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- W2149018897 abstract "The goal of this thesis is to investigate whether it is possible to constructan advanced measurement approach (AMA) model for operationalrisk when the number of internal data points are very scarce.An AMA model should combine internal data, external data, scenariodata, and business environment and internal control factors to give aone year VaR estimate with 99.9 % confidence of operational risk. Outof the methods of combining the different data sources suggested in theliterature, only the Bayesian inference approach is suitable due to thesmall amount of data available. In order to not be restricted to suitableconjugate-pairs, a numerical approach to evaluating the posteriordistributions is undertaken, and three different severity distributionsare tried out. The distributions tried are the Weibull; the generalizedChampernowne, which is suggested by the literature due to itstail behavior; and the g-and-h, which is suggested by the literaturedue to both its versatility and tail behavior. The conclusion of thisthesis is that it is possible to construct an AMA model with Poissonloss frequencies using Bayesian inference to combine the different datasources. However, the data material was too scarce to draw any reliableconclusions about the severity distribution." @default.
- W2149018897 created "2016-06-24" @default.
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- W2149018897 date "2015-01-01" @default.
- W2149018897 modified "2023-09-27" @default.
- W2149018897 title "A Bayesian Approach to Modeling Operational Risk When Data is Scarce." @default.
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