IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v62y2003i3p255-263.html
   My bibliography  Save this article

One more experiment on estimating high-dimensional integrals by quasi-Monte Carlo methods

Author

Listed:
  • Sobol’, I.M
  • Asotsky, D.I

Abstract

Integrands that depend on a large number of equally important variables are considered and conditions that make expedient quasi-Monte Carlo integrations are investigated for dimensions n≤300.

Suggested Citation

  • Sobol’, I.M & Asotsky, D.I, 2003. "One more experiment on estimating high-dimensional integrals by quasi-Monte Carlo methods," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 62(3), pages 255-263.
    • Handle: RePEc:eee:matcom:v:62:y:2003:i:3:p:255-263
    DOI: 10.1016/S0378-4754(02)00228-8
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378475402002288
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/S0378-4754(02)00228-8?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. Ilya M. Sobol’ & Boris V. Shukhman, 1995. "Integration With Quasirandom Sequences: Numerical Experience," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 6(02), pages 263-275.
    2. Spassimir H. Paskov & Joseph F. Traub, 1995. "Faster Valuation of Financial Derivatives," Working Papers 95-03-034, Santa Fe Institute.
    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. Takhtamyshev, George & Vandewoestyne, Bart & Cools, Ronald, 2007. "Quasi-random integration in high dimensions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 73(5), pages 309-319.

    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. Sobol Ilya M. & Shukhman Boris V., 2014. "Quasi-Monte Carlo: A high-dimensional experiment," Monte Carlo Methods and Applications, De Gruyter, vol. 20(3), pages 167-171, September.
    2. Sobol, I.M., 1998. "On quasi-Monte Carlo integrations," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 47(2), pages 103-112.
    3. Xiaoqun Wang, 2016. "Handling Discontinuities in Financial Engineering: Good Path Simulation and Smoothing," Operations Research, INFORMS, vol. 64(2), pages 297-314, April.
    4. George Chang, 2018. "Examining the Efficiency of American Put Option Pricing by Monte Carlo Methods with Variance Reduction," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(2), pages 10-13, February.
    5. John Rust & Joseph Traub & Henryk Wozniakowski, 1999. "No Curse of Dimensionality for Contraction Fixed Points Even in the Worst Case," Computational Economics 9902001, University Library of Munich, Germany.
    6. Aintablian, Sebouh & Khoury, Wissam El, 2017. "A simulation on the presence of competing bidders in mergers and acquisitions," Finance Research Letters, Elsevier, vol. 22(C), pages 233-243.
    7. Kucherenko, S. & Rodriguez-Fernandez, M. & Pantelides, C. & Shah, N., 2009. "Monte Carlo evaluation of derivative-based global sensitivity measures," Reliability Engineering and System Safety, Elsevier, vol. 94(7), pages 1135-1148.
    8. Okten, Giray & Eastman, Warren, 2004. "Randomized quasi-Monte Carlo methods in pricing securities," Journal of Economic Dynamics and Control, Elsevier, vol. 28(12), pages 2399-2426, December.
    9. Eichler Andreas & Leobacher Gunther & Zellinger Heidrun, 2011. "Calibration of financial models using quasi-Monte Carlo," Monte Carlo Methods and Applications, De Gruyter, vol. 17(2), pages 99-131, January.
    10. Xiaoqun Wang, 2006. "On the Effects of Dimension Reduction Techniques on Some High-Dimensional Problems in Finance," Operations Research, INFORMS, vol. 54(6), pages 1063-1078, December.
    11. Xiaoqun Wang & Ken Seng Tan, 2013. "Pricing and Hedging with Discontinuous Functions: Quasi-Monte Carlo Methods and Dimension Reduction," Management Science, INFORMS, vol. 59(2), pages 376-389, July.
    12. Krupenev, Dmitry & Boyarkin, Denis & Iakubovskii, Dmitrii, 2020. "Improvement in the computational efficiency of a technique for assessing the reliability of electric power systems based on the Monte Carlo method," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    13. Tan, Ken Seng & Boyle, Phelim P., 2000. "Applications of randomized low discrepancy sequences to the valuation of complex securities," Journal of Economic Dynamics and Control, Elsevier, vol. 24(11-12), pages 1747-1782, October.
    14. Boyle, Phelim & Broadie, Mark & Glasserman, Paul, 1997. "Monte Carlo methods for security pricing," Journal of Economic Dynamics and Control, Elsevier, vol. 21(8-9), pages 1267-1321, June.
    15. Gonzalez, Jorge & Tuerlinckx, Francis & De Boeck, Paul & Cools, Ronald, 2006. "Numerical integration in logistic-normal models," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1535-1548, December.
    16. Nelson Areal & Artur Rodrigues & Manuel Armada, 2008. "On improving the least squares Monte Carlo option valuation method," Review of Derivatives Research, Springer, vol. 11(1), pages 119-151, March.
    17. Xiaoqun Wang & Ian H. Sloan, 2011. "Quasi-Monte Carlo Methods in Financial Engineering: An Equivalence Principle and Dimension Reduction," Operations Research, INFORMS, vol. 59(1), pages 80-95, February.
    18. Jean-Jacques Forneron, 2019. "A Scrambled Method of Moments," Papers 1911.09128, arXiv.org.
    19. Marco Bianchetti & Sergei Kucherenko & Stefano Scoleri, 2015. "Pricing and Risk Management with High-Dimensional Quasi Monte Carlo and Global Sensitivity Analysis," Papers 1504.02896, arXiv.org.
    20. Siegl, Thomas & F. Tichy, Robert, 2000. "Ruin theory with risk proportional to the free reserve and securitization," Insurance: Mathematics and Economics, Elsevier, vol. 26(1), pages 59-73, February.

    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:62:y:2003:i:3:p:255-263. 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.