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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.

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  • 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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    4. 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.
    5. Xiaopeng Guo & Jiaxing Shi & Dongfang Ren, 2016. "Coal Price Forecasting and Structural Analysis in China," Discrete Dynamics in Nature and Society, Hindawi, vol. 2016, pages 1-7, October.
    6. 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.
    7. 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.
    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).
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