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An Application of Bayesian Inference on the Modeling and Estimation of Operational Risk Using Banking Loss Data
Company: Applied Mathematics
Company Url: Click here to open
Year Of Publication: 2014
Month Of Publication: April
Resource Link: Click here to open
Pages: 862-876
Download Count: 0
View Count: 1656
Comment Num: 0
Language: English
Source: article
Who Can Read: Free
Date: 4-6-2014
Publisher: Administrator
Bank internal and external data are divided into defined loss cells and then fitted into probability distributions. The distribution parameters and their uncertainties are estimated from posterior distributions derived using the Bayesian inference. Loss frequency is fitted into Poisson distributions. While the Poisson parameters, in a similar way, are defined by a posterior distribution developed using Bayesian inference. Bank operation loss typically has some low frequency but high magnitude loss data. These heavy tail low frequency loss data are divided into several buckets where the bucket frequencies are defined by the experts. A probability distribution, as defined by the internal and external data, is used for these data. A Poisson distribution is used for the bucket frequencies. However instead of using any distribution of the Poisson parameters, point estimations are used. Monte Carlo simulation is then carried out to calculate the capital charge of the internal as well as the heavy tail high profile low frequency losses.
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Rahman, Kashfia N. Sign in to follow this author
Black, Dennis A. Sign in to follow this author
McDonald, Gary C. Sign in to follow this author
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