Measuring Model Risk
AbstractWe propose to interpret distribution model risk as sensitivity of expected loss to changes in the risk factor distribution, and to measure the distribution model risk of a portfolio by the maximum expected loss over a set of plausible distributions defined in terms of some divergence from an estimated distribution. The divergence may be relative entropy, a Bregman distance, or an $f$-divergence. We give formulas for the calculation of distribution model risk and explicitly determine the worst case distribution from the set of plausible distributions. We also give formulas for the evaluation of divergence preferences describing ambiguity averse decision makers.
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Bibliographic InfoPaper provided by arXiv.org in its series Papers with number 1301.4832.
Date of creation: Jan 2013
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Web page: http://arxiv.org/
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-01-26 (All new papers)
- NEP-RMG-2013-01-26 (Risk Management)
- NEP-UPT-2013-01-26 (Utility Models & Prospect Theory)
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