IDEAS home Printed from https://ideas.repec.org/b/pal/palbok/978-1-137-03351-2.html
   My bibliography  Save this book

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

No abstract is available for this item.

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, September.
    • Handle: RePEc:pal:palbok:978-1-137-03351-2
    DOI: 10.1057/978-1-137-03351-2
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. David A. Goldberg & Yilun Chen, 2018. "Beating the curse of dimensionality in options pricing and optimal stopping," Papers 1807.02227, arXiv.org, revised Aug 2018.
    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.

    Book Chapters

    The following chapters of this book are listed in IDEAS

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:palbok:978-1-137-03351-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.