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A Machine Learning Approach to Volatility Forecasting

Citations

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As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Machine Learning for Realized Volatility Forecasting
    by Francis Diebold in No Hesitations on 2021-02-01 12:16:00

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
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Cited by:

  1. Zhou, Mingtao & Ma, Yong, 2025. "Climate risk and predictability of global stock market volatility," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 101(C).
  2. Minh Vo, 2025. "Measuring and Forecasting Stock Market Volatilities with High-Frequency Data," Computational Economics, Springer;Society for Computational Economics, vol. 65(6), pages 3503-3544, June.
  3. Katsafados, Apostolos G. & Leledakis, George N. & Panagiotou, Nikolaos P. & Pyrgiotakis, Emmanouil G., 2024. "Can central bankers’ talk predict bank stock returns? A machine learning approach," MPRA Paper 122899, University Library of Munich, Germany.
  4. Lihki Rubio & Adriana Palacio Pinedo & Adriana Mejía Castaño & Filipe Ramos, 2023. "Forecasting volatility by using wavelet transform, ARIMA and GARCH models," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 13(3), pages 803-830, December.
  5. Hu, Nan & Yin, Xuebao & Yao, Yuhang, 2025. "A novel HAR-type realized volatility forecasting model using graph neural network," International Review of Financial Analysis, Elsevier, vol. 98(C).
  6. Chun, Dohyun & Cho, Hoon & Ryu, Doojin, 2025. "Volatility forecasting and volatility-timing strategies: A machine learning approach," Research in International Business and Finance, Elsevier, vol. 75(C).
  7. M. Shabani & M. Magris & George Tzagkarakis & J. Kanniainen & A. Iosifidis, 2023. "Predicting the state of synchronization of financial time series using cross recurrence plots," Post-Print hal-04415269, HAL.
  8. Zhu, Haibin & Bai, Lu & He, Lidan & Liu, Zhi, 2023. "Forecasting realized volatility with machine learning: Panel data perspective," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 251-271.
  9. Reisenhofer, Rafael & Bayer, Xandro & Hautsch, Nikolaus, 2022. "HARNet: A convolutional neural network for realized volatility forecasting," CFS Working Paper Series 680, Center for Financial Studies (CFS).
  10. Chen, Wang & Chen, Zhu & Luo, Qin, 2025. "Predicting volatility in China's clean energy sector: Advantages of the carbon transition risk," Finance Research Letters, Elsevier, vol. 72(C).
  11. Chao Zhang & Yihuang Zhang & Mihai Cucuringu & Zhongmin Qian, 2022. "Volatility forecasting with machine learning and intraday commonality," Papers 2202.08962, arXiv.org, revised Feb 2023.
  12. Francesco Audrino & Jonathan Chassot, 2024. "HARd to Beat: The Overlooked Impact of Rolling Windows in the Era of Machine Learning," Papers 2406.08041, arXiv.org.
  13. Zhang, Hongwei & Zhao, Xinyi & Gao, Wang & Niu, Zibo, 2023. "The role of higher moments in predicting China's oil futures volatility: Evidence from machine learning models," Journal of Commodity Markets, Elsevier, vol. 32(C).
  14. Robert Stok & Paul Bilokon, 2023. "From Deep Filtering to Deep Econometrics," Papers 2311.06256, arXiv.org.
  15. Chen, Ying & Kimura, Yosuke & Inoue, Kotaro, 2025. "How does managerial perception of uncertainty affect corporate investment during the COVID-19 pandemic: A text mining approach," Pacific-Basin Finance Journal, Elsevier, vol. 90(C).
  16. Luo, Jiawen & Cepni, Oguzhan & Demirer, Riza & Gupta, Rangan, 2025. "Forecasting multivariate volatilities with exogenous predictors: An application to industry diversification strategies," Journal of Empirical Finance, Elsevier, vol. 81(C).
  17. Juan D. Díaz & Erwin Hansen & Gabriel Cabrera, 2025. "Forecasting the Volatility of US Oil and Gas Firms With Machine Learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(4), pages 1383-1402, July.
  18. Liao, Cunfei & Ma, Tian, 2024. "From fundamental signals to stock volatility: A machine learning approach," Pacific-Basin Finance Journal, Elsevier, vol. 84(C).
  19. Kaczmarek, Tomasz & Będowska-Sójka, Barbara & Grobelny, Przemysław & Perez, Katarzyna, 2022. "False Safe Haven Assets: Evidence From the Target Volatility Strategy Based on Recurrent Neural Network," Research in International Business and Finance, Elsevier, vol. 60(C).
  20. Guangying Liu & Ziyan Zhuang & Min Wang, 2024. "Forecasting the high‐frequency volatility based on the LSTM‐HIT model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1356-1373, August.
  21. Anubha Goel & Puneet Pasricha & Juho Kanniainen, 2024. "Time-Series Foundation AI Model for Value-at-Risk Forecasting," Papers 2410.11773, arXiv.org, revised May 2025.
  22. Díaz, Juan D. & Hansen, Erwin & Cabrera, Gabriel, 2024. "Machine-learning stock market volatility: Predictability, drivers, and economic value," International Review of Financial Analysis, Elsevier, vol. 94(C).
  23. Zeng, Qing & Lu, Xinjie & Xu, Jin & Lin, Yu, 2024. "Macro-Driven Stock Market Volatility Prediction: Insights from a New Hybrid Machine Learning Approach," International Review of Financial Analysis, Elsevier, vol. 96(PB).
  24. Martina Halouskov'a & v{S}tefan Ly'ocsa, 2025. "Forecasting U.S. equity market volatility with attention and sentiment to the economy," Papers 2503.19767, arXiv.org.
  25. Luo, Qin & Lu, Xinjie & Huang, Dengshi & Zeng, Qing, 2024. "The impact of carbon transition risk concerns on stock market cycles: Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 209(C).
  26. Timothé Gronier & William Maréchal & Christophe Geissler & Stéphane Gibout, 2022. "Usage of GAMS-Based Digital Twins and Clustering to Improve Energetic Systems Control," Energies, MDPI, vol. 16(1), pages 1-17, December.
  27. Lyócsa, Štefan & Todorova, Neda, 2024. "Forecasting of clean energy market volatility: The role of oil and the technology sector," Energy Economics, Elsevier, vol. 132(C).
  28. Lyócsa, Štefan & Todorova, Neda, 2024. "What drives the uranium sector risk? The role of attention, economic and geopolitical uncertainty," Energy Economics, Elsevier, vol. 140(C).
  29. Fang, Yan & Liu, Yinglin & Yang, Yi & Lucey, Brian & Abedin, Mohammad Zoynul, 2025. "How do Chinese urban investment bonds affect its economic resilience? Evidence from double machine learning," Research in International Business and Finance, Elsevier, vol. 74(C).
  30. Niu, Zibo & Demirer, Riza & Suleman, Muhammad Tahir & Zhang, Hongwei & Zhu, Xuehong, 2024. "Do industries predict stock market volatility? Evidence from machine learning models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 90(C).
  31. Borup, Daniel & Rapach, David E. & Schütte, Erik Christian Montes, 2023. "Mixed-frequency machine learning: Nowcasting and backcasting weekly initial claims with daily internet search volume data," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1122-1144.
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