# SOA Monograph- A New Approach to Managing Operational Risk - Ch.8

#1

• Goals of Measuring/Assessing Operational Risk
• Actuarial Approach
• Data Requirements
• Frequency and Severity Distriubtions
• How to Model Frequency
• How to Model Severity
• Combining Internal and External Loss Data
• Risk Assessment, Scenario Analysis and Stress Testing
• Calculating Value at Risk
• Modeling the Operational Risk Component of Other Risks

SOA Monograph- A New Approach to Managing Operational Risk Ch. 8
#2

Hi all,

I have a question about āExhibit 8.7 ā Risk Assessment Under Modern ORMā. Can you confirm me that in the table, the last column ā1 in N years Outputā is the result after the LEC was fitted to a normal distribution?

Thanks,

Tinou

#3

Good question. The notes indicate that they are fitting a log-normal distribution to the LEC. I canāt seem to reproduce the same percentiles with the given mu and sigma though. Was anyone else able to get the correct output results given the parameters indicated?

#4

Thatās correct. The output column is produced by the model. They donāt explain how to actually fit it, and itās probably proprietary since there is a āpatent pending.ā

#5

It says that you should use a:

• Poisson distribution when there is no variability in the frequency relative to the mean
• Negative Binomial distribution when there is excessive variability in the frequency relative to the mean
• Binomial when there is very low variability in the frequency relative to the mean

I think I can make sense of the Poisson and Binomial examples, but I am struggling with the Negative Binomial. Can we talk through some examples of operational risks whose frequency follow the three different distributions?

#6

Iāll give binomial distribution a shot

One type of operational risk is process risk. So letās say we have a conveyor belt that puts coke caps on coke bottles. Letās say it it does it 30 bottles per second.

Letās say that on average, every 1000th bottle has an issue. It may be the 998th bottle, or the 1013th bottle, but itās always around the 1000th bottle.

I would say that this follows a binomial distribution because the risk occurs not on every 1000th bottle on the dot, but AROUND the 1000th bottle (low variability).

Does this sounds reasonable?

I canāt really think of an example where thereās no frequency relative to the mean - If thatās the case wouldnāt we know exactly when the risk will occur (ex/ every 1000th bottle on the dot), meaning we can just exercise an action to stop the risk from occurring?