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Answer: It is suitable for modeling the tail of the operational loss distribution, but not for modeling the body of the distribution.
## Explanation The correct answer is **D** because the power law is particularly useful for modeling the **tail** of operational loss distributions, where extreme events occur. ### Key Points: - **Power Law Characteristics**: Power laws describe distributions where large events (tail events) occur more frequently than would be predicted by normal distributions. This makes them suitable for modeling extreme operational losses. - **Body vs. Tail**: The "body" of the distribution (frequent, smaller losses) is often better modeled by other distributions like lognormal or Poisson distributions. The power law specifically captures the heavy-tailed behavior. - **Why Other Options Are Incorrect**: - **A**: Incorrect - Power laws imply heavy-tailed distributions, not normal distributions which have thin tails. - **B**: Incorrect - While power laws can model fraud losses, they are also applicable to natural disasters and other extreme events. The statement about being "more effective" for some types than others is not accurate. - **C**: Incorrect - Power laws are used for extreme, low-frequency losses, not routine high-frequency losses. ### Practical Application: In operational risk management, power laws help quantify the risk of rare but catastrophic events, which is crucial for capital allocation and risk mitigation strategies.
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An operational risk manager is presenting to a group of risk analysts about different techniques to model operational risk. An analyst asks the manager about the appropriate use of the power law in estimating operational losses. Which of the following would be a correct statement for the manager to make about the use of the power law?
A
It implies that operational losses tend to follow a normal distribution.
B
It is more effective in modeling some types of operational risk, such as losses from fraud, than others, such as losses from natural disasters.
C
It is generally used to estimate routine operational losses which occur at a relatively high frequency.
D
It is suitable for modeling the tail of the operational loss distribution, but not for modeling the body of the distribution.