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Answer: Merge external data from other banks with the bank's internal data after making appropriate scale adjustments.
D is correct. Using external data obtained from other banks is one good way to increase the data set of historical operational losses. Data from other banks needs to be adjusted for size before being merged with the bank's internal data. A is incorrect because using distributions does not help resolve the issue of incomplete underlying data. B is incorrect as Lognormal distributions, not Poisson distributions, are generally used for modeling loss severity. Also, using distributions does not help resolve the issue of incomplete underlying data. C is incorrect because credit losses are generally much better documented than operational losses inside the bank. External credit ratings publish probability of default and expected loss data that provides additional data. Operational loss is generally documented much less rigorously, and regulatory initiatives are now pushing banks to document operational loss data.
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In the realm of operational risk analysis, where historical data on operational risk losses is limited, what is the most effective strategy for an analyst to adopt in order to estimate a bank's loss severity distribution accurately?
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.