Long Memory in Electricity Price Time Series
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F16%3A39902102" target="_blank" >RIV/00216275:25410/16:39902102 - isvavai.cz</a>
Result on the web
<a href="http://sgemsocial.org/ssgemlib/spip.php?article2705" target="_blank" >http://sgemsocial.org/ssgemlib/spip.php?article2705</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5593/SGEMSOCIAL2016/B23/S06.050" target="_blank" >10.5593/SGEMSOCIAL2016/B23/S06.050</a>
Alternative languages
Result language
angličtina
Original language name
Long Memory in Electricity Price Time Series
Original language description
The goal of this paper is to analyze a long memory in electricity price time series. Electricity price is different from other commodities by its features like mean-reversion, high volatility rate and frequent occurrence of jumps. These differences are mainly caused by non-storability of the electricity, which need to balance supply and demand in real time. We calculate the Hurst exponent by using the Rescaled Range analysis. The Hurst exponent is a measure that has been widely used to evaluate the self-similarity and correlation properties of fractional Brownian noise, the time-series produced by a fractional (fractal) Gaussian process. The Hurst exponent is used to evaluate the presence or absence of long-range dependence and its degree in a time-series. The Hurst exponent is a numerical estimate of the predictability of a time series. In this paper we investigate the use of the Hurst exponent to classify series of the biggest European energy markets EEX (Central European Energy Exchange). The values of the Hurst exponent vary between 0 and 1, with higher values indicating a smoother trend, less volatility, and less roughness. Random walk has a Hurst exponent of 0,5. When the values of the Hurst exponent lie close to 1.0, the system has long-memory dependence. The larger the H value is, the stronger the trend. Our results show exactly between the stochastic and deterministic process. We think that this value is a sufficient value for credible prediction.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
AH - Economics
OECD FORD branch
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Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
SGEM 2016 : Political Sciences, Law, Finance, Economics and Tourism Conference Proceedings. Book 2. Vol. 3
ISBN
978-619-7105-74-2
ISSN
2367-5659
e-ISSN
—
Number of pages
10
Pages from-to
395-404
Publisher name
STEF92 Technology Ltd.
Place of publication
Sofie
Event location
Albena
Event date
Aug 22, 2016
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
000395727000050