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Fractal Characterization of Long Memory in Electricity Prices

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  • 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
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    File URL: https://eusp.org/sites/default/files/archive/ec_dep/wp/Ec-03_16.pdf
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    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;

    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

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