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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Out-of-sample prediction of the oil futures market volatility: A comparison of new and traditional combination approaches
Yaojie Zhang, Feng Ma, Yu Wei
Energy Economics (2019) Vol. 81, pp. 1109-1120
Closed Access | Times Cited: 48

Showing 1-25 of 48 citing articles:

Geopolitical risk uncertainty and oil future volatility: Evidence from MIDAS models
Dexiang Mei, Feng Ma, Yin Liao, et al.
Energy Economics (2019) Vol. 86, pp. 104624-104624
Closed Access | Times Cited: 195
Grid search with a weighted error function: Hyper-parameter optimization for financial time series forecasting
Yuan Zhao, Weiguo Zhang, Xiufeng Liu
Applied Soft Computing (2024) Vol. 154, pp. 111362-111362
Open Access | Times Cited: 32
Forecasting crude oil volatility with uncertainty indicators: New evidence
Xiafei Li, Chao Liang, Zhonglu Chen, et al.
Energy Economics (2022) Vol. 108, pp. 105936-105936
Closed Access | Times Cited: 56
Forecasting crude oil futures price using machine learning methods: Evidence from China
Lili Guo, Xinya Huang, Yanjiao Li, et al.
Energy Economics (2023) Vol. 127, pp. 107089-107089
Closed Access | Times Cited: 27
Natural gas volatility prediction: Fresh evidence from extreme weather and extended GARCH-MIDAS-ES model
Chao Liang, Zhenglan Xia, Xiaodong Lai, et al.
Energy Economics (2022) Vol. 116, pp. 106437-106437
Closed Access | Times Cited: 37
A novel hybrid approach to forecast crude oil futures using intraday data
Jeevananthan Manickavasagam, S. Visalakshmi, Nicholas Apergis
Technological Forecasting and Social Change (2020) Vol. 158, pp. 120126-120126
Open Access | Times Cited: 45
Forecasting the Chinese stock market volatility with international market volatilities: The role of regime switching
Yaojie Zhang, Likun Lei, Yu Wei
The North American Journal of Economics and Finance (2020) Vol. 52, pp. 101145-101145
Closed Access | Times Cited: 41
Efficient predictability of oil price: The role of VIX-based panic index shadow line difference
Zhifeng Dai, Xiaotong Zhang, Chao Liang
Energy Economics (2023) Vol. 129, pp. 107234-107234
Closed Access | Times Cited: 16
Investors’ perspective on forecasting crude oil return volatility: Where do we stand today?
Li Liu, Qianjie Geng, Yaojie Zhang, et al.
Journal of Management Science and Engineering (2021) Vol. 7, Iss. 3, pp. 423-438
Open Access | Times Cited: 32
Medium-term and long-term volatility forecasts for EUA futures with country-specific economic policy uncertainty indices
Lixia Zhang, Qin Luo, Xiaozhu Guo, et al.
Resources Policy (2022) Vol. 77, pp. 102644-102644
Closed Access | Times Cited: 22
Forecasting crude oil prices with shrinkage methods: Can nonconvex penalty and Huber loss help?
Limin Xing, Yue‐Jun Zhang
Energy Economics (2022) Vol. 110, pp. 106014-106014
Closed Access | Times Cited: 21
Forecasting gold volatility with geopolitical risk indices
Xiafei Li, Qiang Guo, Chao Liang, et al.
Research in International Business and Finance (2022) Vol. 64, pp. 101857-101857
Closed Access | Times Cited: 21
Forecasting volatility of EUA futures: New evidence
Xiaozhu Guo, Yisu Huang, Chao Liang, et al.
Energy Economics (2022) Vol. 110, pp. 106021-106021
Closed Access | Times Cited: 20
Predicting the volatility of China's new energy stock market: Deep insight from the realized EGARCH-MIDAS model
Lu Wang, Chenchen Zhao, Chao Liang, et al.
Finance research letters (2022) Vol. 48, pp. 102981-102981
Closed Access | Times Cited: 19
Forecasting China's crude oil futures volatility: How to dig out the information of other energy futures volatilities?
Daxiang Jin, Mengxi He, Lü Xing, et al.
Resources Policy (2022) Vol. 78, pp. 102852-102852
Closed Access | Times Cited: 19
Combining Realized Volatility Estimators Based on Economic Performance
Vasiliki D. Skintzi, Stavroula P. Fameliti
(2025)
Closed Access
Bioenergy Market Predictions using AI: Integrating Climate Change and Green Finance
Lili Guo, Quanfeixue Cheng, Xiangyi He, et al.
Renewable Energy (2025), pp. 123328-123328
Closed Access
A multiscale time-series decomposition learning for crude oil price forecasting
Jinghua Tan, Z Li, Chuanhui Zhang, et al.
Energy Economics (2024) Vol. 136, pp. 107733-107733
Closed Access | Times Cited: 3
Forecasting regular and extreme gold price volatility: The roles of asymmetry, extreme event, and jump
Xiafei Li, Dongxin Li, Xuhui Zhang, et al.
Journal of Forecasting (2021) Vol. 40, Iss. 8, pp. 1501-1523
Closed Access | Times Cited: 19
Discovering the drivers of stock market volatility in a data-rich world
Dohyun Chun, Hoon Cho, Doojin Ryu
Journal of International Financial Markets Institutions and Money (2022) Vol. 82, pp. 101684-101684
Closed Access | Times Cited: 14
Forecasting China's Crude Oil Futures Volatility: The Role of the Jump, Jumps Intensity, and Leverage Effect
Jiqian Wang, Feng Ma, M.I.M. Wahab, et al.
Journal of Forecasting (2020) Vol. 40, Iss. 5, pp. 921-941
Closed Access | Times Cited: 19
Neural Network-Based Predictive Models for Stock Market Index Forecasting
Karime Chahuán-Jiménez
Journal of risk and financial management (2024) Vol. 17, Iss. 6, pp. 242-242
Open Access | Times Cited: 2
Volatility forecasting of clean energy ETF using GARCH-MIDAS with neural network model
Li Zhang, Lu Wang, Thong Trung Nguyen, et al.
Finance research letters (2024) Vol. 70, pp. 106286-106286
Closed Access | Times Cited: 2
Forecasting green bond volatility via novel heterogeneous ensemble approaches
Yufei Xia, Hanfei Ren, Yinguo Li, et al.
Expert Systems with Applications (2022) Vol. 204, pp. 117580-117580
Closed Access | Times Cited: 10
Exploiting the heteroskedasticity in measurement error to improve volatility predictions in oil and biofuel feedstock markets
Emawtee Bissoondoyal‐Bheenick, Robert Brooks, Hung Xuan, et al.
Energy Economics (2020) Vol. 86, pp. 104689-104689
Closed Access | Times Cited: 16
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