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2 Modelling Operational Risk in Financial Institutions using Hybrid Dynamic Bayesian Networks

By Professor Martin Neil, Dr. Lasse, B. Andersen, David Häger, Phd Student, David Häger, Martin Neil and Lasse B. Andersen


This paper describes the use of Hybrid Dynamic Bayesian Networks (HDBNs) to model operational risk in an AMA context. The approach focuses on causeeffect modelling including interactions between failure modes and controls. Value at Risk is calculated by applying a new state-of-the-art HDBN algorithm that approximates continuous loss distributions and aggregates across loss types. In order to illustrate the natural match between the model and the underlying process, including the causal complexity underlying known and possible severe operational risk losses, we apply the generalised model to a financial trading example — rogue trading. We conclude that the statistical properties of the model have the potential to explain recent large scale loss events and offer improved means of loss prediction

Topics: Operational Risk Management, Bayesian Networks, Causal Models, Dynamic Discretization, Basel II, Advanced Measurement Approach
Year: 2013
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