Where do your rates come from?
Turn your default and loss history into model assumptions. Choose a method below, then replace the example with your own observations.
What happened, by grade
Best grade at the top. The order is the model — each grade's estimate is taken over itself and everything worse than it.
| Grade | Borrowers | Defaults | Observed rate | |
|---|---|---|---|---|
| 0.00% | ||||
| 0.00% | ||||
| 1.11% | ||||
| 6.32% |
These are example figures, not your data — a book where the two best grades have never had a default. Type over them.
Your probabilities of default
An upper confidence bound, not a best estimate. Deliberately conservative, and describe it that way wherever it reaches a disclosure.
| Grade | Observed | Borrowers used | Defaults used | Default rate to use |
|---|---|---|---|---|
| A | 0.00% | 1,005 | 8 | 1.290% |
| BBB | 0.00% | 585 | 8 | 2.212% |
| BB | 1.11% | 275 | 8 | 4.682% |
| B | 6.32% | 95 | 6 | 10.823% |
Each grade is measured over itself and every worse grade — that is why the borrower counts fall as you go down. A grade with no defaults of its own cannot be measured on its own, but a scale ordered by credit quality says its rate is at most that of everything below it, and that is enough. It also keeps a small top grade from coming out riskier than the rest of the book, which an ungrouped calculation does routinely.
Put these rates into the model
Written into one sector's default rates by grade, with this calculation recorded as where they came from. It becomes a draft change you can review and undo like any other.
These are still the shipped example figures. Type your own default history into the table above and this becomes available — demonstration numbers must not enter a model under a basis that calls them your experience.