Many commentators have suggested that firms need to do stress tests to examine their vulnerability to adverse situations that are not within the data set used to parameterize their risk models. In the article linked below, I suggest the adoption of a terminology to describe stress tests and also a methodology that can be adopted by any risk model user to test and
communicate a test of the stability of model results. This method can be called a Black Swan test. The terminology would be to set one Black Swan equal to the most adverse data point. A one Black Swan stress test would be a test of a repeat of the worst event in the data set. A two Black Swan stress test would
be a test of experience twice as adverse as the worst data point.
So for credit losses for a certain class of bonds, if the historical period worst loss was 2 percent, then a 1BLS stress test would be a 2 percent loss, a 4 percent loss a 2BLS stress test, etc.
Further, the company could state their resiliency in terms of Black Swans. For example:
Tests show that the company can withstand a 3.5BLS stress test for credit and a 4.2BLS for equity risk and a simultaneous 1.7BLS credit and equity stress.
Similar terminology could be used to describe a test of model stability. A 1BLS model stability test would be performed by adding a single additional point to the data used to parameterize the model. So a 1BLS model stability test would involve adding a single data point equal to the worst point in the data set. A 2BLS test would be adding a data point that is twice as bad as the worst point.