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Measuring distribution model risk

Breuer, T. and Csiszár, Imre (2016) Measuring distribution model risk. MATHEMATICAL FINANCE: AN INTERNATIONAL JOURNAL OF MATHEMATICS, STATISTICS AND FINANCIAL ECONOMICS, 26 (2). pp. 395-411. ISSN 0960-1627

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Abstract

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 or another f-divergence or Bregman distance. We use the theory of minimizing convex integral functionals under moment constraints to give formulae for the calculation of distribution model risk and to explicitly determine the worst case distribution from the set of plausible distributions. We also evaluate related risk measures describing divergence preferences. © 2013 Wiley Periodicals, Inc.

Item Type: Article
Uncontrolled Keywords: RELATIVE ENTROPY; Multiple priors; Maximum entropy principle; Generalized exponential family; f-divergence; Divergence preferences; Convex integral functional; Bregman distance
Subjects: Q Science / természettudomány > QA Mathematics / matematika
SWORD Depositor: MTMT SWORD
Depositing User: MTMT SWORD
Date Deposited: 03 Jan 2017 19:40
Last Modified: 03 Jan 2017 19:40
URI: http://real.mtak.hu/id/eprint/44216

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