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Modelling power spot prices in deregulated European energy markets: a dual long memory approach

Authors: Najeh Chaâbane; Foued Saâdaoui; Saloua Benammou;

Modelling power spot prices in deregulated European energy markets: a dual long memory approach

Abstract

In the last decade, with deregulation and introduction of competition in power markets, prices forecasting have become a real challenge for all market participants. However, forecasting is a rather complex task since electricity prices involve many features comparably with financial ones. Electricity markets have a highly volatile nature. They are indeed a more unpredictable than that of other commodities referred to as extreme volatile. In this paper, the two most emerging European electricity markets are considered. A preliminary analysis of the time series attests to the presence of a long range dependance behaviour. Therefore, prices processes are modelled using ARFIMA-FIGARCH under Gaussian and non-Gaussian distributions. Such models are sufficiently flexible to handle the long memory phenomena often encountered in both conditional mean and conditional variance in electricity spot prices. Forecasting is subsequently performed on the basis of adequate models.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
7
Average
Average
Average
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