IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v37y2022i1d10.1007_s00180-021-01117-z.html
   My bibliography  Save this article

An algorithm of generating random number by wavelet denoising method and its application

Author

Listed:
  • Zhou Xiaohui

    (Zhejiang University of Finance and Economics Dongfang College
    Shanghai University of Finance and Economics)

  • Gu Guiding

    (Shanghai University of Finance and Economics)

Abstract

According to the wavelet deniosing method, a new algorithm for generating standard normal random numbers is proposed in this paper. For the standard normal random number generated by randn function, a comparative study is done to discuss the influence of different threshold rules on the mean and variance of random number, the influence of different decomposition levels on random number. Then the correlation among the components of high-dimensional random number is discussed in different space scales. For 1000 groups of normal random number, the distributions of p value of J-B test, mean, variance and correlation are shown by their boxplot. WMC method is presented and applied in numerical integration. For 1000 groups of approximation values computed by WMC method, the mean and variance are given for discussing its accuracy and stability by the boxplot. Finally, an example is given for numerical simulation of financial model.

Suggested Citation

  • Zhou Xiaohui & Gu Guiding, 2022. "An algorithm of generating random number by wavelet denoising method and its application," Computational Statistics, Springer, vol. 37(1), pages 107-124, March.
    • Handle: RePEc:spr:compst:v:37:y:2022:i:1:d:10.1007_s00180-021-01117-z
    DOI: 10.1007/s00180-021-01117-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-021-01117-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00180-021-01117-z?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. Mark J. Jensen, 1997. "Using Wavelets to Obtain a Consistent Ordinary Least Squares Estimator of the Long Memory Parameter," Econometrics 9710002, University Library of Munich, Germany.
    2. Xiaohui Zhou & Gang Wang & Basil K. Papadopoulos, 2021. "Biorthogonal Wavelet on a Logarithm Curve â„‚," Journal of Mathematics, Hindawi, vol. 2021, pages 1-14, March.
    Full references (including those not matched with items on IDEAS)

    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. SangKun Bae & Mark J. Jensen, 1998. "Long-Run Neutrality in a Long-Memory Model," Macroeconomics 9809006, University Library of Munich, Germany, revised 21 Apr 1999.
    2. Derek Bond & Michael J. Harrison & Edward J. O'Brien, 2005. "Testing for Long Memory and Nonlinear Time Series: A Demand for Money Study," Trinity Economics Papers tep20021, Trinity College Dublin, Department of Economics.
    3. Muniandy, Sithi V. & Uning, Rosemary, 2006. "Characterization of exchange rate regimes based on scaling and correlation properties of volatility for ASEAN-5 countries," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 371(2), pages 585-598.
    4. Boubaker Heni & Canarella Giorgio & Miller Stephen M. & Gupta Rangan, 2017. "Time-varying persistence of inflation: evidence from a wavelet-based approach," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(4), pages 1-18, September.
    5. Avishek Bhandari & Bandi Kamaiah, 2021. "Long Memory and Fractality Among Global Equity Markets: a Multivariate Wavelet Approach," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 23-37, March.
    6. Bhandari, Avishek, 2020. "Long Memory and Correlation Structures of Select Stock Returns Using Novel Wavelet and Fractal Connectivity Networks," MPRA Paper 101946, University Library of Munich, Germany.
    7. Patrick M. Crowley, 2007. "A Guide To Wavelets For Economists," Journal of Economic Surveys, Wiley Blackwell, vol. 21(2), pages 207-267, April.
    8. Shapour Mohammadi & Ahmad Pouyanfar, 2011. "Behaviour of stock markets' memories," Applied Financial Economics, Taylor & Francis Journals, vol. 21(3), pages 183-194.
    9. Lin Shinn-Juh & Stevenson Maxwell, 2001. "Wavelet Analysis of the Cost-of-Carry Model," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 5(1), pages 1-17, April.
    10. Li, Yushu, 2012. "Estimating Long Memory Causality Relationships by a Wavelet Method," Working Papers 2012:15, Lund University, Department of Economics.
    11. Brandon Whitcher, 2000. "Wavelet-Based Estimation Procedures For Seasonal Long-Memory Models," Computing in Economics and Finance 2000 148, Society for Computational Economics.
    12. Bhandari, Avishek, 2020. "Long memory and fractality among global equity markets: A multivariate wavelet approach," MPRA Paper 99653, University Library of Munich, Germany.
    13. Crowley, Patrick M., 2005. "An intuitive guide to wavelets for economists," Bank of Finland Research Discussion Papers 1/2005, Bank of Finland.
    14. Gilles Dufrénot & Valérie Mignon & Théo Naccache, 2009. "The slow convergence of per capita income between the developing countries: “growth resistance” and sometimes “growth tragedy”," Discussion Papers 09/03, University of Nottingham, CREDIT.
    15. Lihong Wang & Jinde Wang, 2014. "Wavelet estimation of the memory parameter for long range dependent random fields," Statistical Papers, Springer, vol. 55(4), pages 1145-1158, November.
    16. Jensen, Mark J., 2000. "An alternative maximum likelihood estimator of long-memory processes using compactly supported wavelets," Journal of Economic Dynamics and Control, Elsevier, vol. 24(3), pages 361-387, March.
    17. Boubaker Heni & Canarella Giorgio & Miller Stephen M. & Gupta Rangan, 2021. "Long-memory modeling and forecasting: evidence from the U.S. historical series of inflation," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 25(5), pages 289-310, December.
    18. Mejra Festic & Alenka Kavkler & Silvo Dajcman, 2012. "Long memory in the Croatian and Hungarian stock market returns," Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, University of Rijeka, Faculty of Economics and Business, vol. 30(1), pages 115-139.
    19. Ysusi Carla, 2009. "Analysis of the Dynamics of Mexican Inflation Using Wavelets," Working Papers 2009-09, Banco de México.
    20. Dowd, Kevin & Cotter, John & Loh, Lixia, 2011. "U.S. Core Inflation: A Wavelet Analysis," Macroeconomic Dynamics, Cambridge University Press, vol. 15(4), pages 513-536, September.

    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:spr:compst:v:37:y:2022:i:1:d:10.1007_s00180-021-01117-z. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.