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Advanced Simulation-Based Methods for Optimal Stopping and Control

Author

Listed:
  • Denis Belomestny

    (Duisburg-Essen University)

  • John Schoenmakers

    (Weierstrass Institute for Applied Analysis and Stochastics)

Abstract

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Individual chapters are listed in the "Chapters" tab

Suggested Citation

  • Denis Belomestny & John Schoenmakers, 2018. "Advanced Simulation-Based Methods for Optimal Stopping and Control," Palgrave Macmillan Books, Palgrave Macmillan, number 978-1-137-03351-2, March.
    • Handle: RePEc:pal:palbok:978-1-137-03351-2
    DOI: 10.1057/978-1-137-03351-2
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    Cited by:

    1. David A. Goldberg & Yilun Chen, 2018. "Polynomial time algorithm for optimal stopping with fixed accuracy," Papers 1807.02227, arXiv.org, revised May 2024.
    2. D. Belomestny & M. Kaledin & J. Schoenmakers, 2019. "Semi-tractability of optimal stopping problems via a weighted stochastic mesh algorithm," Papers 1906.09431, arXiv.org.
    3. Mike Ludkovski, 2020. "mlOSP: Towards a Unified Implementation of Regression Monte Carlo Algorithms," Papers 2012.00729, arXiv.org, revised Oct 2022.
    4. Denis Belomestny & Tobias Hübner & Volker Krätschmer, 2022. "Solving optimal stopping problems under model uncertainty via empirical dual optimisation," Finance and Stochastics, Springer, vol. 26(3), pages 461-503, July.
    5. Christian Bayer & Martin Redmann & John Schoenmakers, 2018. "Dynamic programming for optimal stopping via pseudo-regression," Papers 1808.04725, arXiv.org, revised Apr 2019.
    6. Christian Bayer & Denis Belomestny & Paul Hager & Paolo Pigato & John Schoenmakers, 2020. "Randomized optimal stopping algorithms and their convergence analysis," Papers 2002.00816, arXiv.org.
    7. Christian Bayer & Ra'ul Tempone & Soren Wolfers, 2018. "Pricing American Options by Exercise Rate Optimization," Papers 1809.07300, arXiv.org, revised Aug 2019.

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