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Forecasting crude oil price volatility via a HM-EGARCH model

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  1. Fulvia Pennoni & Francesco Bartolucci & Gianfranco Forte & Ferdinando Ametrano, 2022. "Exploring the dependencies among main cryptocurrency log‐returns: A hidden Markov model," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 51(1), February.
  2. Sherzod N. Tashpulatov, 2022. "Modeling Electricity Price Dynamics Using Flexible Distributions," Mathematics, MDPI, vol. 10(10), pages 1-15, May.
  3. Lin, Yu & Yan, Yan & Xu, Jiali & Liao, Ying & Ma, Feng, 2021. "Forecasting stock index price using the CEEMDAN-LSTM model," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
  4. Dimitrios Vortelinos & Angeliki Menegaki & Ioannis Passas & Alexandros Garefalakis & Georgios Viskadouros, 2024. "Heterogeneous Responses of Energy and Non-Energy Assets to Crises in Commodity Markets," Energies, MDPI, vol. 17(21), pages 1-25, October.
  5. Cao, Yanyan & Xiang, Shihui, 2023. "Natural resources volatility and causal associations for BRICS countries: Evidence from Covid-19 data," Resources Policy, Elsevier, vol. 80(C).
  6. Abdollahi, Hooman & Ebrahimi, Seyed Babak, 2020. "A new hybrid model for forecasting Brent crude oil price," Energy, Elsevier, vol. 200(C).
  7. Mustofa Usman & M. Komarudin & Munti Sarida & Wamiliana Wamiliana & Edwin Russel & Mahatma Kufepaksi & Iskandar Ali Alam & Faiz A.M. Elfaki, 2022. "Analysis of Some Variable Energy Companies by Using VAR(p)-GARCH(r,s) Model : Study From Energy Companies of Qatar over the Years 2015 2022," International Journal of Energy Economics and Policy, Econjournals, vol. 12(5), pages 178-191, September.
  8. He, Huizi & Sun, Mei & Li, Xiuming & Mensah, Isaac Adjei, 2022. "A novel crude oil price trend prediction method: Machine learning classification algorithm based on multi-modal data features," Energy, Elsevier, vol. 244(PA).
  9. Lu, Fei & Ma, Feng & Guo, Qiang, 2023. "Less is more? New evidence from stock market volatility predictability," International Review of Financial Analysis, Elsevier, vol. 89(C).
  10. Patra, Saswat, 2024. "An empirical analysis of the volume-volatility nexus in crude oil markets under structural breaks: Implications for forecasting," International Review of Economics & Finance, Elsevier, vol. 94(C).
  11. Feng Ma & M. I. M. Wahab & Julien Chevallier & Ziyang Li, 2023. "A tug of war of forecasting the US stock market volatility: Oil futures overnight versus intraday information," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 60-75, January.
  12. Xiaowen Wang & Ying Ma & Wen Li, 2021. "The Prediction of Gold Futures Prices at the Shanghai Futures Exchange Based on the MEEMD-CS-Elman Model," SAGE Open, , vol. 11(1), pages 21582440211, March.
  13. Marcin Fałdziński & Piotr Fiszeder & Witold Orzeszko, 2020. "Forecasting Volatility of Energy Commodities: Comparison of GARCH Models with Support Vector Regression," Energies, MDPI, vol. 14(1), pages 1-18, December.
  14. Harrison, Andre & Liu, Xiaochun & Stewart, Shamar L., 2023. "Structural sources of oil market volatility and correlation dynamics," Energy Economics, Elsevier, vol. 121(C).
  15. Abdollahi, Hooman, 2020. "A novel hybrid model for forecasting crude oil price based on time series decomposition," Applied Energy, Elsevier, vol. 267(C).
  16. Xiafei Li & Dongxin Li & Xuhui Zhang & Guiwu Wei & Lan Bai & Yu Wei, 2021. "Forecasting regular and extreme gold price volatility: The roles of asymmetry, extreme event, and jump," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1501-1523, December.
  17. Wen, Danyan & He, Mengxi & Wang, Yudong & Zhang, Yaojie, 2024. "Forecasting crude oil market volatility: A comprehensive look at uncertainty variables," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1022-1041.
  18. Sherzod N. Tashpulatov, 2021. "Modeling and Estimating Volatility of Day-Ahead Electricity Prices," Mathematics, MDPI, vol. 9(7), pages 1-11, March.
  19. Wen, Jun & Mughal, Nafeesa & Kashif, Maryam & Jain, Vipin & Ramos Meza, Carlos Samuel & Cong, Phan The, 2022. "Volatility in natural resources prices and economic performance: Evidence from BRICS economies," Resources Policy, Elsevier, vol. 75(C).
  20. Elena Villar-Rubio & María-Dolores Huete-Morales & Federico Galán-Valdivieso, 2023. "Using EGARCH models to predict volatility in unconsolidated financial markets: the case of European carbon allowances," Journal of Environmental Studies and Sciences, Springer;Association of Environmental Studies and Sciences, vol. 13(3), pages 500-509, September.
  21. Cui, Lianbiao & Weng, Shimei & Kirikkaleli, Dervis & Bashir, Muhammad Adnan & Rjoub, Husam & Zhou, Yuanxiang, 2021. "Exploring the role of natural resources, natural gas and oil production for economic growth of China," Resources Policy, Elsevier, vol. 74(C).
  22. Liang, Xuedong & Luo, Peng & Li, Xiaoyan & Wang, Xia & Shu, Lingli, 2023. "Crude oil price prediction using deep reinforcement learning," Resources Policy, Elsevier, vol. 81(C).
  23. Jiang, He & Hu, Weiqiang & Xiao, Ling & Dong, Yao, 2022. "A decomposition ensemble based deep learning approach for crude oil price forecasting," Resources Policy, Elsevier, vol. 78(C).
  24. Zeyi Fu & Hongli Niu & Weiqing Wang, 2023. "Market Efficiency and Cross-Correlations of Chinese New Energy Market with Other Assets: Evidence from Multifractality Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 62(3), pages 1287-1311, October.
  25. Liu, Yuanyuan & Niu, Zibo & Suleman, Muhammad Tahir & Yin, Libo & Zhang, Hongwei, 2022. "Forecasting the volatility of crude oil futures: The role of oil investor attention and its regime switching characteristics under a high-frequency framework," Energy, Elsevier, vol. 238(PA).
  26. Wang, Xuerui & Li, Xiangyu & Li, Shaoting, 2022. "Point and interval forecasting system for crude oil price based on complete ensemble extreme-point symmetric mode decomposition with adaptive noise and intelligent optimization algorithm," Applied Energy, Elsevier, vol. 328(C).
  27. Liang, Chao & Li, Yan & Ma, Feng & Wei, Yu, 2021. "Global equity market volatilities forecasting: A comparison of leverage effects, jumps, and overnight information," International Review of Financial Analysis, Elsevier, vol. 75(C).
  28. Panagiotis Delis & Stavros Degiannakis & George Filis, 2022. "What matters when developing oil price volatility forecasting frameworks?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 361-382, March.
  29. Tong Liu & Yanlin Shi, 2022. "Forecasting Crude Oil Future Volatilities with a Threshold Zero-Drift GARCH Model," Mathematics, MDPI, vol. 10(15), pages 1-20, August.
  30. Yingying Xu & Donald Lien, 2022. "Forecasting volatilities of oil and gas assets: A comparison of GAS, GARCH, and EGARCH models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 259-278, March.
  31. Guan, Keqin & Gong, Xu, 2023. "A new hybrid deep learning model for monthly oil prices forecasting," Energy Economics, Elsevier, vol. 128(C).
  32. Sherzod N. Tashpulatov, 2021. "The Impact of Regulatory Reforms on Demand Weighted Average Prices," Mathematics, MDPI, vol. 9(10), pages 1-15, May.
  33. Bonnier, Jean-Baptiste, 2022. "Forecasting crude oil volatility with exogenous predictors: As good as it GETS?," Energy Economics, Elsevier, vol. 111(C).
  34. Qin Lu & Jingwen Liao & Kechi Chen & Yanhui Liang & Yu Lin, 2024. "Predicting Natural Gas Prices Based on a Novel Hybrid Model with Variational Mode Decomposition," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 639-678, February.
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