more exposed, exposed to higher numbers of infections or have an increased risk of disease.