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Bayesian calibration and number of jump components in electricity spot price models

Author

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  • Gonzalez, Jhonny
  • Moriarty, John
  • Palczewski, Jan

Abstract

We find empirical evidence that mean-reverting jump processes are not statistically adequate to model electricity spot price spikes but independent, signed sums of such processes are statistically adequate. Further we demonstrate a change in the composition of these sums after a major economic event. This is achieved by developing a Markov Chain Monte Carlo (MCMC) procedure for Bayesian model calibration and a Bayesian assessment of model adequacy (posterior predictive checking). In particular we determine the number of signed mean-reverting jump components required in the APXUK and EEX markets, in time periods both before and after the recent global financial crises. Statistically, consistent structural changes occur across both markets, with a reduction of the intensity and size, or the disappearance, of positive price spikes in the later period. All code and data are provided to enable replication of results.

Suggested Citation

  • Gonzalez, Jhonny & Moriarty, John & Palczewski, Jan, 2017. "Bayesian calibration and number of jump components in electricity spot price models," Energy Economics, Elsevier, vol. 65(C), pages 375-388.
    • Handle: RePEc:eee:eneeco:v:65:y:2017:i:c:p:375-388
    DOI: 10.1016/j.eneco.2017.04.022
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Deschatre, Thomas & Féron, Olivier & Gruet, Pierre, 2021. "A survey of electricity spot and futures price models for risk management applications," Energy Economics, Elsevier, vol. 102(C).
    2. Sirin, Selahattin Murat & Camadan, Ercument & Erten, Ibrahim Etem & Zhang, Alex Hongliang, 2023. "Market failure or politics? Understanding the motives behind regulatory actions to address surging electricity prices," Energy Policy, Elsevier, vol. 180(C).
    3. Hinderks, W.J. & Wagner, A., 2020. "Factor models in the German electricity market: Stylized facts, seasonality, and calibration," Energy Economics, Elsevier, vol. 85(C).
    4. Thomas Deschatre & Olivier F'eron & Pierre Gruet, 2021. "A survey of electricity spot and futures price models for risk management applications," Papers 2103.16918, arXiv.org, revised Jul 2021.
    5. Trespalacios, Alfredo & Cortés, Lina M. & Perote, Javier, 2020. "Uncertainty in electricity markets from a semi-nonparametric approach," Energy Policy, Elsevier, vol. 137(C).
    6. Christian Laudag'e & Florian Aichinger & Sascha Desmettre, 2023. "A Comparative Study of Factor Models for Different Periods of the Electricity Spot Price Market," Papers 2306.07731, arXiv.org, revised Apr 2024.

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    More about this item

    Keywords

    Multi-factor models; Bayesian calibration; Markov Chain Monte Carlo; Ornstein-Uhlenbeck process; Electricity spot price; Negative jumps;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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