Modeling Dependencies in Operational Risk with Hybrid Bayesian Networks |
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Authors: | Stefan Mittnik Irina Starobinskaya |
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Institution: | (1) Division of International Business and Technology Studies, Texas A&M International University, 5201 University Boulevard, Laredo, TX 78041, USA |
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Abstract: | This paper addresses the problem of quantifying and modeling financial institutions’ operational risk in accordance with the
Advanced Measurement Approach put forth in the Basel II Accord. We argue that standard approaches focusing on modeling stochastic
dependencies are not sufficient to adequately assess operational risk. In addition to stochastic dependencies, causal topological
dependencies between the risk classes are typically encountered. These dependencies arise when risk units have common information-
and/or work-flows and when failure of upstream processes imply risk for downstream processes. In this paper, we present a
modeling strategy that explicitly captures both topological and stochastic dependencies between risk classes. We represent
the operational-risk taxonomy in the framework of a hybrid Bayesian network (BN) and provide an intuitively compelling approach
for handling causal relationships and external influences. We demonstrate the use of hybrid BNs as a tool for mapping causal
dependencies between frequencies and severities of risk events and for modeling common shocks. Monte-Carlo simulations illustrate
that the impact of topological dependencies on triggering overall system breakdowns can be substantial. |
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