IDEAS home Printed from https://ideas.repec.org/a/wly/jfutmk/v45y2025i11p2092-2108.html

A New Star Is Born: Does the VIX1D Render Common Volatility Forecasting Models for the US Equity Market Obsolete?

Author

Listed:
  • Stefan Albers

Abstract

We examine the characteristics of Cboe's 1‐day Volatility Index (VIX1D) and its predictive power regarding the next day's volatility of the S&P 500. Compared to the longer‐term volatility indices of the VIX family, it is generally lower and more volatile, exhibits a weaker negative correlation with the S&P 500, and has a distinct intraday pattern with a daily upward trend. We show that the VIX1D overestimates the volatility of the S&P 500 and propose an easy‐to‐implement proxy to adjust for the inherent risk premium. Our results indicate that the adjusted VIX1D provides model‐free, parsimonious, and easy‐to‐implement 1‐day volatility forecasts for the S&P 500 that are even more precise than those of the HAR and HAR‐VIX1D models. We conclude that the VIX1D seems to effectively capture the information embedded in the zero‐day‐to‐expiration (0DTE) options and is a promising indicator for pinpointed risk assessment of the US stock market.

Suggested Citation

  • Stefan Albers, 2025. "A New Star Is Born: Does the VIX1D Render Common Volatility Forecasting Models for the US Equity Market Obsolete?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 45(11), pages 2092-2108, November.
    • Handle: RePEc:wly:jfutmk:v:45:y:2025:i:11:p:2092-2108
    DOI: 10.1002/fut.70023
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/fut.70023
    Download Restriction: no
    File URL: https://libkey.io/10.1002/fut.70023?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Yiguo Sun & Ximing Wu, 2018. "Leverage and Volatility Feedback Effects and Conditional Dependence Index: A Nonparametric Study," JRFM, MDPI, vol. 11(2), pages 1-20, June.
    2. Buncic, Daniel & Gisler, Katja I.M., 2016. "Global equity market volatility spillovers: A broader role for the United States," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1317-1339.
    3. Chen, Jing & Han, Qian & Ryu, Doojin & Tang, Jing, 2022. "Does the world smile together? A network analysis of global index option implied volatilities," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 77(C).
    4. Xinglin Yang & Ji Chen, 2021. "VIX term structure: The role of jump propagation risks," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(6), pages 785-810, June.
    5. Liu, Lily Y. & Patton, Andrew J. & Sheppard, Kevin, 2015. "Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes," Journal of Econometrics, Elsevier, vol. 187(1), pages 293-311.
    6. Zhang, Junyu & Ruan, Xinfeng & Zhang, Jin E., 2023. "Do short-term market swings improve realized volatility forecasts?," Finance Research Letters, Elsevier, vol. 58(PD).
    7. Bekaert, Geert & Wu, Guojun, 2000. "Asymmetric Volatility and Risk in Equity Markets," The Review of Financial Studies, Society for Financial Studies, vol. 13(1), pages 1-42.
    8. Busch, Thomas & Christensen, Bent Jesper & Nielsen, Morten Ørregaard, 2011. "The role of implied volatility in forecasting future realized volatility and jumps in foreign exchange, stock, and bond markets," Journal of Econometrics, Elsevier, vol. 160(1), pages 48-57, January.
    9. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    10. Ole E. Barndorff‐Nielsen & Neil Shephard, 2002. "Econometric analysis of realized volatility and its use in estimating stochastic volatility models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 253-280, May.
    11. Ihsan U. Badshah, 2018. "Volatility Spillover from the Fear Index to Developed and Emerging Markets," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 54(1), pages 27-40, January.
    12. Paul Lynch & Gilles Zumbach, 2003. "Market heterogeneities and the causal structure of volatility," Quantitative Finance, Taylor & Francis Journals, vol. 3(4), pages 320-331.
    13. Charles J. Corrado & Thomas W. Miller, Jr., 2005. "The forecast quality of CBOE implied volatility indexes," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 25(4), pages 339-373, April.
    14. Albers, Stefan & Kestner, Lars N., 2024. "The daily rise and fall of the VIX1D: Causes and solutions of its overnight bias," Finance Research Letters, Elsevier, vol. 62(PA).
    15. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2007. "Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 701-720, November.
    16. Megaritis, Anastasios & Vlastakis, Nikolaos & Triantafyllou, Athanasios, 2021. "Stock market volatility and jumps in times of uncertainty," Journal of International Money and Finance, Elsevier, vol. 113(C).
    17. Kang, Sang Hoon & Arreola Hernandez, Jose & Rehman, Mobeen Ur & Shahzad, Syed Jawad Hussain & Yoon, Seong-Min, 2023. "Spillovers and hedging between US equity sectors and gold, oil, islamic stocks and implied volatilities," Resources Policy, Elsevier, vol. 81(C).
    18. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March.
    19. Xue Gong & Weiguo Zhang & Weijun Xu & Zhe Li, 2022. "Uncertainty index and stock volatility prediction: evidence from international markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-44, December.
    20. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    21. Peter Reinhard Hansen & Asger Lunde, 2005. "A Realized Variance for the Whole Day Based on Intermittent High-Frequency Data," Journal of Financial Econometrics, Oxford University Press, vol. 3(4), pages 525-554.
    22. 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.
    23. Chao Liang & Yu Wei & Yaojie Zhang, 2020. "Is implied volatility more informative for forecasting realized volatility: An international perspective," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1253-1276, December.
    24. Johnson, Travis L., 2017. "Risk Premia and the VIX Term Structure," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(6), pages 2461-2490, December.
    25. Dimos S. Kambouroudis & David G. McMillan & Katerina Tsakou, 2021. "Forecasting realized volatility: The role of implied volatility, leverage effect, overnight returns, and volatility of realized volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(10), pages 1618-1639, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Carlo Confalonieri & Paola De Vincentiis, 2026. "Forecasting the worst: is implied volatility forward-looking enough?," Journal of Banking Regulation, Palgrave Macmillan, vol. 27(1), pages 1-20, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Santos, Douglas G. & Candido, Osvaldo & Tófoli, Paula V., 2022. "Forecasting risk measures using intraday and overnight information," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).
    2. Chao Liang & Yan Li & Feng Ma & Yaojie Zhang, 2022. "Forecasting international equity market volatility: A new approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1433-1457, November.
    3. Yusui Tang & Feng Ma & Yaojie Zhang & Yu Wei, 2022. "Forecasting the oil price realized volatility: A multivariate heterogeneous autoregressive model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4770-4783, October.
    4. Li, Zhao-Chen & Xie, Chi & Wang, Gang-Jin & Zhu, You & Zeng, Zhi-Jian & Gong, Jue, 2024. "Forecasting global stock market volatilities: A shrinkage heterogeneous autoregressive (HAR) model with a large cross-market predictor set," International Review of Economics & Finance, Elsevier, vol. 93(PB), pages 673-711.
    5. Dudley Gilder & Leonidas Tsiaras, 2020. "Volatility forecasts embedded in the prices of crude‐oil options," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(7), pages 1127-1159, July.
    6. Zhiyuan Pan & Jun Zhang & Yudong Wang & Juan Huang, 2024. "Modeling and forecasting stock return volatility using the HARGARCH model with VIX information," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(8), pages 1383-1403, August.
    7. Byounghyun Jeon & Sung Won Seo & Jun Sik Kim, 2020. "Uncertainty and the volatility forecasting power of option‐implied volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(7), pages 1109-1126, July.
    8. Tomáš Plíhal, 2021. "Scheduled macroeconomic news announcements and Forex volatility forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1379-1397, December.
    9. Tim Bollerslev & Benjamin Hood & John Huss & Lasse Heje Pedersen, 2018. "Risk Everywhere: Modeling and Managing Volatility," The Review of Financial Studies, Society for Financial Studies, vol. 31(7), pages 2729-2773.
    10. Danyan Wen & Mengxi He & Yaojie Zhang & Yudong Wang, 2022. "Forecasting realized volatility of Chinese stock market: A simple but efficient truncated approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 230-251, March.
    11. Bu, Ruijun & Hizmeri, Rodrigo & Izzeldin, Marwan & Murphy, Anthony & Tsionas, Mike, 2023. "The contribution of jump signs and activity to forecasting stock price volatility," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 144-164.
    12. Andersen, Torben G. & Bollerslev, Tim & Huang, Xin, 2011. "A reduced form framework for modeling volatility of speculative prices based on realized variation measures," Journal of Econometrics, Elsevier, vol. 160(1), pages 176-189, January.
    13. Wei Zhang & Kai Yan & Dehua Shen, 2021. "Can the Baidu Index predict realized volatility in the Chinese stock market?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-31, December.
    14. Dimos Kambouroudis & David McMillan & Katerina Tsakou, 2019. "Forecasting Realized Volatility: The role of implied volatility, leverage effect, overnight returns and volatility of realized volatility," Working Papers 2019-03, Swansea University, School of Management.
    15. 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).
    16. Loïc Maréchal, 2021. "Do economic variables forecast commodity futures volatility?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(11), pages 1735-1774, November.
    17. 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).
    18. Denis Haoheng Wu & Sherry Zhefang Zhou, 2026. "Component‐Driven FX Volatility Prediction: Evidence From USDCNH via GARCH‐MIDAS Models Exploiting Leading Indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 45(1), pages 194-216, January.
    19. Francesco Audrino & Yujia Hu, 2016. "Volatility Forecasting: Downside Risk, Jumps and Leverage Effect," Econometrics, MDPI, vol. 4(1), pages 1-24, February.
    20. Zhang, Yaojie & He, Mengxi & Wang, Yudong & Wen, Danyan, 2025. "Model specification for volatility forecasting benchmark," International Review of Financial Analysis, Elsevier, vol. 97(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:jfutmk:v:45:y:2025:i:11:p:2092-2108. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/0270-7314/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.