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Stock market volatility forecasting: Do we need high-frequency data?

Citations

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Cited by:

  1. Dean Fantazzini, 2024. "Adaptive Conformal Inference for Computing Market Risk Measures: An Analysis with Four Thousand Crypto-Assets," JRFM, MDPI, vol. 17(6), pages 1-44, June.
  2. Hasanov, Akram Shavkatovich & Burkhanov, Aktam Usmanovich & Usmonov, Bunyod & Khajimuratov, Nizomjon Shukurullaevich & Khurramova, Madina Mansur qizi, 2024. "The role of sudden variance shifts in predicting volatility in bioenergy crop markets under structural breaks," Energy, Elsevier, vol. 293(C).
  3. Yan, Xiang & Bai, Jiancheng & Li, Xiafei & Chen, Zhonglu, 2022. "Can dimensional reduction technology make better use of the information of uncertainty indices when predicting volatility of Chinese crude oil futures?," Resources Policy, Elsevier, vol. 75(C).
  4. Haukvik, Nicole & Cheraghali, Hamid & Molnár, Peter, 2024. "The role of investors’ fear in crude oil volatility forecasting," Research in International Business and Finance, Elsevier, vol. 70(PB).
  5. Bauwens, Luc & Xu, Yongdeng, 2025. "The contribution of realized variance–covariance models to the economic value of volatility timing," International Journal of Forecasting, Elsevier, vol. 41(3), pages 1165-1183.
  6. 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.
  7. Bauwens, Luc & Xu, Yongdeng, 2023. "The contribution of realized covariance models to the economic value of volatility timing," Cardiff Economics Working Papers E2023/20, Cardiff University, Cardiff Business School, Economics Section.
  8. 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).
  9. 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.
  10. Sercan Demiralay & Hatice Gaye Gencer & Alexander Brauneis, 2025. "Stock–Commodity Correlations, Optimal Hedging, and Climate Risks," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 45(10), pages 1693-1716, October.
  11. Hassanniakalager, Arman & Baker, Paul L. & Platanakis, Emmanouil, 2024. "A False Discovery Rate approach to optimal volatility forecasting model selection," International Journal of Forecasting, Elsevier, vol. 40(3), pages 881-902.
  12. Lyu, Yongjian & Yang, Zhidan & Luo, Ya & Qin, Zhilong & Yi, Heling & Ke, Rui, 2025. "Forecasting the volatility of crude oil futures market: Does the simple 5-minute RV hold up?," Energy Economics, Elsevier, vol. 146(C).
  13. Mohammad Al-Shboul & Aktham Maghyereh, 2023. "Did real economic uncertainty drive risk connectedness in the oil–stock nexus during the COVID-19 outbreak? A partial wavelet coherence analysis," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 12(1), pages 1-23, December.
  14. Vasiliki Skintzi & Stavroula P. Fameliti, 2025. "Combining realized volatility estimators based on economic performance," Journal of Asset Management, Palgrave Macmillan, vol. 26(7), pages 819-846, December.
  15. Guan, Zhigui & Zhao, Yuanjun, 2024. "Optimizing stock market volatility predictions based on the SMVF-ANP approach," International Review of Economics & Finance, Elsevier, vol. 95(C).
  16. Wang, Lu & Wang, Xing & Liang, Chao, 2024. "Natural gas volatility prediction via a novel combination of GARCH-MIDAS and one-class SVM," The Quarterly Review of Economics and Finance, Elsevier, vol. 98(C).
  17. Lan, Qiujun & Li, Haojie & Mi, Xianhua & Zhang, Chunyu, 2025. "Optimizing investment strategies: Harnessing the power of K-line complex networks," International Review of Economics & Finance, Elsevier, vol. 99(C).
  18. Fiszeder, Piotr & Fałdziński, Marcin & Molnár, Peter, 2023. "Modeling and forecasting dynamic conditional correlations with opening, high, low, and closing prices," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 308-321.
  19. Virbickaitė, Audronė & Lopes, Hedibert F. & Zaharieva, Martina Danielova, 2025. "Multivariate dynamic mixed-frequency density pooling for financial forecasting," International Journal of Forecasting, Elsevier, vol. 41(3), pages 1184-1198.
  20. Ma, Feng & Wang, Jiqian & Wahab, M.I.M. & Ma, Yuanhui, 2023. "Stock market volatility predictability in a data-rich world: A new insight," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1804-1819.
  21. 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).
  22. Li, Zhao-Chen & Xie, Chi & Zeng, Zhi-Jian & Wang, Gang-Jin & Zhang, Ting, 2023. "Forecasting global stock market volatilities in an uncertain world," International Review of Financial Analysis, Elsevier, vol. 85(C).
  23. Lyócsa, Štefan & Tabaček, Jakub, 2026. "Attention to renewable energy: A risk-factor for stocks in the renewable energy sector," Research in International Business and Finance, Elsevier, vol. 81(C).
  24. Li, Jian, 2025. "Predicting China's transportation sector volatility: Evidence from a new economic indicator," Finance Research Letters, Elsevier, vol. 84(C).
  25. Yu, Wei & Zheng, Ying & Jia, Jianjun, 2025. "Political uncertainty and stock performance: Evidence from sessions of the Chinese Provincial People’s Congress," Research in International Business and Finance, Elsevier, vol. 75(C).
  26. Horváth, Roman & Kalistová, Anna & Lyócsa, Štefan & Miškufová, Marta & Moravcová, Michala, 2025. "Do hurricanes cause storm on the stock market? The case of US energy companies," International Review of Financial Analysis, Elsevier, vol. 97(C).
  27. Xingyu Dai & Roy Cerqueti & Qunwei Wang & Ling Xiao, 2025. "Volatility forecasting: a new GARCH-type model for fuzzy sets-valued time series," Annals of Operations Research, Springer, vol. 348(1), pages 735-775, May.
  28. Fantazzini, Dean, 2023. "Assessing the Credit Risk of Crypto-Assets Using Daily Range Volatility Models," MPRA Paper 117141, University Library of Munich, Germany.
  29. 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.
  30. He, Mengxi & Wen, Danyan & Xing, Lu & Zhang, Yaojie, 2024. "Industry volatility concentration and the predictability of aggregate stock market volatility," International Review of Economics & Finance, Elsevier, vol. 95(C).
  31. Guo, Yangli & Li, Pan & Wu, Hanlin, 2023. "Jumps in the Chinese crude oil futures volatility forecasting: New evidence," Energy Economics, Elsevier, vol. 126(C).
  32. Werner Kristjanpoller, 2024. "A hybrid econometrics and machine learning based modeling of realized volatility of natural gas," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-32, December.
  33. Zhang, Qun & Zhang, Zhendong & Luo, Jiawen, 2024. "Asymmetric and high-order risk transmission across VIX and Chinese futures markets," International Review of Financial Analysis, Elsevier, vol. 93(C).
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