IDEAS home Printed from https://ideas.repec.org/a/wly/jforec/v44y2025i4p1602-1618.html
   My bibliography  Save this article

Trading VIX on Volatility Forecasts: Another Volatility Puzzle?

Author

Listed:
  • Stavros Degiannakis
  • Panagiotis Delis
  • George Filis
  • George Giannopoulos

Abstract

This study evaluates the economic usefulness of stock market implied volatility forecasts, based on their ability to improve the short‐run trading decision‐making process. The current literature aligns the forecast horizon with the frequency of the trading decision in order to evaluate different forecasting frameworks. By contrast, the premise of our paper is that these should not be necessarily related, but rather the evaluation should be based on the actual needs of the end‐user. Thus, we evaluate whether the multiple days ahead stock market volatility forecasts vis‐à‐vis the 1‐day ahead forecasts can improve the 1‐day ahead trading profits from VIX and the S&P500 futures. Our results suggest that indeed the 1‐day ahead trading profits are significantly improved when the trading decisions are based on longer term volatility forecasts. More specifically, the highest trading gains are obtained when using the 22‐day ahead forecasts. The results hold true for both VIX and S&P500 futures day‐ahead trading. Although there is no theoretical background regarding the fact that forecasting and trading horizons should not be aligned, we strongly motivate this potential issue, both from the statistical and financial points of view.

Suggested Citation

  • Stavros Degiannakis & Panagiotis Delis & George Filis & George Giannopoulos, 2025. "Trading VIX on Volatility Forecasts: Another Volatility Puzzle?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(4), pages 1602-1618, July.
  • Handle: RePEc:wly:jforec:v:44:y:2025:i:4:p:1602-1618
    DOI: 10.1002/for.3257
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/for.3257
    Download Restriction: no

    File URL: https://libkey.io/10.1002/for.3257?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. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    2. I‐Ming Jiang & Jui‐Cheng Hung & Chuan‐San Wang, 2014. "Volatility Forecasts: Do Volatility Estimators and Evaluation Methods Matter?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 34(11), pages 1077-1094, November.
    3. Xue Gong & Weiguo Zhang & Yuan Zhao & Xin Ye, 2023. "Forecasting stock volatility with a large set of predictors: A new forecast combination method," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1622-1647, November.
    4. Hansen, Lars Peter & Hodrick, Robert J, 1980. "Forward Exchange Rates as Optimal Predictors of Future Spot Rates: An Econometric Analysis," Journal of Political Economy, University of Chicago Press, vol. 88(5), pages 829-853, October.
    5. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    6. Chou, Ray Yeutien & Liu, Nathan, 2010. "The economic value of volatility timing using a range-based volatility model," Journal of Economic Dynamics and Control, Elsevier, vol. 34(11), pages 2288-2301, November.
    7. Andrew J. Patton & Kevin Sheppard, 2015. "Good Volatility, Bad Volatility: Signed Jumps and The Persistence of Volatility," The Review of Economics and Statistics, MIT Press, vol. 97(3), pages 683-697, July.
    8. 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.
    9. 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).
    10. Kim, Chang-Jin & Morley, James C & Nelson, Charles R, 2004. "Is There a Positive Relationship between Stock Market Volatility and the Equity Premium?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 36(3), pages 339-360, June.
    11. Degiannakis, Stavros & Filis, George, 2017. "Forecasting oil price realized volatility using information channels from other asset classes," Journal of International Money and Finance, Elsevier, vol. 76(C), pages 28-49.
    12. Charlotte Christiansen & Maik Schmeling & Andreas Schrimpf, 2012. "A comprehensive look at financial volatility prediction by economic variables," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(6), pages 956-977, September.
    13. Bernales, Alejandro & Guidolin, Massimo, 2014. "Can we forecast the implied volatility surface dynamics of equity options? Predictability and economic value tests," Journal of Banking & Finance, Elsevier, vol. 46(C), pages 326-342.
    14. Bumho Son & Yunyoung Lee & Seongwan Park & Jaewook Lee, 2023. "Forecasting global stock market volatility: The impact of volatility spillover index in spatial‐temporal graph‐based model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1539-1559, November.
    15. Panagiotis Delis & Stavros Degiannakis & Konstantinos Giannopoulos, 2023. "What Should be Taken into Consideration when Forecasting Oil Implied Volatility Index?," The Energy Journal, , vol. 44(5), pages 231-250, September.
    16. 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.
    17. Afees A. Salisu & Riza Demirer & Rangan Gupta, 2023. "Policy uncertainty and stock market volatility revisited: The predictive role of signal quality," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2307-2321, December.
    18. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    19. 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.
    20. Branco, Rafael R. & Rubesam, Alexandre & Zevallos, Mauricio, 2024. "Forecasting realized volatility: Does anything beat linear models?," Journal of Empirical Finance, Elsevier, vol. 78(C).
    21. Panagiotis Delis, Stavros Degiannakis, and Konstantinos Giannopoulos, 2023. "What Should be Taken into Consideration when Forecasting Oil Implied Volatility Index?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 5).
    22. Chkili, Walid & Hammoudeh, Shawkat & Nguyen, Duc Khuong, 2014. "Volatility forecasting and risk management for commodity markets in the presence of asymmetry and long memory," Energy Economics, Elsevier, vol. 41(C), pages 1-18.
    23. Elder, Adam & Gannon, Gerard, 1998. "Evaluation of volatility forecasts in an economic value framework," International Review of Financial Analysis, Elsevier, vol. 7(3), pages 221-236.
    24. Becker, R. & Clements, A.E. & Doolan, M.B. & Hurn, A.S., 2015. "Selecting volatility forecasting models for portfolio allocation purposes," International Journal of Forecasting, Elsevier, vol. 31(3), pages 849-861.
    25. Nicholas Taylor, 2014. "The Economic Value of Volatility Forecasts: A Conditional Approach," Journal of Financial Econometrics, Oxford University Press, vol. 12(3), pages 433-478.
    26. Richardson, Matthew & Stock, James H., 1989. "Drawing inferences from statistics based on multiyear asset returns," Journal of Financial Economics, Elsevier, vol. 25(2), pages 323-348, December.
    27. Panagiotis Delis & Stavros Degiannakis & George Filis, 2022. "What matters when developing oil price volatility forecasting frameworks?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 361-382, March.
    28. Bollerslev, Tim & Patton, Andrew J. & Quaedvlieg, Rogier, 2016. "Exploiting the errors: A simple approach for improved volatility forecasting," Journal of Econometrics, Elsevier, vol. 192(1), pages 1-18.
    29. Liang, Chao & Tang, Linchun & Li, Yan & Wei, Yu, 2020. "Which sentiment index is more informative to forecast stock market volatility? Evidence from China," International Review of Financial Analysis, Elsevier, vol. 71(C).
    30. Degiannakis, Stavros & Filis, George, 2022. "Oil price volatility forecasts: What do investors need to know?," Journal of International Money and Finance, Elsevier, vol. 123(C).
    31. Qiao, Gaoxiu & Jiang, Gongyue & Yang, Jiyu, 2022. "VIX term structure forecasting: New evidence based on the realized semi-variances," International Review of Financial Analysis, Elsevier, vol. 82(C).
    32. 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.
    33. 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)

    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. Degiannakis, Stavros & Filis, George, 2022. "Oil price volatility forecasts: What do investors need to know?," Journal of International Money and Finance, Elsevier, vol. 123(C).
    2. Zhang, Yaojie & Wahab, M.I.M. & Wang, Yudong, 2023. "Forecasting crude oil market volatility using variable selection and common factor," International Journal of Forecasting, Elsevier, vol. 39(1), pages 486-502.
    3. He, Mengxi & Wang, Yudong & Zeng, Qing & Zhang, Yaojie, 2023. "Forecasting aggregate stock market volatility with industry volatilities: The role of spillover index," Research in International Business and Finance, Elsevier, vol. 65(C).
    4. Mengxi He & Xianfeng Hao & Yaojie Zhang & Fanyi Meng, 2021. "Forecasting stock return volatility using a robust regression model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1463-1478, December.
    5. Liu, Guangqiang & Guo, Xiaozhu, 2022. "Forecasting stock market volatility using commodity futures volatility information," Resources Policy, Elsevier, vol. 75(C).
    6. He, Mengxi & Wen, Danyan & Xing, Lu & Zhang, Yaojie, 2024. "Industry volatility concentration and the predictability of aggregate stock market volatility," International Review of Economics & Finance, Elsevier, vol. 95(C).
    7. 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.
    8. Yuqing Feng & Yaojie Zhang, 2025. "Forecasting Realized Volatility: The Choice of Window Size," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(2), pages 692-705, March.
    9. Kim Christensen & Mathias Siggaard & Bezirgen Veliyev, 2023. "A Machine Learning Approach to Volatility Forecasting," Journal of Financial Econometrics, Oxford University Press, vol. 21(5), pages 1680-1727.
    10. Niu, Zibo & Demirer, Riza & Suleman, Muhammad Tahir & Zhang, Hongwei & Zhu, Xuehong, 2024. "Do industries predict stock market volatility? Evidence from machine learning models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 90(C).
    11. Li, Xiaodan & Gong, Xue & Ge, Futing & Huang, Jingjing, 2024. "Forecasting stock volatility using pseudo-out-of-sample information," International Review of Economics & Finance, Elsevier, vol. 90(C), pages 123-135.
    12. Zhang, Hongwei & Zhao, Xinyi & Gao, Wang & Niu, Zibo, 2023. "The role of higher moments in predicting China's oil futures volatility: Evidence from machine learning models," Journal of Commodity Markets, Elsevier, vol. 32(C).
    13. Zhang, Yaojie & Lei, Likun & Wei, Yu, 2020. "Forecasting the Chinese stock market volatility with international market volatilities: The role of regime switching," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    14. Yaojie Zhang & Yudong Wang & Feng Ma, 2021. "Forecasting US stock market volatility: How to use international volatility information," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 733-768, August.
    15. 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.
    16. Guo, Yangli & He, Feng & Liang, Chao & Ma, Feng, 2022. "Oil price volatility predictability: New evidence from a scaled PCA approach," Energy Economics, Elsevier, vol. 105(C).
    17. Gong, Xue & Ye, Xin & Zhang, Weiguo & Zhang, Yue, 2023. "Predicting energy futures high-frequency volatility using technical indicators: The role of interaction," Energy Economics, Elsevier, vol. 119(C).
    18. Zhang, Yaojie & He, Mengxi & Wang, Yudong & Wen, Danyan, 2025. "Model specification for volatility forecasting benchmark," International Review of Financial Analysis, Elsevier, vol. 97(C).
    19. 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.
    20. Zhu, Haibin & Bai, Lu & He, Lidan & Liu, Zhi, 2023. "Forecasting realized volatility with machine learning: Panel data perspective," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 251-271.

    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:jforec:v:44:y:2025:i:4:p:1602-1618. 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://www3.interscience.wiley.com/cgi-bin/jhome/2966 .

    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.