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Measuring Model Risk


  • Thomas Breuer
  • Imre Csiszar


We 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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  • Thomas Breuer & Imre Csiszar, 2013. "Measuring Model Risk," Papers 1301.4832,
  • Handle: RePEc:arx:papers:1301.4832

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    References listed on IDEAS

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    7. Philippe Artzner & Freddy Delbaen & Jean-Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228.
    8. Barillas, Francisco & Hansen, Lars Peter & Sargent, Thomas J., 2009. "Doubts or variability?," Journal of Economic Theory, Elsevier, vol. 144(6), pages 2388-2418, November.
    9. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers Archive 1488, Iowa State University, Department of Economics.
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    13. Philippe Robert-Demontrond & R. Ringoot, 2004. "Introduction," Post-Print halshs-00081823, HAL.
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