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The k-factor Gegenbauer asymmetric Power GARCH approach for modelling electricity spot price dynamics

Author

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  • Abdou Kâ Diongue

    (UGB - Université Gaston Berger de Saint-Louis Sénégal, CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, School of Economics and Finance - QUT - Queensland University of Technology [Brisbane])

  • Dominique Guegan

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

Abstract

Electricity spot prices exhibit a number of typical features that are not found in most financial time series, such as complex seasonality patterns, persistence (hyperbolic decay of the autocorrelation function), mean reversion, spikes, asymmetric behavior and leptokurtosis. Efforts have been made worldwide to model the behaviour of the electricity's market price. In this paper, we propose a new approach dealing with the stationary k-factor Gegenbauer process with asymmetric Power GARCH noise under conditional Student-t distribution, which can take into account the previous features. We derive the stationary and invertible conditions as well as the δth-order moment of this model that we called GGk-APARCH model. Then we focus on the estimation parameters and provide the analytical from of the likelihood which permits to obtain consitent estimates. In order to characterize the properties of these estimates we perform a Monte Carlo experiment. Finally the previous approach is used to the model electricity spot prices coming from the Leipzig Power Exchange (LPX) in Germany, Powernext in France, Operadora del Mercado Espagñol de Electricidad (OMEL) in Spain and the Pennsylvania-New Jersey-Maryland (PJM) interconnection in United States. In terms of forecasting criteria we obtain very good results comparing with models using hederoscedastic asymmetric errors.

Suggested Citation

  • Abdou Kâ Diongue & Dominique Guegan, 2008. "The k-factor Gegenbauer asymmetric Power GARCH approach for modelling electricity spot price dynamics," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00259225, HAL.
    • Handle: RePEc:hal:cesptp:halshs-00259225
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00259225v1
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    Cited by:

    1. Diongue, Abdou Kâ & Guégan, Dominique & Vignal, Bertrand, 2009. "Forecasting electricity spot market prices with a k-factor GIGARCH process," Applied Energy, Elsevier, vol. 86(4), pages 505-510, April.
    2. Montero, José M. & García-Centeno, Maria C. & Fernández-Avilés, Gema, 2011. "Modelling the Volatility of the Spanish Wholesale Electricity Spot Market. Asymmetric GARCH Models vs. Threshold ARSV model/Modelización de la volatilidad en el mercado eléctrico español. Modelos GARC," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 29, pages 597-616, Agosto.
    3. Faheem Jan & Ismail Shah & Sajid Ali, 2022. "Short-Term Electricity Prices Forecasting Using Functional Time Series Analysis," Energies, MDPI, vol. 15(9), pages 1-15, May.

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    More about this item

    Keywords

    A-PARCH; Asymmetric distribution function; electricity spot prices; Leptokurtosis; persistence; seasonality; GARMA; A-PARCH.; Distribution asymétrique; prix d'électricité; persistance; longue mémoire; A-P-ARCH; Gegenbauer.;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation 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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