1. Integrating sentiment information for risk prediction: the case of crude oil futures market in China. (2025). Zhang, Lin ; Lu, Yunguo ; Jiang, Zhe.
   In: Empirical Economics.
   RePEc:spr:empeco:v:68:y:2025:i:4:d:10.1007_s00181-024-02678-w.

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2. Volatility forecasting: a new GARCH-type model for fuzzy sets-valued time series. (2025). Dai, Xingyu ; Cerqueti, Roy ; Wang, Qunwei ; Xiao, Ling.
   In: Annals of Operations Research.
   RePEc:spr:annopr:v:348:y:2025:i:1:d:10.1007_s10479-023-05746-z.

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3. Environmental attention and the predictability of crude oil volatility: Evidence from a new MIDAS multifractal model. (2025). Dong, Xin ; Gong, Jinguo ; Wang, Qin.
   In: Energy Economics.
   RePEc:eee:eneeco:v:143:y:2025:i:c:s0140988325000507.

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4. Out‐of‐sample volatility prediction: Rolling window, expanding window, or both?. (2024). Zhang, Yaojie ; Wang, Yudong ; Feng, Yuqing.
   In: Journal of Forecasting.
   RePEc:wly:jforec:v:43:y:2024:i:3:p:567-582.

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5. Investigating the Impact of Agricultural, Financial, Economic, and Political Factors on Oil Forward Prices and Volatility: A SHAP Analysis. (2024). Choi, Sun-Yong ; Kim, Hyeon-Seok.
   In: Energies.
   RePEc:gam:jeners:v:17:y:2024:i:5:p:1001-:d:1342719.

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6. Influence of Ukraine invasion by Russia on Turkish markets. (2024). Tanrivermi, Yesim ; Salami, Monsurat Ayojimi.
   In: The Journal of Economic Asymmetries.
   RePEc:eee:joecas:v:29:y:2024:i:c:s1703494923000609.

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7. Predicting the volatility of major energy commodity prices: The dynamic persistence model. (2024). Vacha, Lukas ; Baruník, Jozef ; Vcha, Luk ; Barunk, Jozef.
   In: Energy Economics.
   RePEc:eee:eneeco:v:140:y:2024:i:c:s014098832400690x.

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8. A time-frequency-based interval decomposition ensemble method for forecasting gasoil prices under the trend of low-carbon development. (2024). Yan, Zichun ; Sun, Yuying ; Wang, Shouyang ; Tian, Fangzhu.
   In: Energy Economics.
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9. Sparse Interval-valued Time Series Modeling with Machine Learning. (2024). Wang, Shouyang ; Sun, Yuying ; Hong, Yongmiao ; Bao, Haowen.
   In: Papers.
   RePEc:arx:papers:2411.09452.

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10. Forecasting Industrial Commodity Prices : Literature Review and a Model Suite. (2023). Ohnsorge, Franziska Lieselotte ; Khadan, Jeetendra ; Marioli, Francisco Arroyo ; Yamazaki, Takefumi.
    In: Policy Research Working Paper Series.
    RePEc:wbk:wbrwps:10611.

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11. International stock market volatility: A data-rich environment based on oil shocks. (2023). Lu, Xinjie ; Wang, Tianyang ; Ma, Feng ; Wen, Fenghua.
    In: Journal of Economic Behavior & Organization.
    RePEc:eee:jeborg:v:214:y:2023:i:c:p:184-215.

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12. Global economic policy uncertainty aligned: An informative predictor for crude oil market volatility. (2023). Zhang, Yaojie ; Wang, Yudong ; He, Mengxi ; Liang, Chao.
    In: International Journal of Forecasting.
    RePEc:eee:intfor:v:39:y:2023:i:3:p:1318-1332.

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13. Forecasting oil futures realized range‐based volatility with jumps, leverage effect, and regime switching: New evidence from MIDAS models. (2022). Lu, Xinjie ; Wang, Jiqian ; Liu, Jing ; Ma, Feng.
    In: Journal of Forecasting.
    RePEc:wly:jforec:v:41:y:2022:i:4:p:853-868.

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14. Which predictor is more predictive for Bitcoin volatility? And why?. (2022). Zhang, Yaojie ; Li, Xiafei ; Ma, Feng ; Liang, Chao.
    In: International Journal of Finance & Economics.
    RePEc:wly:ijfiec:v:27:y:2022:i:2:p:1947-1961.

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15. Predicting the volatility of crude oil futures: The roles of leverage effects and structural changes. (2022). Lin, Boqiang ; Gong, XU.
    In: International Journal of Finance & Economics.
    RePEc:wly:ijfiec:v:27:y:2022:i:1:p:610-640.

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16. The role of intermediary capital risk in predicting oil volatility. (2022). Yin, Libo.
    In: International Journal of Finance & Economics.
    RePEc:wly:ijfiec:v:27:y:2022:i:1:p:401-416.

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17. Quantitative modelling frontiers: a literature review on the evolution in financial and risk modelling after the financial crisis (2008–2019). (2022). Vogl, Markus.
    In: SN Business & Economics.
    RePEc:spr:snbeco:v:2:y:2022:i:12:d:10.1007_s43546-022-00359-3.

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18. A decomposition ensemble based deep learning approach for crude oil price forecasting. (2022). Dong, Yao ; Hu, Weiqiang ; Xiao, Ling ; Jiang, HE.
    In: Resources Policy.
    RePEc:eee:jrpoli:v:78:y:2022:i:c:s0301420722003014.

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19. Multi-perspective crude oil price forecasting with a new decomposition-ensemble framework. (2022). Zhao, Zhengling ; Sun, Shaolong ; Guo, Jingjun.
    In: Resources Policy.
    RePEc:eee:jrpoli:v:77:y:2022:i:c:s0301420722001854.

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20. Impact of financial instability on international crude oil volatility: New sight from a regime-switching framework. (2022). Hong, Yanran ; Umar, Muhammad ; Wang, LU ; Liang, Chao.
    In: Resources Policy.
    RePEc:eee:jrpoli:v:77:y:2022:i:c:s0301420722001155.

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21. Uncertainty and oil volatility: Evidence from shrinkage method. (2022). He, Xiaofeng ; Li, Pan ; Wang, Jiqian ; Ma, Feng.
    In: Resources Policy.
    RePEc:eee:jrpoli:v:75:y:2022:i:c:s0301420721004906.

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22. Forecasting Chinas crude oil futures volatility: New evidence from the MIDAS-RV model and COVID-19 pandemic. (2022). Chen, Zhonglu ; Ye, Yong ; Li, Xiafei.
    In: Resources Policy.
    RePEc:eee:jrpoli:v:75:y:2022:i:c:s030142072100461x.

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23. A novel crude oil price trend prediction method: Machine learning classification algorithm based on multi-modal data features. (2022). Li, Xiuming ; Sun, Mei ; Mensah, Isaac Adjei ; He, Huizi.
    In: Energy.
    RePEc:eee:energy:v:244:y:2022:i:pa:s0360544221029558.

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24. Forecasting the real prices of crude oil: A robust weighted least squares approach. (2022). Wang, Yudong ; Hao, Xianfeng.
    In: Energy Economics.
    RePEc:eee:eneeco:v:116:y:2022:i:c:s0140988322005345.

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25. Forecasting crude oil volatility with uncertainty indicators: New evidence. (2022). Chen, Zhonglu ; Li, Xiafei ; Umar, Muhammad ; Liang, Chao.
    In: Energy Economics.
    RePEc:eee:eneeco:v:108:y:2022:i:c:s0140988322001141.

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26. Out-of-sample prediction of Bitcoin realized volatility: Do other cryptocurrencies help?. (2022). Zhang, Yaojie ; Yi, Yongsheng ; He, Mengxi.
    In: The North American Journal of Economics and Finance.
    RePEc:eee:ecofin:v:62:y:2022:i:c:s106294082200081x.

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27. Regime Switching Mechanism during Energy Futures Price Bubbles. (2022). Koy, Ayben.
    In: International Journal of Energy Economics and Policy.
    RePEc:eco:journ2:2022-01-46.

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28. Effects of structural changes on the prediction of downside volatility in futures markets. (2021). Lin, Boqiang ; Gong, XU.
    In: Journal of Futures Markets.
    RePEc:wly:jfutmk:v:41:y:2021:i:7:p:1124-1153.

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29. Forecasting the Crude Oil Prices Volatility With Stochastic Volatility Models. (2021). Yaobin, Liu ; Oyuna, Dondukova.
    In: SAGE Open.
    RePEc:sae:sagope:v:11:y:2021:i:3:p:21582440211026269.

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30. Economic Analysis and Generic Algorithm for Optimizing the Investments Decision-Making Process in Oil Field Development. (2021). Popescu, Catalin ; Gheorghiu, Sorin Alexandru.
    In: Energies.
    RePEc:gam:jeners:v:14:y:2021:i:19:p:6119-:d:643331.

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31. Forecasting crude oil volatility with geopolitical risk: Do time-varying switching probabilities play a role?. (2021). Gao, Xinxin ; Wang, LU ; Hao, Jianyang ; Ma, Feng.
    In: International Review of Financial Analysis.
    RePEc:eee:finana:v:76:y:2021:i:c:s1057521921000983.

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32. Global equity market volatilities forecasting: A comparison of leverage effects, jumps, and overnight information. (2021). Wei, YU ; Li, Yan ; Ma, Feng ; Liang, Chao.
    In: International Review of Financial Analysis.
    RePEc:eee:finana:v:75:y:2021:i:c:s1057521921000922.

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33. What drives volatility of the U.S. oil and gas firms?. (2021). Lyócsa, Štefan ; Lyocsa, Tefan ; Todorova, Neda.
    In: Energy Economics.
    RePEc:eee:eneeco:v:100:y:2021:i:c:s014098832100270x.

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34. A hybrid model for carbon price forecastingusing GARCH and long short-term memory network. (2021). Wang, Qunwei ; Huang, Yumeng ; Dai, Xingyu ; Zhou, Dequn.
    In: Applied Energy.
    RePEc:eee:appene:v:285:y:2021:i:c:s0306261921000489.

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35. Oil Price Volatility Models during Coronavirus Crisis: Testing with Appropriate Models Using Further Univariate GARCH and Monte Carlo Simulation Models. (2021). Hadhek, Zouhaier ; Bouazizi, Tarek ; Lassoued, Mongi.
    In: International Journal of Energy Economics and Policy.
    RePEc:eco:journ2:2021-01-35.

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36. A detailed look at crude oil price volatility prediction using macroeconomic variables. (2020). Nonejad, Nima.
    In: Journal of Forecasting.
    RePEc:wly:jforec:v:39:y:2020:i:7:p:1119-1141.

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37. Predictive ability and economic gains from volatility forecast combinations. (2020). Skintzi, Vasiliki ; Fameliti, Stavroula P.
    In: Journal of Forecasting.
    RePEc:wly:jforec:v:39:y:2020:i:2:p:200-219.

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38. The Implications of Policy Uncertainty on Solar Photovoltaic Investment. (2020). Assereto, Martina ; Byrne, Julie.
    In: Energies.
    RePEc:gam:jeners:v:13:y:2020:i:23:p:6233-:d:451620.

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39. The (in)efficiency of NYMEX energy futures: A multifractal analysis. (2020). , Leonardo.
    In: Physica A: Statistical Mechanics and its Applications.
    RePEc:eee:phsmap:v:556:y:2020:i:c:s0378437120303952.

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40. Forecasting crude oil price with multilingual search engine data. (2020). Wang, Shouyang ; Tang, Ling ; Li, Jingjing.
    In: Physica A: Statistical Mechanics and its Applications.
    RePEc:eee:phsmap:v:551:y:2020:i:c:s037843712030025x.

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41. The predictive power of oil price shocks on realized volatility of oil: A note. (2020). Shahzad, Syed Jawad Hussain ; Pierdzioch, Christian ; GUPTA, RANGAN ; Demirer, Riza ; Hussain, Syed Jawad.
    In: Resources Policy.
    RePEc:eee:jrpoli:v:69:y:2020:i:c:s0301420720308874.

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42. Detection of volatility regime-switching for crude oil price modeling and forecasting. (2020). TAGHIZADEH-HESARY, Farhad ; Liu, Yue ; Zhang, Jijian ; Sun, Huaping.
    In: Resources Policy.
    RePEc:eee:jrpoli:v:69:y:2020:i:c:s0301420719306439.

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43. Forecasting stock price volatility: New evidence from the GARCH-MIDAS model. (2020). Wang, LU ; Yang, Lin ; Liu, Jing ; Ma, Feng.
    In: International Journal of Forecasting.
    RePEc:eee:intfor:v:36:y:2020:i:2:p:684-694.

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44. Brent crude oil prices volatility during major crises. (2020). Zavadska, Miroslava ; Morales, Lucia ; Coughlan, Joseph.
    In: Finance Research Letters.
    RePEc:eee:finlet:v:32:y:2020:i:c:s1544612318304380.

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45. A multi-granularity heterogeneous combination approach to crude oil price forecasting. (2020). Li, Xiang ; Hong, Tao ; Wang, Shouyang ; Zhou, Hao.
    In: Energy Economics.
    RePEc:eee:eneeco:v:91:y:2020:i:c:s0140988320301304.

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46. On realized volatility of crude oil futures markets: Forecasting with exogenous predictors under structural breaks. (2020). Zhang, Dayong ; Ji, Qiang ; Klein, Tony ; Todorova, Neda ; Luo, Jiawen.
    In: Energy Economics.
    RePEc:eee:eneeco:v:89:y:2020:i:c:s0140988320301213.

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47. Crude oil price analysis and forecasting: A perspective of “new triangle”. (2020). Li, Yuze ; Wang, Shouyang ; Lu, Quanying ; Chai, Jian.
    In: Energy Economics.
    RePEc:eee:eneeco:v:87:y:2020:i:c:s0140988320300608.

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48. Forecasting volatility in the petroleum futures markets: A re-examination and extension. (2020). Shaiban, Mohammed ; Hasanov, Akram ; Al-Freedi, Ajab.
    In: Energy Economics.
    RePEc:eee:eneeco:v:86:y:2020:i:c:s0140988319304232.

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49. The economic and financial properties of crude oil: A review. (2020). Lang, Korbinian ; Auer, Benjamin R.
    In: The North American Journal of Economics and Finance.
    RePEc:eee:ecofin:v:52:y:2020:i:c:s1062940818302559.

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50. Can We Forecast Daily Oil Futures Prices? Experimental Evidence from Convolutional Neural Networks. (2019). Tanaka, Katsuyuki ; Hamori, Shigeyuki ; Luo, Zhaojie ; Cai, Xiaojing ; Takiguchi, Tetsuya ; Kinkyo, Takuji.
    In: JRFM.
    RePEc:gam:jjrfmx:v:12:y:2019:i:1:p:9-:d:195801.

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51. Volatility forecasting of crude oil market: Can the regime switching GARCH model beat the single-regime GARCH models?. (2019). Zhang, Yue-Jun ; He, Ling-Yun ; Yao, Ting ; Ripple, Ronald.
    In: International Review of Economics & Finance.
    RePEc:eee:reveco:v:59:y:2019:i:c:p:302-317.

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52. How to effectively estimate the time-varying risk spillover between crude oil and stock markets? Evidence from the expectile perspective. (2019). Zhang, Yue-Jun ; Ma, Shu-Jiao.
    In: Energy Economics.
    RePEc:eee:eneeco:v:84:y:2019:i:c:s0140988319303573.

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53. Structural breaks and volatility forecasting in the copper futures market. (2018). Lin, Boqiang ; Gong, XU.
    In: Journal of Futures Markets.
    RePEc:wly:jfutmk:v:38:y:2018:i:3:p:290-339.

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54. The Lead–Lag Relationship between Oil Futures and Spot Prices—A Literature Review. (2018). Zavadska, Miroslava ; Morales, Lucia ; Coughlan, Joseph.
    In: IJFS.
    RePEc:gam:jijfss:v:6:y:2018:i:4:p:89-:d:179491.

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55. Persistence of Oil Prices in Gas Import Prices and the Resilience of the Oil-Indexation Mechanism. The Case of Spanish Gas Import Prices. (2018). Cansado-Bravo, Pablo ; Rodriguez-Monroy, Carlos.
    In: Energies.
    RePEc:gam:jeners:v:11:y:2018:i:12:p:3486-:d:190416.

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56. Forecasting Inflation Uncertainty in the G7 Countries. (2018). Wilfling, Bernd ; Bekiros, Stelios ; Segnon, Mawuli.
    In: Econometrics.
    RePEc:gam:jecnmx:v:6:y:2018:i:2:p:23-:d:143630.

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57. Analysis and Bayes statistical probability inference of crude oil price change point. (2018). Hu, YI ; Wang, Shouyang ; Lu, Quanying ; Lai, Kin Keung ; Chai, Jian ; Liu, Hongtao.
    In: Technological Forecasting and Social Change.
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58. Multifractal analysis of Bitcoin market. (2018). Maganini, Natalia Diniz ; de Almeida, Eduardo Fonseca ; da Silva, Antonio Carlos.
    In: Physica A: Statistical Mechanics and its Applications.
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59. Forecasting crude oil price volatility. (2018). Pastor, Daniel ; Herrera, Ana María ; Hu, Liang.
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    RePEc:eee:intfor:v:34:y:2018:i:4:p:622-635.

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60. The prediction of oil price turning points with log-periodic power law and multi-population genetic algorithm. (2018). Fan, Tijun ; Li, Shanling ; Cheng, Fangzheng.
    In: Energy Economics.
    RePEc:eee:eneeco:v:72:y:2018:i:c:p:341-355.

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61. Forecasting the WTI crude oil price by a hybrid-refined method. (2018). Zhang, Zhe George ; Zhou, Xiao-Yang ; Xing, Li-Min ; Li, Jie-Xun ; Chai, Jian.
    In: Energy Economics.
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62. Forecasting Inflation Uncertainty in the G7 Countries. (2018). Wilfling, Bernd ; Bekiros, Stelios ; Segnon, Mawuli.
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63. How to Measure Financial Market Efficiency? A Multifractality-Based Quantitative Approach with an Application to the European Carbon Market. (2018). Gronwald, Marc ; Sattarhoff, Cristina.
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    RePEc:ces:ceswps:_7102.

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64. Multifractal characteristics and return predictability in the Chinese stock markets. (2018). Zhou, Wei-Xing ; Jiang, Zhi-Qiang ; Shan, Zheng ; Gao, Xing-Lu ; Fu, Xin-Lan.
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65. Are Multifractal Processes Suited to Forecasting Electricity Price Volatility? Evidence from Australian Intraday Data. (2017). Wilfling, Bernd ; Lau, Chi Keung ; GUPTA, RANGAN ; Segnon, Mawuli.
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66. Forecasting crude-oil market volatility: Further evidence with jumps. (2017). Darné, Olivier ; Charles, Amelie ; Darne, Olivier.
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67. Forecasting crude-oil market volatility: Further evidence with jumps. (2017). Darné, Olivier ; Darne, Olivier ; Charles, Amelie.
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    RePEc:eee:eneeco:v:67:y:2017:i:c:p:508-519.

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68. A deep learning ensemble approach for crude oil price forecasting. (2017). Yu, Lean ; Li, jianping ; Zhao, Yang.
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    RePEc:eee:eneeco:v:66:y:2017:i:c:p:9-16.

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69. Oil price volatility and macroeconomic fundamentals: A regime switching GARCH-MIDAS model. (2017). Yin, Libo ; Wang, Yudong ; Pan, Zhiyuan ; Wu, Chongfeng.
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70. Are multifractal processes suited to forecasting electricity price volatility? Evidence from Australian intraday data. (2017). Wilfling, Bernd ; Lau, Chi Keung ; GUPTA, RANGAN ; Segnon, Mawuli.
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71. An ICA-based support vector regression scheme for forecasting crude oil prices. (2016). Li, Zimin ; Pan, Si Jia ; Fan, Liwei.
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    RePEc:eee:tefoso:v:112:y:2016:i:c:p:245-253.

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72. An event study analysis of oil and gas firm acreage and reserve acquisitions. (2016). Heaney, Richard ; Sabet, Amir H.
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    RePEc:eee:eneeco:v:57:y:2016:i:c:p:215-227.

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73. Forecasting crude oil price volatility and value-at-risk: Evidence from historical and recent data. (2016). GUPTA, RANGAN ; Lux, Thomas ; Segnon, Mawuli.
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