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Optimal stopping under model uncertainty: randomized stopping times approach

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  • Denis Belomestny
  • Volker Kraetschmer

Abstract

In this work we consider optimal stopping problems with conditional convex risk measures called optimised certainty equivalents. Without assuming any kind of time-consistency for the underlying family of risk measures, we derive a novel representation for the solution of the optimal stopping problem. In particular, we generalise the additive dual representation of Rogers (2002) to the case of optimal stopping under uncertainty. Finally, we develop several Monte Carlo algorithms and illustrate their power for optimal stopping under Average Value at Risk.

Suggested Citation

  • Denis Belomestny & Volker Kraetschmer, 2014. "Optimal stopping under model uncertainty: randomized stopping times approach," Papers 1405.2240, arXiv.org, revised Dec 2014.
    • Handle: RePEc:arx:papers:1405.2240
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    File URL: http://arxiv.org/pdf/1405.2240
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    Cited by:

    1. Denis Belomestny & Volker Kraetschmer, 2017. "Minimax theorems for American options in incomplete markets without time-consistency," Papers 1708.08904, arXiv.org.

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