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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:

Identifying Jumps in Financial Assets: A Comparison Between Nonparametric Jump Tests
Ana-Maria Dumitru, Giovanni Urga
Journal of Business and Economic Statistics (2012) Vol. 30, Iss. 2, pp. 242-255
Open Access | Times Cited: 139

Showing 1-25 of 139 citing articles:

Forecasting the volatility of crude oil futures using intraday data
Benoît Sévi
European Journal of Operational Research (2014) Vol. 235, Iss. 3, pp. 643-659
Open Access | Times Cited: 257
Forecasting the realized volatility of the oil futures market: A regime switching approach
Feng Ma, M.I.M. Wahab, Dengshi Huang, et al.
Energy Economics (2017) Vol. 67, pp. 136-145
Closed Access | Times Cited: 151
Jumps in High-Frequency Data: Spurious Detections, Dynamics, and News
Pierre Bajgrowicz, Olivier Scaillet, Adrien Treccani
Management Science (2015) Vol. 62, Iss. 8, pp. 2198-2217
Open Access | Times Cited: 128
Cojumps in stock prices: Empirical evidence
Dudley Gilder, Mark B. Shackleton, Stephen J. Taylor
Journal of Banking & Finance (2013) Vol. 40, pp. 443-459
Open Access | Times Cited: 89
Forecasting the oil futures price volatility: Large jumps and small jumps
Jing Liu, Feng Ma, Ke Yang, et al.
Energy Economics (2018) Vol. 72, pp. 321-330
Closed Access | Times Cited: 75
Systemic co-jumps
Massimiliano Caporin, Aleksey Kolokolov, Roberto Renò
Journal of Financial Economics (2017) Vol. 126, Iss. 3, pp. 563-591
Open Access | Times Cited: 74
Modelling systemic price cojumps with Hawkes factor models
Giacomo Bormetti, Lucio Maria Calcagnile, Michele Treccani, et al.
Quantitative Finance (2015) Vol. 15, Iss. 7, pp. 1137-1156
Open Access | Times Cited: 71
Jumps in Oil Prices: The Role of Economic News
John P. Elder, Hong Miao, Sanjay Ramchander
The Energy Journal (2013) Vol. 34, Iss. 3, pp. 217-237
Open Access | Times Cited: 59
Volatility Forecasting: Downside Risk, Jumps and Leverage Effect
Francesco Audrino, Yujia Hu
Econometrics (2016) Vol. 4, Iss. 1, pp. 8-8
Open Access | Times Cited: 57
Distributional predictability between commodity spot and futures: Evidence from nonparametric causality-in-quantiles tests
Sangram Keshari Jena, Aviral Kumar Tiwari, Shawkat Hammoudeh, et al.
Energy Economics (2018) Vol. 78, pp. 615-628
Closed Access | Times Cited: 53
Oil Price Uncertainty and Industrial Production
Karl Pinno, Apostolos Serletis
The Energy Journal (2013) Vol. 34, Iss. 3, pp. 191-216
Closed Access | Times Cited: 47
Forecasting the variance of stock index returns using jumps and cojumps
Adam Clements, Yin Liao
International Journal of Forecasting (2017) Vol. 33, Iss. 3, pp. 729-742
Open Access | Times Cited: 45
News sentiment and jumps in energy spot and futures markets
Svetlana Maslyuk, Kristian Rotaru, Alexander Dokumentov
Pacific-Basin Finance Journal (2016) Vol. 45, pp. 186-210
Closed Access | Times Cited: 39
Oil price volatility forecasts: What do investors need to know?
Stavros Degiannakis, George Filis
Journal of International Money and Finance (2021) Vol. 123, pp. 102594-102594
Open Access | Times Cited: 32
Good, bad cojumps and volatility forecasting: New evidence from crude oil and the U.S. stock markets
Yixiang Chen, Feng Ma, Yaojie Zhang
Energy Economics (2019) Vol. 81, pp. 52-62
Closed Access | Times Cited: 35
Collective synchronization and high frequency systemic instabilities in financial markets
Lucio Maria Calcagnile, Giacomo Bormetti, Michele Treccani, et al.
Quantitative Finance (2017) Vol. 18, Iss. 2, pp. 237-247
Open Access | Times Cited: 34
Inference on Self‐Exciting Jumps in Prices and Volatility Using High‐Frequency Measures
Worapree Maneesoonthorn, Catherine Forbes, Gael M. Martin
Journal of Applied Econometrics (2016) Vol. 32, Iss. 3, pp. 504-532
Open Access | Times Cited: 32
High-frequency jump tests: Which test should we use?
Worapree Maneesoonthorn, Gael M. Martin, Catherine Forbes
Journal of Econometrics (2020) Vol. 219, Iss. 2, pp. 478-487
Open Access | Times Cited: 28
Identifying the underlying components of high-frequency data: Pure vs jump diffusion processes
Rodrigo Hizmeri, Marwan Izzeldin, Giovanni Urga
Journal of Empirical Finance (2025), pp. 101594-101594
Closed Access
A new method for jump detection: analysis of jumps in the S&P 500 financial index
Khaldoun Khashanah, Jing Chen, Mike Buckle, et al.
Journal of the Royal Statistical Society Series C (Applied Statistics) (2025)
Open Access
Jump Detection Using Deep Learning: With Applications to Financial Time Series Data
Weizheng Chen, Zhang Guang
Computational Economics (2025)
Closed Access
S&P 500 Index‐Futures Price Jumps and Macroeconomic News
Hong Miao, Sanjay Ramchander, J. Kenton Zumwalt
Journal of Futures Markets (2013) Vol. 34, Iss. 10, pp. 980-1001
Open Access | Times Cited: 36
Volatility estimation and jump detection for drift–diffusion processes
Sébastien Laurent, Shuping Shi
Journal of Econometrics (2020) Vol. 217, Iss. 2, pp. 259-290
Open Access | Times Cited: 24
Jumps in the Chinese crude oil futures volatility forecasting: New evidence
Yangli Guo, Pan Li, Hanlin Wu
Energy Economics (2023) Vol. 126, pp. 106955-106955
Closed Access | Times Cited: 9
Jumps in High-Frequency Data: Spurious Detections, Dynamics, and News
Pierre Bajgrowicz, Olivier Scaillet, Adrien Treccani
SSRN Electronic Journal (2011)
Open Access | Times Cited: 29
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