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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 need to be adjusted for size, based on the relative size of the banks' revenues, 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 operational loss is generally documented much less rigorously than credit losses, and regulatory initiatives are pushing banks to document operational loss data, making it less reliable to estimate relevant probabilities using loss information published by credit rating agencies.
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In the context of operational risk management within a banking institution, estimating the loss severity distribution is crucial for assessing potential financial impacts due to operational risk events. However, operational risk analysts frequently encounter the challenge of limited historical loss data, which complicates accurate estimation. Given this scenario, what is the most effective methodology that an operational risk analyst can employ to estimate a bank's loss severity distribution despite the constraint of having insufficient historical operational risk loss data?
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.