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Risk Measurement Robust under Model Uncertainty
Year Of Publication: 2011
Month Of Publication: May
Pages: 17
Download Count: 3
View Count: 1339
Comment Num: 0
Language: English
Source: working paper
Who Can Read: Free
Date: 11-30-2013
Publisher: Administrator
Systematic model stress tests identify worst case risk factor distributions (models) satisfying some plausibility constraint. The expected loss in the worst case model provides a scenario-based risk measure which is coherent, robust under model uncertainty, and law-invariant. As plausibility constraint for risk factor distributions (models) we take a bound on relative entropy with respect to some estimated reference distribution. We determine explicitly the maximum expected loss within this admissibility set of models, as well as the worst case model. Practical implementations of this method do not require any numerical optimisation.
Breuer, Thomas Sign in to follow this author
Csiszár, Imre Sign in to follow this author
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