IDEAS home Printed from https://ideas.repec.org/a/nwe/iisabg/y2019i4p95-110.html
   My bibliography  Save this article

Analyzing the Cyclical Components of the S&P 500 Stock Index through Wavelet Transformation

Author

Listed:
  • Bozhidar Nedev

    (University of Sofia, Bulgaria)

  • Boryana Bogdanova

    (University of Sofia, Bulgaria)

Abstract

The paper systematizes the main scientific constellations when employing wavelet transformation. Thus, the advantages of the wavelet as a relatively new approach in comparison to the alternative methods of time-frequency analysis of time-series are presented. This innovative approach is being neglected by the economic scientific community as a whole due to the preference of applying traditional econometric techniques. Wavelet transformation can partition the time-series structure into simpler components that correspond to different investment horizons (frequencies). The main goal of the paper is to investigate the time-frequency characteristics of the volatility of the monthly close prices of S&P500 index for a period of 65 years. The applicability of the continuous wavelet transformation to one-dimensional analysis is also presented. The results show that the data structure undergoes severе structural changes within the analyzed period with a clear appearance of the Technological boom and the Mortgage crisis of 2007-2008

Suggested Citation

  • Bozhidar Nedev & Boryana Bogdanova, 2019. "Analyzing the Cyclical Components of the S&P 500 Stock Index through Wavelet Transformation," Ikonomiceski i Sotsialni Alternativi, University of National and World Economy, Sofia, Bulgaria, issue 4, pages 95-110, December.
  • Handle: RePEc:nwe:iisabg:y:2019:i:4:p:95-110
    as

    Download full text from publisher

    File URL: https://www.unwe.bg/uploads/Alternatives/10_ISA_4_2019.pdf
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    wavelet transformation; capital markets; S&P 500; cyclical components; economic crisis;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G01 - Financial Economics - - General - - - Financial Crises
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    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:nwe:iisabg:y:2019:i:4:p:95-110. 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: Vanya Lazarova (email available below). General contact details of provider: https://edirc.repec.org/data/unweebg.html .

    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.