IDEAS home Printed from https://ideas.repec.org/a/bla/mathfi/v29y2019i1p368-406.html

Distribution‐constrained optimal stopping

Author

Listed:
  • Erhan Bayraktar
  • Christopher W. Miller

Abstract

We solve the problem of optimal stopping of a Brownian motion subject to the constraint that the stopping time's distribution is a given measure consisting of finitely many atoms. In particular, we show that this problem can be converted to a finite sequence of state‐constrained optimal control problems with additional states corresponding to the conditional probability of stopping at each possible terminal time. The proof of this correspondence relies on a new variation of the dynamic programming principle for state‐constrained problems, which avoids measurable selections. We emphasize that distribution constraints lead to novel and interesting mathematical problems on their own, but also demonstrate an application in mathematical finance to model‐free superhedging with an outlook on volatility.

Suggested Citation

  • Erhan Bayraktar & Christopher W. Miller, 2019. "Distribution‐constrained optimal stopping," Mathematical Finance, Wiley Blackwell, vol. 29(1), pages 368-406, January.
  • Handle: RePEc:bla:mathfi:v:29:y:2019:i:1:p:368-406
    DOI: 10.1111/mafi.12171
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/mafi.12171
    Download Restriction: no

    File URL: https://libkey.io/10.1111/mafi.12171?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    Citations

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


    Cited by:

    1. Christopher W. Miller, 2016. "A Duality Result for Robust Optimization with Expectation Constraints," Papers 1610.01227, arXiv.org.
    2. Sigrid Kallblad, 2017. "A Dynamic Programming Principle for Distribution-Constrained Optimal Stopping," Papers 1703.08534, arXiv.org.
    3. Soren Christensen & Kristoffer Lindensjo, 2019. "Moment constrained optimal dividends: precommitment \& consistent planning," Papers 1909.10749, arXiv.org.
    4. Shantanu Awasthi & Indranil SenGupta, 2020. "First exit-time analysis for an approximate Barndorff-Nielsen and Shephard model with stationary self-decomposable variance process," Papers 2006.07167, arXiv.org, revised Jan 2021.
    5. Bayraktar, Erhan & Yao, Song, 2024. "Stochastic control/stopping problem with expectation constraints," Stochastic Processes and their Applications, Elsevier, vol. 176(C).

    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:bla:mathfi:v:29:y:2019:i:1:p:368-406. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0960-1627 .

    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.