Title: Quantification of Model Risk: Data Uncertainty
Abstract: Worldwide regulations oblige financial institutions to manage and address model risk (MR) as any other type of risk. MR quantification is essential not only in meeting these requirements but also for institution’s basic internal operative. In [5] the authors introduce a framework for the quantification of MR based on information geometry. The framework is applicable in great generality and accounts for different sources of MR during the entire lifecycle of a model. The aim of this paper is to extend the framework in [5] by studying its relation with the uncertainty associated to the data used for building the model. We define a metric on the space of samples in order to measure the data intrinsic distance, providing a novel way to probe the data for insight, allowing us to work on the sample space, gain business intuition and access tools such as perturbation methods.
Publication Year: 2017
Publication Date: 2017-01-01
Language: en
Type: book-chapter
Indexed In: ['crossref']
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Cited By Count: 1
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