Research on Forecasting International Energy Price Volatility based on Model Combination
DOI:
https://doi.org/10.62051/r21q6j75Keywords:
International energy markets; Volatility forecasting; Model combination; Macroeconomic variables; Uncertainty index.Abstract
As an important support for the national economy, the volatility of international energy price is of great importance to the stability of the national economy, the maintenance of social stability and the protection of national security. The modeling and forecasting of international energy market price volatility based on many forecasting factors have attracted more and more attention. The model combination method, which can integrate the strengths of various models, is widely used in the field of financial forecasting. In view of this, based on the monthly volatility data of international energy market, macroeconomic variables, uncertainty index and AR extended model from January 1994 to December 2022, this paper adopts five model combination forecasting methods for the prediction of international energy price volatility, and the adaptability of the model combination to different predictors and its performance in predicting the volatility of different energy markets are discussed. Through empirical study, it is found that the forecasting ability of single macroeconomic variable is not satisfactory when forecasting international energy price volatility, but the model combination forecasting method can significantly improve the forecasting accuracy of international crude oil market volatility, and the model combination forecasting based on the uncertainty index performs well in both the crude oil market and the natural gas market. Therefore, this paper can provide some empirical basis for international energy market investors to carry out risk management and portfolio optimization, and also help policy makers grasp the timing of policy issuance and implementation.
Downloads
References
[1] He Y N, Wang S Y. World economy and international crude oil prices: A cointegration analysis based on the Kilian economic index[J]. Systems Engineering — Theory & Practice, 2011, 31(2): 221-228.
[2] Wang T, Zhang D, David C. Financialization, fundamentals, and the time-varying determinants of US natural gas prices[J]. Energy Economics, 2019, 80: 707-719. DOI: https://doi.org/10.1016/j.eneco.2019.01.026
[3] Xing W T, Zhang Z Y, Wu S L. Natural gas futures pricing model considering price jumps and empirical analysis[J]. Journal of Systems & Management, 2017, 26(3): 496-501.
[4] Xie N, Gong X, Lin B Q, et al. Impacts of China’s oil demand and global oil inventories on international oil prices[J]. Modern Economic Science, 2018, 40(4): 21-28.
[5] Li G J, Wu Q N, Zheng X Y. The interaction among oil inventories, time spread structure and international oil prices[J]. International Petroleum Economics, 2023, 31(5): 63-67.
[6] Liu S, Huang S, Chi Y, et al. Three-level network analysis of the North American natural gas price: A multiscale perspective[J]. International Review of Financial Analysis, 2020, 67: 101420. DOI: https://doi.org/10.1016/j.irfa.2019.101420
[7] Roger C D, Andre Y. The value of the US dollar and its impact on oil prices: Evidence from a non-linear asymmetric cointegration approach[J]. Energy Economics, 2018, 70(3): 61-69. DOI: https://doi.org/10.1016/j.eneco.2017.12.027
[8] Han L Y, Zhen Z, Cai L X. Short‑run and long‑run driving factors of international oil prices[J]. Chinese Journal of Management Science, 2017, 25(8): 68-78.
[9] Wang P P, Xia T, Shi J X, et al. Time varying characteristics and influencing factors of the dynamic oil dollar linkage[J]. Studies of International Finance, 2020, 11: 35-44.
[10] Li Z, Shi Q, Bu L. Is geopolitical risk a driver of international crude oil price volatility? Evidence from the GARCH‑MIDAS model[J]. World Economy Studies, 2021, 11: 18-32+135.
[11] Mei D, Ma F, Liao Y, et al. Geopolitical risk uncertainty and oil future volatility: Evidence from MIDAS models[J]. Energy Economics, 2020, 86: 104624. DOI: https://doi.org/10.1016/j.eneco.2019.104624
[12] Yang K, Wei Y, Li S W, et al. Does geopolitical risk drive China’s crude oil futures market? A wavelet-based nonparametric causality-in-quantiles test[J]. Chinese Journal of Management Science, 2023, 31(2): 51-62.
[13] Wang L, Ma F, Niu T, et al. The importance of extreme shock: Examining the effect of investor sentiment on the crude oil futures market[J]. Energy Economics, 2021, 99: 105319. DOI: https://doi.org/10.1016/j.eneco.2021.105319
[14] Feng Y Y, Liu C, Sun X L. Measuring the interaction between uncertainty and crude oil market: A multiscale methodology based on synthetic integration[J]. Management Review, 2020, 32(7): 29-40.
[15] Tang Z P, Zhang T T, Wu J C, et al. Multistep-Ahead stock price forecasting based on secondary decomposition technique and extreme learning machine optimized by the differential evolution algorithm[J]. Mathematical Problems in Engineering, 2020, 01: 1-13. DOI: https://doi.org/10.1155/2020/2604915
[16] Lv W D, Qi J P. Stock market return predictability: A combination forecast perspective[J]. International Review of Financial Analysis, 2022, 84: 102376. DOI: https://doi.org/10.1016/j.irfa.2022.102376
[17] Ren S S, Wang X, Zhou X, Zhou Y. A novel hybrid model for stock price forecasting integrating Encoder Forest and Informer[J]. Expert Systems with Applications, 2023, 234: 121080. DOI: https://doi.org/10.1016/j.eswa.2023.121080
[18] Kim H Y, Won C H. Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models[J]. Expert Systems with Applications, 2018, 113: 25-37. DOI: https://doi.org/10.1016/j.eswa.2018.03.002
[19] Lin Y, Lin Z X, Liao Y, Li Y Z, et al. Forecasting the realized volatility of stock price index: A hybrid model integrating CEEMDAN and LSTM[J]. Expert Systems with Applications, 2022, 206: 117736. DOI: https://doi.org/10.1016/j.eswa.2022.117736
[20] Zhang C, Zhou X. Forecasting value-at-risk of crude oil futures using a hybrid ARIMA-SVR-POT model[J]. Heliyon, 2023, 10(1): e23358. DOI: https://doi.org/10.1016/j.heliyon.2023.e23358
[21] Zhao G Y, Xue M G. Prediction of the next-day price change direction and volatility magnitude of crude oil futures based on the TN-LP-LSTM-SVM hybrid model[J]. Control and Decision, 2022, 37(10): 2627-2636.
[22] Zhang X, Cheng S, Dong R, et al. Selective ensemble forecasting method for oil prices based on scale analysis and dynamic error correction[J]. Journal of Systems Science and Mathematical Sciences, 2023, 43(10): 2451-2466.
[23] Abrishami H, Varahrami V. Different methods for gas price forecasting[J]. Cuadernos de Economía. 2011, 34(96): 137-144. DOI: https://doi.org/10.1016/S0210-0266(11)70013-9
[24] Yang J, Feng Y, Huang Q. A study on Brent crude oil price volatility based on the MHPSO-NHMM-FIEGARCH-GED model[J]. Chinese Journal of Management Science, 2023, 31(6): 265-275.
[25] Liang C, Xia Z, Lai X, et al. Natural gas volatility prediction: Fresh evidence from extreme weather and extended GARCH-MIDAS-ES model[J]. Energy Economics, 2022, 116: 106437. DOI: https://doi.org/10.1016/j.eneco.2022.106437
[26] Christiansen C, Schmeling M, Schrimpf A. A comprehensive look at financial volatility prediction by economic variables[J]. Journal of Applied Econometrics, 2012, 27(6): 956-977. DOI: https://doi.org/10.1002/jae.2298
[27] Paye B S. 'Déjà vol': Predictive regressions for aggregate stock market volatility using macroeconomic variables[J]. Journal of Financial Economics, 2012, 106(3): 527-546. DOI: https://doi.org/10.1016/j.jfineco.2012.06.005
[28] Wang Y, Wei Y, Wu C, Yin L. Oil and the short-term predictability of stock return volatility[J]. Journal of Empirical Finance, 2018, 47:90-104. DOI: https://doi.org/10.1016/j.jempfin.2018.03.002
[29] Liang C, Ma F, Li Z Y, Li Y. Which types of commodity price information are more useful for predicting US stock market volatility? [J]. Economic Modelling, 2020, 93: 642-650. DOI: https://doi.org/10.1016/j.econmod.2020.03.022
[30] Rapach D E, Strauss J K, Zhou G. Out-of-Sample equity premium prediction: Combination forecasts and links to the real economy[J]. Review of Financial Studies, 2010, 23(2): 821-862. DOI: https://doi.org/10.1093/rfs/hhp063
[31] Wen D Y, He M X, Wang Y D, Zhang Y J. Forecasting crude oil market volatility: A comprehensive look at uncertainty variables[J]. International Journal of Forecasting, 2023, 09: 002.
[32] Liang C, Umar M, Ma F, Huynh T. Climate policy uncertainty and world renewable energy index volatility forecasting[J]. Technological Forecasting and Social Change, 2022, 182: 121810. DOI: https://doi.org/10.1016/j.techfore.2022.121810
[33] Welch I, Goyal A. A comprehensive look at the empirical performance of equity premium prediction[J]. Review of Financial Studies, 2008, 21: 1455-1508. DOI: https://doi.org/10.1093/rfs/hhm014
[34] Hubert D, Wolfgang D, Andreas N, et al. Data snooping in equity premium prediction[J]. International Journal of Forecasting, 2021, 37(1): 72-94. DOI: https://doi.org/10.1016/j.ijforecast.2020.03.002
[35] Wang Y D, Pan Z Y, Liu L, Wu C F. Oil price increases and the predictability of equity premium[J]. Journal of Banking & Finance, 2019, 102: 43-58. DOI: https://doi.org/10.1016/j.jbankfin.2019.03.009
[36] Hailemariam A, Smyth R. What drives volatility in natural gas prices? [J]. Energy Economics, 2019, 80: 731-742. DOI: https://doi.org/10.1016/j.eneco.2019.02.011
[37] Zheng Y, Luo J, Chen J, et al. Natural gas spot price prediction research under the background of Russia-Ukraine conflict-based on FS-GA-SVR hybrid model[J]. Journal of Environmental Management, 2023, 344: 118446. DOI: https://doi.org/10.1016/j.jenvman.2023.118446
[38] Liang C, Ma F, Wang L, Zeng Q. The information content of uncertainty indices for natural gas futures volatility forecasting[J]. Journal of Forecasting, 2021, 40(7): 1310-1324. DOI: https://doi.org/10.1002/for.2769
[39] Liu J, Kemp A. Forecasting the sign of U.S. oil and gas industry stock index excess returns employing macroeconomic variables[J]. Energy Economics, 2019, 81: 672-686. DOI: https://doi.org/10.1016/j.eneco.2019.04.023
[40] Gong X, Lin B. The incremental information content of investor fear gauge for volatility forecasting in the crude oil futures market[J]. Energy Economics, 2018, 74: 370-386. DOI: https://doi.org/10.1016/j.eneco.2018.06.005
[41] Lin Y, Xiao Y, Li F. Forecasting crude oil price volatility via a HM-EGARCH model[J]. Energy Economics, 2020, 87: 04693. DOI: https://doi.org/10.1016/j.eneco.2020.104693
[42] Zhang Y J, Wahab M I M, Wang Y D. Forecasting crude oil market volatility using variable selection and common factor[J]. International Journal of Forecasting, 2023, 39(1): 486-502. DOI: https://doi.org/10.1016/j.ijforecast.2021.12.013
[43] Baker S R, Bloom N, Davis S J. Measuring economic policy uncertainty[J]. The Quarterly Journal of Economics, 2016, 131: 1593-1636. DOI: https://doi.org/10.1093/qje/qjw024
[44] Dutta A, Bouri E, Saeed T. News-based equity market uncertainty and crude oil volatility[J]. Energy, 2021, 222: 119930. DOI: https://doi.org/10.1016/j.energy.2021.119930
[45] Zhang Y J, He M X, Wang Y D, Liang C. Global economic policy uncertainty aligned: An informative predictor for crude oil market volatility[J]. International Journal of Forecasting, 2023, 39(3): 1318-1332. DOI: https://doi.org/10.1016/j.ijforecast.2022.07.002
[46] Husted L, Rogers J, Sun B. Uncertainty, currency excess returns, and risk reversals[J]. Journal of International Money and Finance, 2018, 88: 228-241. DOI: https://doi.org/10.1016/j.jimonfin.2017.07.011
[47] Bekaert G, Engstrom E C, Xu N R. The time variation in risk appetite and uncertainty[J]. Management Science, 2022, 68: 3975-4004. DOI: https://doi.org/10.1287/mnsc.2021.4068
[48] Fernandes M, Medeiros M C, Scharth M. Modeling and predicting the CBOE market volatility index[J]. Journal of Banking & Finance, 2014, 40: 1-10. DOI: https://doi.org/10.1016/j.jbankfin.2013.11.004
[49] Jurado K, Ludvigson S C, Ng S. Measuring uncertainty[J]. American Economic Review, 2015, 105: 1177-121. DOI: https://doi.org/10.1257/aer.20131193
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Transactions on Environment, Energy and Earth Sciences
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
