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Which is the better forecasting model? A comparison between HAR-RV and multifractality volatility

Citations

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Cited by:

  1. Zargar, Faisal Nazir & Kumar, Dilip, 2020. "Modeling unbiased extreme value volatility estimator in presence of heterogeneity and jumps: A study with economic significance analysis," International Review of Economics & Finance, Elsevier, vol. 67(C), pages 25-41.
  2. Duan, Yinying & Chen, Wang & Zeng, Qing & Liu, Zhicao, 2018. "Leverage effect, economic policy uncertainty and realized volatility with regime switching," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 493(C), pages 148-154.
  3. Wang, Yajing & Liang, Fang & Wang, Tianyi & Huang, Zhuo, 2020. "Does measurement error matter in volatility forecasting? Empirical evidence from the Chinese stock market," Economic Modelling, Elsevier, vol. 87(C), pages 148-157.
  4. Li, Xiafei & Liao, Yin & Lu, Xinjie & Ma, Feng, 2022. "An oil futures volatility forecast perspective on the selection of high-frequency jump tests," Energy Economics, Elsevier, vol. 116(C).
  5. Wu, Hanlin & Li, Pan & Cao, Jiawei & Xu, Zijian, 2024. "Forecasting the Chinese crude oil futures volatility using jump intensity and Markov-regime switching model," Energy Economics, Elsevier, vol. 134(C).
  6. Sapkota, Niranjan, 2022. "News-based sentiment and bitcoin volatility," International Review of Financial Analysis, Elsevier, vol. 82(C).
  7. Lv, Wendai, 2018. "Does the OVX matter for volatility forecasting? Evidence from the crude oil market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 916-922.
  8. Mei, Dexiang & Liu, Jing & Ma, Feng & Chen, Wang, 2017. "Forecasting stock market volatility: Do realized skewness and kurtosis help?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 481(C), pages 153-159.
  9. Gong, Xu & Lin, Boqiang, 2019. "Modeling stock market volatility using new HAR-type models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 194-211.
  10. Lin, XuXun & Yuan, PengCheng, 2018. "A dynamic parking charge optimal control model under perspective of commuters’ evolutionary game behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1096-1110.
  11. Liu, Zhichao & Ma, Feng & Long, Yujia, 2015. "High and low or close to close prices? Evidence from the multifractal volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 50-61.
  12. Liu, Zhicao & Ye, Yong & Ma, Feng & Liu, Jing, 2017. "Can economic policy uncertainty help to forecast the volatility: A multifractal perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 181-188.
  13. Gong, Xu & Lin, Boqiang, 2018. "Structural changes and out-of-sample prediction of realized range-based variance in the stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 27-39.
  14. Xie, Nan & Wang, Zongrun & Chen, Sicen & Gong, Xu, 2019. "Forecasting downside risk in China’s stock market based on high-frequency data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 530-541.
  15. Yuan, Ying & Zhang, Tonghui, 2020. "Forecasting stock market in high and low volatility periods: a modified multifractal volatility approach," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
  16. Anupam Dutta & Debojyoti Das, 2022. "Forecasting realized volatility: New evidence from time‐varying jumps in VIX," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(12), pages 2165-2189, December.
  17. da Silva Filho, Antônio Carlos & Maganini, Natália Diniz & de Almeida, Eduardo Fonseca, 2018. "Multifractal analysis of Bitcoin market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 954-967.
  18. Caporin, Massimiliano & Rossi, Eduardo & Santucci de Magistris, Paolo, 2017. "Chasing volatility," Journal of Econometrics, Elsevier, vol. 198(1), pages 122-145.
  19. Lu, Xinjie & Ma, Feng & Wang, Jianqiong & Dong, Dayong, 2022. "Singlehanded or joint race? Stock market volatility prediction," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 734-754.
  20. Jiang, Wei & Ruan, Qingsong & Li, Jianfeng & Li, Ye, 2018. "Modeling returns volatility: Realized GARCH incorporating realized risk measure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 500(C), pages 249-258.
  21. Lahmiri, Salim, 2017. "Modeling and predicting historical volatility in exchange rate markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 387-395.
  22. Zargar, Faisal Nazir & Kumar, Dilip, 2020. "Heterogeneous market hypothesis approach for modeling unbiased extreme value volatility estimator in presence of leverage effect: An individual stock level study with economic significance analysis," The Quarterly Review of Economics and Finance, Elsevier, vol. 77(C), pages 271-285.
  23. Guo, Yangli & Li, Pan & Wu, Hanlin, 2023. "Jumps in the Chinese crude oil futures volatility forecasting: New evidence," Energy Economics, Elsevier, vol. 126(C).
  24. Zhuang, Chunjuan, 2018. "Improving performance of exchange rate momentum strategy using volatility information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 741-753.
  25. Dutta, Anupam & Uddin, Gazi Salah & Sheng, Lin Wen & Park, Donghyun & Zhu, Xuening, 2024. "Volatility dynamics of agricultural futures markets under uncertainties," Energy Economics, Elsevier, vol. 136(C).
  26. Li, Jiang-Cheng & Xu, Ming-Zhe & Han, Xu & Tao, Chen, 2022. "Dynamic risk resonance between crude oil and stock market by econophysics and machine learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
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