Operational Risk Management Based on Bayesian MCMC

被引：1

作者：

Zou, Qingzhong ^{[1
]}

Li, Jinlin ^{[1
]}

Ran, Lun ^{[1
]}

机构：

[1] Beijing Inst Technol, Sch Management & Econ, Beijing 100081, Peoples R China

来源：

IACSIT-SC 2009: INTERNATIONAL ASSOCIATION OF COMPUTER SCIENCE AND INFORMATION TECHNOLOGY - SPRING CONFERENCE | 2009年

关键词：

operational risk; loss distribution approach; beyesian; markov chain monte carlo; GB2; distribution;

D O I：

10.1109/IACSIT-SC.2009.40

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

The aim of this paper is to introduce a new framework for operational risk management, based on Bayesian Markov chain Monte Carlo (MCMC). Under the LDA approach, non-conjugate distribution is used to fit the frequency and severity. One of the problems relative to the non-conjugate distribution is difficult to estimate the parameter. Then the Bayesian MCMC approach is brought forward. The Bayesian is implemented to obtain the posterior of non-conjugate distribution, the MCMC algorithm is employed to estimate the posterior parameters. The Bayesian MCMC framework is strongly recommended in the operational risk management as it incorporate internal and external loss data observations in combination with expert opinion. A numerical example is constructed to illustrate the performance of the framework advocated by this paper.

引用

页码：236 / 239

页数：4

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