IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v143y2018icp125-137.html
   My bibliography  Save this article

Stratified regression-based variance reduction approach for weak approximation schemes

Author

Listed:
  • Belomestny, D.
  • Häfner, S.
  • Urusov, M.

Abstract

In this paper we suggest a modification of the regression-based variance reduction approach recently proposed in Belomestny et al. [1]. This modification is based on the stratification technique and allows for a further significant variance reduction. The performance of the proposed approach is illustrated by several numerical examples.

Suggested Citation

  • Belomestny, D. & Häfner, S. & Urusov, M., 2018. "Stratified regression-based variance reduction approach for weak approximation schemes," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 143(C), pages 125-137.
  • Handle: RePEc:eee:matcom:v:143:y:2018:i:c:p:125-137
    DOI: 10.1016/j.matcom.2017.05.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378475417301891
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.matcom.2017.05.003?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. David Heath & Eckhard Platen, 2002. "A variance reduction technique based on integral representations," Quantitative Finance, Taylor & Francis Journals, vol. 2(5), pages 362-369.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Marc Sabate Vidales & David Siska & Lukasz Szpruch, 2018. "Unbiased deep solvers for linear parametric PDEs," Papers 1810.05094, arXiv.org, revised Jan 2022.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nicola Bruti-Liberati & Christina Nikitopoulos-Sklibosios & Eckhard Platen & Erik Schlögl, 2009. "Alternative Defaultable Term Structure Models," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 16(1), pages 1-31, March.
    2. Okano Yusuke & Yamada Toshihiro, 2019. "A control variate method for weak approximation of SDEs via discretization of numerical error of asymptotic expansion," Monte Carlo Methods and Applications, De Gruyter, vol. 25(3), pages 239-252, September.
    3. David Heath & Eckhard Platen, 2006. "Local volatility function models under a benchmark approach," Quantitative Finance, Taylor & Francis Journals, vol. 6(3), pages 197-206.
    4. Chuan-Hsiang Han & Wei-Han Liu & Tzu-Ying Chen, 2014. "VaR/CVaR ESTIMATION UNDER STOCHASTIC VOLATILITY MODELS," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 17(02), pages 1-35.
    5. Coskun Sema & Korn Ralf, 2018. "Pricing barrier options in the Heston model using the Heath–Platen estimator," Monte Carlo Methods and Applications, De Gruyter, vol. 24(1), pages 29-41, March.
    6. Gao, Jiti, 2002. "Modeling long-range dependent Gaussian processes with application in continuous-time financial models," MPRA Paper 11973, University Library of Munich, Germany, revised 18 Sep 2003.
    7. David Heath & Eckhard Platen, 2014. "A Monte Carlo Method using PDE Expansions for a Diversifed Equity Index Model," Research Paper Series 350, Quantitative Finance Research Centre, University of Technology, Sydney.
    8. Mark Broadie & Özgür Kaya, 2006. "Exact Simulation of Stochastic Volatility and Other Affine Jump Diffusion Processes," Operations Research, INFORMS, vol. 54(2), pages 217-231, April.
    9. Kailin Ding & Zhenyu Cui & Xiaoguang Yang, 2023. "Pricing arithmetic Asian and Amerasian options: A diffusion operator integral expansion approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(2), pages 217-241, February.
    10. Detlef Seese & Christof Weinhardt & Frank Schlottmann (ed.), 2008. "Handbook on Information Technology in Finance," International Handbooks on Information Systems, Springer, number 978-3-540-49487-4, November.
    11. Johan Auster & Ludovic Mathys & Fabio Maeder, 2021. "JDOI Variance Reduction Method and the Pricing of American-Style Options," Papers 2104.01365, arXiv.org, revised May 2021.
    12. Denis Belomestny & Stefan Hafner & Mikhail Urusov, 2016. "Stratified regression-based variance reduction approach for weak approximation schemes," Papers 1612.05255, arXiv.org, revised Mar 2017.

    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:eee:matcom:v:143:y:2018:i:c:p:125-137. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/mathematics-and-computers-in-simulation/ .

    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.