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The Information Content of Intraday Implied Volatility for Volatility Forecasting

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  • Yaw‐Huei Wang
  • Yun‐Yi Wang

Abstract

This study examines the intraday S&P 500 implied volatility index (VIX) to determine when the index contains the most information for volatility forecasting. The findings indicate that, in general, VIX levels around noon are most informative for predicting realized volatility. We posit that the VIX performs better during this time period because trading motivation around noon is less complex, and therefore trades contain more information on the market expectation of future volatility. Further investigation on the 2008 financial crisis period suggests that market participants become more cautious, and thus the forecasting performance is sustained until the market's close. Copyright © 2015 John Wiley & Sons, Ltd.

Suggested Citation

  • Yaw‐Huei Wang & Yun‐Yi Wang, 2016. "The Information Content of Intraday Implied Volatility for Volatility Forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(2), pages 167-178, March.
    • Handle: RePEc:wly:jforec:v:35:y:2016:i:2:p:167-178
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    Cited by:

    1. 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).
    2. Yen-Ju Hsu & Yang-Cheng Lu & J. Jimmy Yang, 2021. "News sentiment and stock market volatility," Review of Quantitative Finance and Accounting, Springer, vol. 57(3), pages 1093-1122, October.
    3. Chun, Dohyun & Cho, Hoon & Ryu, Doojin, 2019. "Forecasting the KOSPI200 spot volatility using various volatility measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 156-166.
    4. Bastien Baldacci, 2020. "High-frequency dynamics of the implied volatility surface," Papers 2012.10875, arXiv.org.
    5. Chun, Dohyun & Cho, Hoon & Ryu, Doojin, 2023. "Discovering the drivers of stock market volatility in a data-rich world," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 82(C).
    6. Li, Xiafei & Liang, Chao & Chen, Zhonglu & Umar, Muhammad, 2022. "Forecasting crude oil volatility with uncertainty indicators: New evidence," Energy Economics, Elsevier, vol. 108(C).
    7. Ye, Wuyi & Xia, Wenjing & Wu, Bin & Chen, Pengzhan, 2022. "Using implied volatility jumps for realized volatility forecasting: Evidence from the Chinese market," International Review of Financial Analysis, Elsevier, vol. 83(C).
    8. Min Liu & Wei‐Chong Choo & Chi‐Chuan Lee & Chien‐Chiang Lee, 2023. "Trading volume and realized volatility forecasting: Evidence from the China stock market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 76-100, January.
    9. Tissaoui, Kais, 2019. "Forecasting implied volatility risk indexes: International evidence using Hammerstein-ARX approach," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 232-249.
    10. Tissaoui, Kais & Zaghdoudi, Taha, 2021. "Dynamic connectedness between the U.S. financial market and Euro-Asian financial markets: Testing transmission of uncertainty through spatial regressions models," The Quarterly Review of Economics and Finance, Elsevier, vol. 81(C), pages 481-492.
    11. Adam Clements & Yin Liao & Yusui Tang, 2022. "Moving beyond Volatility Index (VIX): HARnessing the term structure of implied volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 86-99, January.
    12. Plíhal, Tomáš & Lyócsa, Štefan, 2021. "Modeling realized volatility of the EUR/USD exchange rate: Does implied volatility really matter?," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 811-829.

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