Operational risk management based on bayesian MCMC

Qingzhong Zou; Jinlin Li; Lun Ran

doi:10.1109/IACSIT-SC.2009.40

Operational risk management based on bayesian MCMC

Qingzhong Zou^*, Jinlin Li, Lun Ran

^*Corresponding author for this work

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

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.

Original language	English
Title of host publication	2009 International Association of Computer Science and Information Technology - Spring Conference, IACSIT-SC 2009
Pages	236-239
Number of pages	4
DOIs	https://doi.org/10.1109/IACSIT-SC.2009.40
Publication status	Published - 2009
Event	2009 International Association of Computer Science and Information Technology - Spring Conference, IACSIT-SC 2009 - Singapore, Singapore Duration: 17 Apr 2009 → 20 Apr 2009

Publication series

Name	2009 International Association of Computer Science and Information Technology - Spring Conference, IACSIT-SC 2009

Conference

Conference	2009 International Association of Computer Science and Information Technology - Spring Conference, IACSIT-SC 2009
Country/Territory	Singapore
City	Singapore
Period	17/04/09 → 20/04/09

Keywords

Beyesian
GB2 distribution
Loss distribution approach
Markov chain monte carlo
Operational risk

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10.1109/IACSIT-SC.2009.40

Cite this

Zou, Q., Li, J., & Ran, L. (2009). Operational risk management based on bayesian MCMC. In 2009 International Association of Computer Science and Information Technology - Spring Conference, IACSIT-SC 2009 (pp. 236-239). Article 5169347 (2009 International Association of Computer Science and Information Technology - Spring Conference, IACSIT-SC 2009). https://doi.org/10.1109/IACSIT-SC.2009.40

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title = "Operational risk management based on bayesian MCMC",

abstract = "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.",

keywords = "Beyesian, GB2 distribution, Loss distribution approach, Markov chain monte carlo, Operational risk",

author = "Qingzhong Zou and Jinlin Li and Lun Ran",

year = "2009",

doi = "10.1109/IACSIT-SC.2009.40",

language = "English",

isbn = "9780769536538",

series = "2009 International Association of Computer Science and Information Technology - Spring Conference, IACSIT-SC 2009",

pages = "236--239",

booktitle = "2009 International Association of Computer Science and Information Technology - Spring Conference, IACSIT-SC 2009",

note = "2009 International Association of Computer Science and Information Technology - Spring Conference, IACSIT-SC 2009 ; Conference date: 17-04-2009 Through 20-04-2009",

}

Zou, Q, Li, J & Ran, L 2009, Operational risk management based on bayesian MCMC. in 2009 International Association of Computer Science and Information Technology - Spring Conference, IACSIT-SC 2009., 5169347, 2009 International Association of Computer Science and Information Technology - Spring Conference, IACSIT-SC 2009, pp. 236-239, 2009 International Association of Computer Science and Information Technology - Spring Conference, IACSIT-SC 2009, Singapore, Singapore, 17/04/09. https://doi.org/10.1109/IACSIT-SC.2009.40

Operational risk management based on bayesian MCMC. / Zou, Qingzhong; Li, Jinlin; Ran, Lun.
2009 International Association of Computer Science and Information Technology - Spring Conference, IACSIT-SC 2009. 2009. p. 236-239 5169347 (2009 International Association of Computer Science and Information Technology - Spring Conference, IACSIT-SC 2009).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Li, Jinlin

AU - Ran, Lun

PY - 2009

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N2 - 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.

AB - 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.

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KW - GB2 distribution

KW - Loss distribution approach

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KW - Operational risk

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T3 - 2009 International Association of Computer Science and Information Technology - Spring Conference, IACSIT-SC 2009

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BT - 2009 International Association of Computer Science and Information Technology - Spring Conference, IACSIT-SC 2009

T2 - 2009 International Association of Computer Science and Information Technology - Spring Conference, IACSIT-SC 2009

Y2 - 17 April 2009 through 20 April 2009

ER -

Operational risk management based on bayesian MCMC

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