Dynamic operational risk: modeling dependence and combining different sources of information

Gareth Peters, Pavel Shevchenko, Mario Wüthrich

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we model dependence between operational risks by allowing risk profiles to evolve stochastically in time and to be dependent. This allows for a flexible correlation structure where the dependence between frequencies of different risk categories and between severities of different risk categories as well as within risk categories can be modeled. The model is estimated using Bayesian inference methodology, allowing for a combination of internal data, external data and expert opinion in the estimation procedure. We use a specialized Markov chain Monte Carlo simulation methodology known as slice sampling to obtain samples from the resulting posterior distribution and estimate the model parameters.
Original languageEnglish
Pages (from-to)69-104
Number of pages36
JournalJournal of Operational Risk
Volume4
Issue number2
DOIs
Publication statusPublished - 2009
Externally publishedYes

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