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Forecast the realized range-based volatility: The role of investor sentiment and regime switching

Author

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  • Xu, Weiju
  • Wang, Jiqian
  • Ma, Feng
  • Lu, Xinjie

Abstract

In this study, we first investigate the impacts of investor sentiment on the realized range-based volatility in the framework of regime switching model. Out-of-sample results show that investor sentiment can significantly improve the forecasting performance of volatility models. Moreover, the results further find that combining the regime switching of those models with investor sentiment can substantially gain higher forecasting accuracy. Our conclusions are robust to different forecasting windows.

Suggested Citation

  • Xu, Weiju & Wang, Jiqian & Ma, Feng & Lu, Xinjie, 2019. "Forecast the realized range-based volatility: The role of investor sentiment and regime switching," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    • Handle: RePEc:eee:phsmap:v:527:y:2019:i:c:s037843711930826x
    DOI: 10.1016/j.physa.2019.121422
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    Citations

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    Cited by:

    1. Zhu, Sha & Liu, Qiuhong & Wang, Yan & Wei, Yu & Wei, Guiwu, 2019. "Which fear index matters for predicting US stock market volatilities: Text-counts or option based measurement?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    2. Jiqian Wang & Feng Ma & Chao Liang & Zhonglu Chen, 2022. "Volatility forecasting revisited using Markov‐switching with time‐varying probability transition," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 1387-1400, January.
    3. Zhao, Ruwei, 2020. "Quantifying the cross sectional relation of daily happiness sentiment and stock return: Evidence from US," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 538(C).
    4. Zheng, Biao & Zhang, Yuquan W. & Yin, Haitao & Geng, Yong, 2021. "The limited role of stock market in financing new energy development in China: An investigation using firms’ high-frequency data," Economic Analysis and Policy, Elsevier, vol. 72(C), pages 651-667.
    5. Li, Yue & W. Goodell, John & Shen, Dehua, 2021. "Does happiness forecast implied volatility? Evidence from nonparametric wave-based Granger causality testing," The Quarterly Review of Economics and Finance, Elsevier, vol. 81(C), pages 113-122.
    6. Hong, Yanran & Wang, Lu & Ye, Xiaoqing & Zhang, Yaojie, 2022. "Dynamic asymmetric impact of equity market uncertainty on energy markets: A time-varying causality analysis," Renewable Energy, Elsevier, vol. 196(C), pages 535-546.

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