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OpenAlex Citation Counts

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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:

Forecasting the prices of crude oil: An iterated combination approach
Yaojie Zhang, Feng Ma, Shi Ben-shan, et al.
Energy Economics (2018) Vol. 70, pp. 472-483
Closed Access | Times Cited: 140

Showing 1-25 of 140 citing articles:

Climate policy uncertainty and world renewable energy index volatility forecasting
Chao Liang, Muhammad Umar, Feng Ma, et al.
Technological Forecasting and Social Change (2022) Vol. 182, pp. 121810-121810
Open Access | Times Cited: 256
Forecasting crude oil prices with a large set of predictors: Can LASSO select powerful predictors?
Yaojie Zhang, Feng Ma, Yudong Wang
Journal of Empirical Finance (2019) Vol. 54, pp. 97-117
Closed Access | Times Cited: 244
Geopolitical risk and oil volatility: A new insight
Jing Liu, Feng Ma, Yingkai Tang, et al.
Energy Economics (2019) Vol. 84, pp. 104548-104548
Closed Access | Times Cited: 234
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: 193
Forecasting oil price volatility: Forecast combination versus shrinkage method
Yaojie Zhang, Yu Wei, Yi Zhang, et al.
Energy Economics (2019) Vol. 80, pp. 423-433
Closed Access | Times Cited: 157
Geopolitical risk trends and crude oil price predictability
Zhikai Zhang, Mengxi He, Yaojie Zhang, et al.
Energy (2022) Vol. 258, pp. 124824-124824
Closed Access | Times Cited: 96
Uncertainty and crude oil market volatility: new evidence
Chao Liang, Yu Wei, Xiafei Li, et al.
Applied Economics (2019) Vol. 52, Iss. 27, pp. 2945-2959
Closed Access | Times Cited: 137
Economic policy uncertainty and the Chinese stock market volatility: Novel evidence
Tao Li, Feng Ma, Xuehua Zhang, et al.
Economic Modelling (2019) Vol. 87, pp. 24-33
Closed Access | Times Cited: 135
Is implied volatility more informative for forecasting realized volatility: An international perspective
Chao Liang, Yu Wei, Yaojie Zhang
Journal of Forecasting (2020) Vol. 39, Iss. 8, pp. 1253-1276
Closed Access | Times Cited: 111
Which popular predictor is more useful to forecast international stock markets during the coronavirus pandemic: VIX vs EPU?
Jiqian Wang, Xinjie Lu, Feng He, et al.
International Review of Financial Analysis (2020) Vol. 72, pp. 101596-101596
Open Access | Times Cited: 110
A hybrid model based on selective ensemble for energy consumption forecasting in China
Jin Xiao, Yuxi Li, Ling Xie, et al.
Energy (2018) Vol. 159, pp. 534-546
Closed Access | Times Cited: 108
A novel hybrid model for forecasting crude oil price based on time series decomposition
Hooman Abdollahi
Applied Energy (2020) Vol. 267, pp. 115035-115035
Closed Access | Times Cited: 102
Forecasting global equity market volatilities
Yaojie Zhang, Feng Ma, Yin Liao
International Journal of Forecasting (2020) Vol. 36, Iss. 4, pp. 1454-1475
Closed Access | Times Cited: 96
Forecasting crude oil prices: A scaled PCA approach
Mengxi He, Yaojie Zhang, Danyan Wen, et al.
Energy Economics (2021) Vol. 97, pp. 105189-105189
Closed Access | Times Cited: 94
Monthly crude oil spot price forecasting using variational mode decomposition
Jinchao Li, Shaowen Zhu, Qianqian Wu
Energy Economics (2019) Vol. 83, pp. 240-253
Closed Access | Times Cited: 92
Modes decomposition method in fusion with robust random vector functional link network for crude oil price forecasting
Ranjeeta Bisoi, P.K. Dash, Sthita Prajna Mishra
Applied Soft Computing (2019) Vol. 80, pp. 475-493
Closed Access | Times Cited: 90
Forecasting the aggregate oil price volatility in a data-rich environment
Feng Ma, Jing Liu, M.I.M. Wahab, et al.
Economic Modelling (2018) Vol. 72, pp. 320-332
Closed Access | Times Cited: 88
The information content of uncertainty indices for natural gas futures volatility forecasting
Chao Liang, Feng Ma, Lu Wang, et al.
Journal of Forecasting (2021) Vol. 40, Iss. 7, pp. 1310-1324
Closed Access | Times Cited: 85
Examining the predictive information of CBOE OVX on China’s oil futures volatility: Evidence from MS-MIDAS models
Xinjie Lu, Feng Ma, Jiqian Wang, et al.
Energy (2020) Vol. 212, pp. 118743-118743
Open Access | Times Cited: 73
Forecasting the volatility of EUA futures with economic policy uncertainty using the GARCH-MIDAS model
Jian Liu, Ziting Zhang, Lizhao Yan, et al.
Financial Innovation (2021) Vol. 7, Iss. 1
Open Access | Times Cited: 72
Dynamic risk spillover among crude oil, economic policy uncertainty and Chinese financial sectors
Zhifeng Dai, Haoyang Zhu
International Review of Economics & Finance (2022) Vol. 83, pp. 421-450
Closed Access | Times Cited: 64
Forecasting crude oil futures market returns: A principal component analysis combination approach
Yaojie Zhang, Yudong Wang
International Journal of Forecasting (2022) Vol. 39, Iss. 2, pp. 659-673
Closed Access | Times Cited: 53
Global financial cycle and the predictability of oil market volatility: Evidence from a GARCH-MIDAS model
Afees A. Salisu, Rangan Gupta, Rıza Demirer
Energy Economics (2022) Vol. 108, pp. 105934-105934
Open Access | Times Cited: 42
Forecasting renewable energy stock volatility using short and long-term Markov switching GARCH-MIDAS models: Either, neither or both?
Lu Wang, Jiangbin Wu, Yang Cao, et al.
Energy Economics (2022) Vol. 111, pp. 106056-106056
Closed Access | Times Cited: 38
Forecasting stock market volatility with various geopolitical risks categories: New evidence from machine learning models
Zibo Niu, Chenlu Wang, Hongwei Zhang
International Review of Financial Analysis (2023) Vol. 89, pp. 102738-102738
Closed Access | Times Cited: 32
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