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Exponential utility maximization under model uncertainty for unbounded endowments


  • Daniel Bartl


We consider the robust exponential utility maximization problem in discrete time: An investor maximizes the worst case expected exponential utility with respect to a family of non-dominated probabilistic models of her endowment by dynamically investing in a financial market. We show that, for any measurable random endowment (regardless of whether the problem is finite or not) an optimal strategy exists, a dual representation in terms of martingale measures holds true, and that the problem satisfies the dynamic programming principle.

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  • Daniel Bartl, 2016. "Exponential utility maximization under model uncertainty for unbounded endowments," Papers 1610.00999,, revised Jun 2017.
  • Handle: RePEc:arx:papers:1610.00999

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    References listed on IDEAS

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    Cited by:

    1. Daniel Bartl & Michael Kupper & David J. Promel & Ludovic Tangpi, 2017. "Duality for pathwise superhedging in continuous time," Papers 1705.02933,, revised Sep 2017.
    2. Daniel Bartl, 2016. "Conditional nonlinear expectations," Papers 1612.09103,, revised Nov 2017.
    3. Mikl'os R'asonyi & Andrea Meireles-Rodrigues, 2018. "On Utility Maximisation Under Model Uncertainty in Discrete-Time Markets," Papers 1801.06860,, revised Feb 2018.
    4. Huy N. Chau & Miklos Rasonyi, 2018. "Robust utility maximization in markets with transaction costs," Papers 1803.04213,

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