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Unveiling endogeneity and temporal dependence in energy prices and demand in Iberian countries: a stochastic hidden Markov model approach

Author

Listed:
  • Jorge Antunes

    (Federal University of Rio de Janeiro)

  • Luis Alberiko Gil-Alana

    (University of Navarra)

  • Rossana Riccardi

    (Università Degli Studi di Brescia)

  • Yong Tan

    (University of Huddersfield)

  • Peter Wanke

    (Federal University of Rio de Janeiro)

Abstract

In this paper, we analyze the temporal dependence in energy prices and demand using daily data of Portugal and Spain over the period 2007–2017. The methodology used is based on a stochastic Hidden Markov Model and the results indicate first that all significant relationships between energy prices and demands were found to be positive; second, spot prices are only time dependent on future prices and spot energy, while future energy is solely time dependent on spot energy behavior; third, future prices are not only autocorrelated but also time-dependent with spot energy and future energy demands level; and finally, spot energy is autocorrelated and time-dependent with future prices and future energy. Policy implications of the results obtained are presented at the end of the article.

Suggested Citation

  • Jorge Antunes & Luis Alberiko Gil-Alana & Rossana Riccardi & Yong Tan & Peter Wanke, 2022. "Unveiling endogeneity and temporal dependence in energy prices and demand in Iberian countries: a stochastic hidden Markov model approach," Annals of Operations Research, Springer, vol. 313(1), pages 191-229, June.
    • Handle: RePEc:spr:annopr:v:313:y:2022:i:1:d:10.1007_s10479-021-04211-z
    DOI: 10.1007/s10479-021-04211-z
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    More about this item

    Keywords

    Renewables; Decarbonization; Iberian countries; Energy demand; Energy prices; Stochastic HMM; Copulas; Bootstrapped VAR models;
    All these keywords.

    JEL classification:

    • 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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