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Stochastic linear programming with a distortion risk constraint

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  • Bazovkin, Pavel
  • Mosler, Karl

Abstract

Linear optimization problems are investigated whose parameters are uncertain. We apply coherent distortion risk measures to capture the violation of restrictions. Such a model turns out to be appropriate for many applications and, principally, for the mean-risk portfolio selection problem. Each risk constraint induces an uncertainty set of coefficients, which comes out to be a weighted-mean trimmed region. We consider a problem with a single constraint. Given an external sample of the coefficients, the uncertainty set is a convex polytope that can be exactly calculated. If the sample is i.i.d. from a general probability distribution, the solution of the stochastic linear program (SLP) is a consistent estimator of the SLP solution with respect to the underlying probability. An efficient geometrical algorithm is proposed to solve the SLP.

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  • Bazovkin, Pavel & Mosler, Karl, 2011. "Stochastic linear programming with a distortion risk constraint," Discussion Papers in Econometrics and Statistics 6/11, University of Cologne, Institute of Econometrics and Statistics.
    • Handle: RePEc:zbw:ucdpse:611
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    References listed on IDEAS

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    1. Wenqing Chen & Melvyn Sim & Jie Sun & Chung-Piaw Teo, 2010. "From CVaR to Uncertainty Set: Implications in Joint Chance-Constrained Optimization," Operations Research, INFORMS, vol. 58(2), pages 470-485, April.
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    11. Dyckerhoff, Rainer & Mosler, Karl, 2012. "Weighted-mean regions of a probability distribution," Statistics & Probability Letters, Elsevier, vol. 82(2), pages 318-325.
    12. Dyckerhoff, Rainer & Mosler, Karl, 2011. "Weighted-mean trimming of multivariate data," Journal of Multivariate Analysis, Elsevier, vol. 102(3), pages 405-421, March.
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    Cited by:

    1. Postek, K.S. & den Hertog, D. & Melenberg, B., 2014. "Tractable Counterparts of Distributionally Robust Constraints on Risk Measures," Discussion Paper 2014-031, Tilburg University, Center for Economic Research.
    2. Postek, K.S. & den Hertog, D. & Melenberg, B., 2015. "Computationally Tractable Counterparts of Distributionally Robust Constraints on Risk Measures (revision of CentER DP 2014-031)," Discussion Paper 2015-047, Tilburg University, Center for Economic Research.
    3. Bazovkin, Pavel, 2014. "Geometrical framework for robust portfolio optimization," Discussion Papers in Econometrics and Statistics 01/14, University of Cologne, Institute of Econometrics and Statistics.

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