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Modelling and forecasting fossil fuels, CO2 and electricity prices and their volatilities

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  • García-Martos, Carolina
  • Rodríguez, Julio
  • Sánchez, María Jesús

Abstract

In the current uncertain context that affects both the world economy and the energy sector, with the rapid increase in the prices of oil and gas and the very unstable political situation that affects some of the largest raw materials’ producers, there is a need for developing efficient and powerful quantitative tools that allow to model and forecast fossil fuel prices, CO2 emission allowances prices as well as electricity prices. This will improve decision making for all the agents involved in energy issues.

Suggested Citation

  • García-Martos, Carolina & Rodríguez, Julio & Sánchez, María Jesús, 2013. "Modelling and forecasting fossil fuels, CO2 and electricity prices and their volatilities," Applied Energy, Elsevier, vol. 101(C), pages 363-375.
    • Handle: RePEc:eee:appene:v:101:y:2013:i:c:p:363-375
    DOI: 10.1016/j.apenergy.2012.03.046
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    3. Hakan Cetintas & I. Murat Bicil & Kumru Turkoz, 2017. "Turkiye'de Enerji Uretiminde Fosil Yakit Kullanimi ve CO2 Emisyonu Iliskisi: Bir Senaryo Analizi," EconWorld Working Papers 17002, WERI-World Economic Research Institute, revised Mar 2017.
    4. Liam Wagner & Lynette Molyneaux & John Foster, 2012. "Magnifying uncertainty: the impact of a shift to gas under a carbon price," Energy Economics and Management Group Working Papers 11-2012, School of Economics, University of Queensland, Australia.
    5. Piao Wang & Shahid Hussain Gurmani & Zhifu Tao & Jinpei Liu & Huayou Chen, 2024. "Interval time series forecasting: A systematic literature review," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 249-285, March.
    6. Katarzyna Maciejowska & Rafal Weron, 2015. "Short- and mid-term forecasting of baseload electricity prices in the UK: The impact of intra-day price relationships and market fundamentals," HSC Research Reports HSC/15/04, Hugo Steinhaus Center, Wroclaw University of Technology.
    7. Wagner, Liam & Molyneaux, Lynette & Foster, John, 2014. "The magnitude of the impact of a shift from coal to gas under a Carbon Price," Energy Policy, Elsevier, vol. 66(C), pages 280-291.
    8. Khosravi, Abbas & Nahavandi, Saeid & Creighton, Doug, 2013. "Quantifying uncertainties of neural network-based electricity price forecasts," Applied Energy, Elsevier, vol. 112(C), pages 120-129.
    9. Wang, Jujie & Cui, Quan & He, Maolin, 2022. "Hybrid intelligent framework for carbon price prediction using improved variational mode decomposition and optimal extreme learning machine," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    10. Rita Sousa & Luís Aguiar-Conraria, 2014. "Dynamics of CO2 price drivers," NIPE Working Papers 02/2014, NIPE - Universidade do Minho.
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