Abstract
Many methods for assessing volatility and making forecasts in literature are applied, e.g. econometric models and soft computing models.
This is a preview of subscription content, log in via an institution to check access.
Notes
- 1.
GARCH class models require high density of data.
References
Abdullah SN, Zeng X (2010) Machine learning approach for crude oil price prediction with Artificial Neural Networks-Quantitative (ANN-Q) model. In: Proceedings of the international joint conference on neural networks. IEEE, pp 1–8. https://doi.org/10.1109/IJCNN.2010.5596602
Bacon R, Kojima M (2008) Coping with oil price volatility. energy sector management assistance program. In: The international bank for reconstruction and development/the world bank group: washington. Retrieved August 1, 2023, from https://www.esmap.org/sites/default/files/esmap-files/8142008101202_coping_oil_price.pdf
Bildirici M, Bayazit NG, Ucan Y (2020) Analyzing crude oil prices under the impact of COVID-19 by using LSTARGARCHLSTM. Energies 13:2980. https://doi.org/10.3390/en13112980
Bollerslev T (1986) Generalised autoregressive conditional heteroskedasticity. J Econom 31:307–327. https://doi.org/10.1016/0304-4076(86)90063-1
Chen Y, He K, Tso GKF (2017) Forecasting crude oil prices: a deep learning based model. Procedia Comput Sci 122:300–307. https://scholars.cityu.edu.hk/files/27925183/Forecasting_Crude_Oil_Prices_A_Deep_Learning_based_Model.pdf. Accessed 01 Aug 2023
Chkili W, Hammoudeh S, Nguyen DK (2014) Volatility forecasting and risk management for commodity markets in the presence of asymmetry and long memory. Energy Econ 41:1–18. https://doi.org/10.1016/j.eneco.2013.10.011
Dickey DA, Fuller WA (1979) Distribution of the estimators for autoregressive time series with a unit root. J Am Stat Assoc 75:427–431. https://doi.org/10.2307/2286348
Eurostat (2023) Share of fossil fuels in gross available energy. Retrieved September 6, 2023, from https://ec.europa.eu/eurostat/databrowser/view/NRG_IND_FFGAE__custom_4713488/bookmark/table?lang=en&bookmarkId=b28d8f1d-02c4-4ce5-bb5c-084990ee5a58
Fiszeder P (2001) Jednorównaniowe modele GARCH – analiza procesów zachodzących na GPW w Warszawie. Materiały na VII Ogólnopolskie Seminarium Naukowe: Dynamiczne Modele Ekonometryczne, pp 221–232
Fiszeder P (2009) Modele klasy GARCH w empirycznych badaniach finansowych. Wydawnictwo Uniwersytetu Mikołaja Kopernika w Toruniu
Gana L, Wang H, Yang Z (2020) Machine learning solutions to challenges in finance: an application to the pricing of financial products. Technol Forecast Soc Change 153:119928. https://doi.org/10.1016/j.techfore.2020.119928
García D, Kristjanpoller W (2019) An adaptive forecasting approach for copper price volatility through hybrid and non-hybrid models. Appl Soft Comput 74:466–478. https://doi.org/10.1016/j.asoc.2018.10.007
Herrera AM, Hu L, Pastor D (2018) Forecasting crude oil price volatility. Int J Forecast 34:622–635. https://doi.org/10.1016/j.ijforecast.2018.04.007
Klein T, Walther T (2016) Oil price volatility forecast with mixture memory GARCH. Energy Econ 58:46–58. https://doi.org/10.1016/j.eneco.2016.06.004
Kriechbaumer T, Angus A, Parsons D, Rivas Casado M (2014) An improved wavelet–ARIMA approach for forecasting metal prices. Resour Policy 39:32–41. https://doi.org/10.1016/j.resourpol.2013.10.005
Kumar D (2011) Forecasting energy futures volatility based on the unbiased extreme value volatility estimator. IIMB Manage Rev 29:294–310. https://doi.org/10.1016/j.iimb.2017.11.002
Lin Y, Xiao Y, Li F (2020) Forecasting crude oil price volatility via a HM-EGARCH model. Energy Econ 87:104693. https://doi.org/10.1016/j.eneco.2020.104693
Lv X, Shan X (2013) Modeling natural gas market volatility using GARCH with different distributions. Physica A 392:5685–5699. https://doi.org/10.1016/j.physa.2013.07.038
Nomikos NK, Pouliasis PK (2011) Forecasting petroleum futures markets volatility: the role of regimes and market conditions. Energy Econ 33:321–337. https://doi.org/10.1016/j.eneco.2010.11.013
Pesaran MH, Shin Y, Smith RJ (2001) Bounds testing approaches to the analysis of level relationships. J Appl Economet 16:289–326. https://doi.org/10.1002/jae.616
Phillips PCB, Perron P (1988) Testing for a unit root in time series regressions. Biometrica 75:335–346. https://doi.org/10.2307/2336182
Sehgal N, Pandey KK (2015) Artificial intelligence methods for oil price forecasting: a review and evaluation. Energy Syst 6:479–506. https://doi.org/10.1007/s12667-015-0151-y
Wang Y, Wu C (2012) Forecasting energy market volatility using GARCH models: can multivariate models beat univariate models? Energy Econ 34:2167–2181. https://doi.org/10.1016/j.eneco.2012.03.010
Zhang Y-J, Zhang J-L (2018) Volatility forecasting of crude oil market: a new hybrid method. J Forecast 37:781–789. https://doi.org/10.1002/for.2502
Author information
Authors and Affiliations
Corresponding authors
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Mróz, M., Korzeb, Z., Niedziółka, P. (2024). The Impact of Fossil Fuel Price Volatility on EU Energy, Oil and Gas Share Price Volatility. In: Fossil Fuels in the European Union. Lecture Notes in Energy, vol 99. Springer, Cham. https://doi.org/10.1007/978-3-031-56790-2_13
Download citation
DOI: https://doi.org/10.1007/978-3-031-56790-2_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-56789-6
Online ISBN: 978-3-031-56790-2
eBook Packages: EnergyEnergy (R0)