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A Practical Guide to Harnessing the HAR Volatility Model

Citations

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Cited by:

  1. Díaz-Mendoza, Ana-Carmen & Pardo, Angel, 2020. "Holidays, weekends and range-based volatility," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
  2. Gu, Qinen & Li, Shaofang & Tian, Sihua & Wang, Yuyouting, 2023. "Climate, geopolitical, and energy market risk interconnectedness: Evidence from a new climate risk index," Finance Research Letters, Elsevier, vol. 58(PB).
  3. Chao Zhang & Xingyue Pu & Mihai Cucuringu & Xiaowen Dong, 2023. "Graph Neural Networks for Forecasting Multivariate Realized Volatility with Spillover Effects," Papers 2308.01419, arXiv.org.
  4. Zhang, Yaojie & He, Mengxi & Wang, Yudong & Wen, Danyan, 2025. "Model specification for volatility forecasting benchmark," International Review of Financial Analysis, Elsevier, vol. 97(C).
  5. 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).
  6. 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.
  7. Qianjie Geng & Xianfeng Hao & Yudong Wang, 2024. "Forecasting the volatility of crude oil futures: A time‐dependent weighted least squares with regularization constraint," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 309-325, March.
  8. 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.
  9. Li, Dan & Drovandi, Christopher & Clements, Adam, 2024. "Outlier-robust methods for forecasting realized covariance matrices," International Journal of Forecasting, Elsevier, vol. 40(1), pages 392-408.
  10. Zhang, Chao & Pu, Xingyue & Cucuringu, Mihai & Dong, Xiaowen, 2025. "Forecasting realized volatility with spillover effects: Perspectives from graph neural networks," International Journal of Forecasting, Elsevier, vol. 41(1), pages 377-397.
  11. Maksim Teterin & Anatoly Peresetsky, 2025. "Can Ethereum predict Bitcoin’s volatility?," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 77, pages 74-90.
  12. 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).
  13. Zhikai Zhang & Yaojie Zhang & Yudong Wang & Qunwei Wang, 2024. "The predictability of carbon futures volatility: New evidence from the spillovers of fossil energy futures returns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(4), pages 557-584, April.
  14. Lyócsa, Štefan & Plíhal, Tomáš & Výrost, Tomáš, 2021. "FX market volatility modelling: Can we use low-frequency data?," Finance Research Letters, Elsevier, vol. 40(C).
  15. Bjoern Schulte-Tillmann & Mawuli Segnon & Timo Wiedemann, 2023. "A comparison of high-frequency realized variance measures: Duration- vs. return-based approaches," CQE Working Papers 10523, Center for Quantitative Economics (CQE), University of Muenster.
  16. 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).
  17. Zheqi Fan & Meng Melody Wang & Yifan Ye, 2026. "On options-driven realized volatility forecasting: Information gains via rough volatility model," Papers 2604.02743, arXiv.org, revised Apr 2026.
  18. 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).
  19. Lyócsa, Štefan & Todorova, Neda & Výrost, Tomáš, 2021. "Predicting risk in energy markets: Low-frequency data still matter," Applied Energy, Elsevier, vol. 282(PA).
  20. Todorova, Neda, 2025. "Asymmetric return–volatility relationship of uranium investments," Finance Research Letters, Elsevier, vol. 85(PB).
  21. Buccheri, Giuseppe & Renò, Roberto & Vocalelli, Giorgio, 2025. "Taking advantage of biased proxies for forecast evaluation," Journal of Econometrics, Elsevier, vol. 251(C).
  22. 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.
  23. Rangika Peiris & Minh-Ngoc Tran & Chao Wang & Richard Gerlach, 2024. "Loss-based Bayesian Sequential Prediction of Value at Risk with a Long-Memory and Non-linear Realized Volatility Model," Papers 2408.13588, arXiv.org.
  24. Li, Yan & Huynh, Luu Duc Toan & Xu, Yongan & Liang, Hao, 2023. "The forecast ability of a belief-based momentum indicator in full-day, daytime, and nighttime volatilities of Chinese oil futures," Energy Economics, Elsevier, vol. 127(PB).
  25. Asgharian, Hossein & Christiansen, Charlotte & Hou, Ai Jun, 2023. "The effect of uncertainty on stock market volatility and correlation," Journal of Banking & Finance, Elsevier, vol. 154(C).
  26. 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.
  27. 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.
  28. Wen, Conghua & Zhai, Jia & Wang, Yinuo & Cao, Yi, 2024. "Implied volatility is (almost) past-dependent: Linear vs non-linear models," International Review of Financial Analysis, Elsevier, vol. 95(PB).
  29. Teterin, M. & Peresetsky, A., 2024. "Google Trends and Bitcoin volatility forecast," Journal of the New Economic Association, New Economic Association, vol. 65(4), pages 118-135.
  30. Awartani, Basel & Maghyereh, Aktham, 2025. "The value of cross market volatility in improving the forecast accuracy of risk in the gold, the dollar and the oil futures markets," Finance Research Letters, Elsevier, vol. 83(C).
  31. Lyócsa, Štefan & Molnár, Peter & Výrost, Tomáš, 2021. "Stock market volatility forecasting: Do we need high-frequency data?," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1092-1110.
  32. Liang, Chao & Huynh, Luu Duc Toan & Li, Yan, 2023. "Market momentum amplifies market volatility risk: Evidence from China’s equity market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 88(C).
  33. Howard Caulfield & James P. Gleeson, 2024. "Systematic comparison of deep generative models applied to multivariate financial time series," Papers 2412.06417, arXiv.org.
  34. Qiao, Gaoxiu & Wang, Yunrun & Liu, Wenwen, 2025. "Prediction of Chinese stock volatility: Harnessing higher-order moments information of stock and futures markets," Research in International Business and Finance, Elsevier, vol. 76(C).
  35. 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).
  36. Dumitru, Ana Maria H. & Hizmeri, Rodrigo & Izzeldin, Marwan, 2025. "Forecasting the realized variance in the presence of intraday periodicity," Journal of Banking & Finance, Elsevier, vol. 170(C).
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