IDEAS home Printed from https://ideas.repec.org/r/arx/papers/2601.13014.html

A machine learning approach to volatility forecasting

Citations

Blog mentions

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.
as


Cited by:

  1. is not listed on IDEAS
  2. 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).
  3. Fu, Tong & Huang, Dasen & Feng, Lingbing & Tang, Xiaoping, 2024. "More is better? The impact of predictor choice on the INE oil futures volatility forecasting," Energy Economics, Elsevier, vol. 134(C).
  4. Ali Rayeni & Hosein Naderi, 2025. "Predicting the Canadian Yield Curve Using Machine Learning Techniques," IJFS, MDPI, vol. 13(3), pages 1-30, September.
  5. 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.
  6. 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.
  7. Rafael Reisenhofer & Xandro Bayer & Nikolaus Hautsch, 2022. "HARNet: A Convolutional Neural Network for Realized Volatility Forecasting," Papers 2205.07719, arXiv.org.
  8. 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).
  9. Zhu, Ziyang & Zheng, Yuhao & Wang, Xinyi & Huang, Dasen & Feng, Lingbing, 2025. "Forecasting China's precious metal futures volatility: GBRT models and time-model dimension combination of Tree SHAP," International Review of Financial Analysis, Elsevier, vol. 104(PA).
  10. Brini, Alessio & Toscano, Giacomo, 2025. "SpotV2Net: Multivariate intraday spot volatility forecasting via vol-of-vol-informed graph attention networks," International Journal of Forecasting, Elsevier, vol. 41(3), pages 1093-1111.
  11. Burak Korkusuz & Mehmet Sahiner, 2025. "Coin impact on cross-crypto realized volatility and dynamic cryptocurrency volatility connectedness," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-32, December.
  12. 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).
  13. 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).
  14. Rehim Kılıç, 2025. "Linear and nonlinear econometric models against machine learning models: realized volatility prediction," Finance and Economics Discussion Series 2025-061, Board of Governors of the Federal Reserve System (U.S.).
  15. 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).
  16. 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).
  17. 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).
  18. Robert Stok & Paul Bilokon, 2023. "From Deep Filtering to Deep Econometrics," Papers 2311.06256, arXiv.org.
  19. 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).
  20. 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).
  21. Yaxuan Kong & Yoontae Hwang & Marcus Kaiser & Chris Vryonides & Roel Oomen & Stefan Zohren, 2025. "Fusing Narrative Semantics for Financial Volatility Forecasting," Papers 2510.20699, arXiv.org.
  22. 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.
  23. Liao, Cunfei & Ma, Tian, 2024. "From fundamental signals to stock volatility: A machine learning approach," Pacific-Basin Finance Journal, Elsevier, vol. 84(C).
  24. Gong, Jue & Wang, Gang-Jin & Zhou, Yang & Xie, Chi, 2025. "Cross-market volatility forecasting with attention-based spatial–temporal graph convolutional networks," Journal of Empirical Finance, Elsevier, vol. 83(C).
  25. 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).
  26. Dániel Léber & Balázs Egyed, 2026. "The Sentiment Augmented GARCH-LSTM Hybrid Model for Value-at-Risk Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 67(1), pages 313-353, January.
  27. 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.
  28. 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.
  29. 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.
  30. Alessio Brini & David A. Hsieh & Patrick Kuiper & Sean Moushegian & David Ye, 2025. "Empirical Models of the Time Evolution of SPX Option Prices," Papers 2506.17511, arXiv.org.
  31. Natalia Roszyk & Robert 'Slepaczuk, 2024. "The Hybrid Forecast of S&P 500 Volatility ensembled from VIX, GARCH and LSTM models," Papers 2407.16780, arXiv.org.
  32. 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).
  33. 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.
  34. Tenghan Zhong, 2026. "Risk-Sensitive Specialist Routing for Volatility Forecasting," Papers 2604.10402, arXiv.org, revised Apr 2026.
  35. Radmir Mishelevich Leushuis & Nicolai Petkov, 2026. "Advances in forecasting realized volatility: a review of methodologies," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 12(1), pages 1-29, December.
  36. Uluc Aysun & Melanie Guldi, 2026. "Revisiting exchange rate predictability: Can machine learning with theoretical filtering outperform canonical models?," Working Papers 2026-01, University of Central Florida, Department of Economics.
  37. Shafqat Iqbal & Štefan Lyócsa, 2026. "A Fuzzy Framework for Realized Volatility Prediction: Empirical Evidence From Equity Markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 45(3), pages 1261-1291, April.
  38. 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).
  39. 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.
  40. 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).
  41. 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.
  42. Yang ZHANG & Ziang QIU Ziang & Donghyun PARK & Shu TIAN, 2026. "Role of Artificial Intelligence in Finance: Selective Literature Review and Implications for Asia's Financial Stability," Working Papers wp61, South East Asian Central Banks (SEACEN) Research and Training Centre, revised Feb 2026.
  43. 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).
  44. 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.
  45. 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.
  46. 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.
  47. 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).
  48. 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.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.