IDEAS home Printed from https://ideas.repec.org/p/eus/wpaper/ec2016_03.html
   My bibliography  Save this paper

Fractal Characterization of Long Memory in Electricity Prices

Author

Listed:
  • Yuri Balagula

Abstract

In the paper we use different methods of fractal analysis for characterization of long memory and other features of wholesale electricity prices. The connection between different characteristics of time series, such as capacity fractal dimension, Hurst exponent, spectral dimension, fractional integration order, is shown. The relation between the notions of long memory, fractional integration and persistence of a time series is considered. We calculated the fractal characteristics for wholesale electricity prices taken from electricity exchanges of Northern Europe, Italia and Ontario (Canada). The results show that the analyzed time series are persistent and reveal the long memory property. (In Russian).

Suggested Citation

  • Yuri Balagula, 2016. "Fractal Characterization of Long Memory in Electricity Prices," EUSP Department of Economics Working Paper Series 2016/03, European University at St. Petersburg, Department of Economics.
  • Handle: RePEc:eus:wpaper:ec2016_03
    as

    Download full text from publisher

    File URL: https://eusp.org/sites/default/files/archive/ec_dep/wp/Ec-03_16.pdf
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Balagula, Yuri, 2020. "Forecasting daily spot prices in the Russian electricity market with the ARFIMA model," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 57, pages 89-101.

    More about this item

    Keywords

    time series; fractal analysis; fractal dimension; Hurst exponent; ARFIMA; long memory; persistence; electricity market;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

    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:eus:wpaper:ec2016_03. 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: Mikhail Pakhnin (email available below). General contact details of provider: https://edirc.repec.org/data/feeusru.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.