An Application of Bayesian Inference on the Modeling and Estimation of Operational Risk Using Banking Loss Data
An Application of Bayesian Inference on the Modeling and Estimation of Operational Risk Using Banking Loss Data作者机构：Oakland University Rochester USA omerica Bank Dallas USA
出 版 物：《Applied Mathematics》 (应用数学（英文）)
年 卷 期：2014年第5卷第6期
页 面：862-876页
学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学]
主 题：Monte Carlo Simulation Value-at-Risk Basel II Operational Risk Bayesian
摘 要：Bayesian inference method has been presented in this paper for the modeling of operational risk. 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 in- ternal as well as the heavy tail high profile low frequency losses. The output of the Monte Carlo simulation defines the capital requirement that has to be allocated to cover potential operational risk losses for the next year.
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