Bayesian Inference, Monte Carlo Sampling and Operational Risk.

 Peters G.W. and Sisson S.A. (2006) “Bayesian Inference, Monte Carlo Sampling and Operational Risk". Journal of Operational Risk, 1(3).

24 Pages Posted: 5 Jun 2017

See all articles by Gareth Peters

Gareth Peters

University of California Santa Barbara; affiliation not provided to SSRN

Scott Sisson

University of New South Wales (UNSW) - School of Mathematics and Statistics

Date Written: 2006

Abstract

Operational risk is an important quantitative topic as a result of the Basel II regulatory requirements. Operational risk models need to incorporate internal and external loss data observations in combination with expert opinion surveyed from business specialists. Following the Loss Distributional Approach, this article considers three aspects of the Bayesian approach to the modelling of operational risk. Firstly we provide an overview of the Bayesian approach to operational risk, before expanding on the current literature through consideration of general families of non-conjugate severity distributions, g-and-h and GB2 distributions. Bayesian model selection is presented as an alternative to popular frequentist tests, such as Kolmogorov-Smirnov or Anderson-Darling. We present a number of examples and develop techniques for parameter estimation for general severity and frequency distribution models from a Bayesian perspective. Finally we introduce and evaluate recently developed stochastic sampling techniques and highlight their application to operational risk through the models developed.

Keywords: Approximate Bayesian Computation; Basel II Advanced Measurement Approach; Bayesian Inference; Compound Processes; Loss Distributional Approach; Markov Chain Monte Carlo; Operational Risk

Suggested Citation

Peters, Gareth and Sisson, Scott, Bayesian Inference, Monte Carlo Sampling and Operational Risk. (2006).  Peters G.W. and Sisson S.A. (2006) “Bayesian Inference, Monte Carlo Sampling and Operational Risk". Journal of Operational Risk, 1(3). , Available at SSRN: https://ssrn.com/abstract=2980407 or http://dx.doi.org/10.2139/ssrn.2980407

Gareth Peters (Contact Author)

University of California Santa Barbara ( email )

Santa Barbara, CA 93106
United States

affiliation not provided to SSRN

Scott Sisson

University of New South Wales (UNSW) - School of Mathematics and Statistics ( email )

Sydney, 2052
Australia

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