Abstract
Model-based decisions are highly sensitive to model risk that arises from the inadequacy of the adopted model. This paper reviews the existing literature on model risk assessment and shows how to use the theoretical results to develop a corresponding best practice. Specifically, we develop tools to assess the contribution to model risk of each of the assumptions that underpin the adopted model. Furthermore, we introduce new model risk measures and propose an intuitive formula for computing model risk capital. Some numerical examples and a case study illustrate our results.
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