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Answer: Merge external data from other banks with the bank's internal data after making appropriate scale adjustments.
## Explanation When dealing with limited historical data for operational risk loss severity estimation, the most appropriate approach is to supplement the internal data with external data from other banks, after making appropriate scale adjustments. **Why Option D is correct:** - **External data supplementation**: Operational risk events are relatively rare, so internal data alone is often insufficient for robust statistical modeling - **Scale adjustments**: External data needs to be scaled to account for differences in bank size, business mix, and risk profile - **Industry practice**: This approach is widely used in operational risk management and is recommended by regulatory frameworks like Basel II/III **Why other options are incorrect:** - **Option A**: Monte Carlo simulation generates synthetic data but doesn't address the fundamental lack of real loss observations - **Option B**: Poisson distribution is typically used for loss frequency modeling, not loss severity modeling - **Option C**: Credit rating agencies primarily focus on credit risk information, not operational risk loss data This approach helps create a more comprehensive dataset for estimating the loss severity distribution while maintaining relevance to the specific bank's characteristics.
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An operational risk analyst is attempting to estimate a bank's loss severity distribution. However, there is a limited amount of historical data on operational risk losses. Which of the following is the best way to address this issue?
A
Generate additional data using Monte Carlo simulation and merge it with the bank's internal historical data.
B
Estimate the parameters of a Poisson distribution to model the loss severity of operational losses.
C
Estimate relevant probabilities using loss information that is published by credit rating agencies.
D
Merge external data from other banks with the bank's internal data after making appropriate scale adjustments.