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Should Risk Managers Rely on Maximum Likelihood Estimation Method While Quantifying Operational Risk?
Company: Federal Reserve Bank of Richmond
Year Of Publication: 2008
Month Of Publication: February
Pages: 23
Download Count: 14
View Count: 1661
Comment Num: 0
Language: English
Source: working paper
Who Can Read: Free
Date: 6-4-2010
Publisher: Administrator
Summary
The paper compares the performance of four estimation methods, including the maximum likelihood estimation,
that can be used in fitting operational risk models to historically available loss data. The other
competing methods are based on minimizing different types of measure of the distance between empirical
and tting loss distributions. These measures are the Cramer-von Mises statistic, the Anderson-Darling
statistic, and a measure of the distance between the quantiles of empirical and fitting distributions. We
call the last method the quantile distance method. Our simulation exercise shows that the quantile distance
method is superior to the other three methods especially when loss data sets are relatively small and/or the
fitting model is misspecied.
Author(s)
Ergashev, Bakhodir Sign in to follow this author
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