IDEAS home Printed from https://ideas.repec.org/a/bpj/sndecm/v16y2012i3n5.html
   My bibliography  Save this article

A Nonlinear Filtering Algorithm based on Wavelet Transforms for High-Frequency Financial Data Analysis

Author

Listed:
  • Meinl Thomas

    () (Karlsruhe Institute of Technology (KIT), Germany)

  • Sun Edward W.

    () (BEM Bordeaux Management School, France)

Abstract

The increased availability of high-frequency financial data has imposed new challenges for its denoising analysis since the data exhibits heavy tails and long-memory effects that render the application of traditional methods difficult. In this paper, we introduce the local linear scaling approximation (in short, LLSA), which is a nonlinear filtering algorithm based on the linear maximal overlap discrete wavelet transform (MODWT). We show the unique properties of LLSA and compare its performance with MODWT. We empirically show the superior performance of LLSA in smoothing analysis (i.e., trend extraction) of high- frequency data from German equity market. Based on our results we conclude that LLSA is reliable and suitable for high-frequency data denoising analysis.

Suggested Citation

  • Meinl Thomas & Sun Edward W., 2012. "A Nonlinear Filtering Algorithm based on Wavelet Transforms for High-Frequency Financial Data Analysis," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(3), pages 1-24, September.
  • Handle: RePEc:bpj:sndecm:v:16:y:2012:i:3:n:5
    as

    Download full text from publisher

    File URL: https://www.degruyter.com/view/j/snde.2012.16.issue-3/1558-3708.1920/1558-3708.1920.xml?format=INT
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.

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

    Citations

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


    Cited by:

    1. repec:eee:reveco:v:49:y:2017:i:c:p:484-498 is not listed on IDEAS
    2. Sun, Edward W. & Chen, Yi-Ting & Yu, Min-Teh, 2015. "Generalized optimal wavelet decomposing algorithm for big financial data," International Journal of Production Economics, Elsevier, vol. 165(C), pages 194-214.

    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:bpj:sndecm:v:16:y:2012:i:3:n:5. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Peter Golla). General contact details of provider: https://www.degruyter.com .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.