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Implied volatility directional forecasting: a machine learning approach

Author

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  • Spyridon D. Vrontos
  • John Galakis
  • Ioannis D. Vrontos

Abstract

This study investigates whether the direction of U.S. implied volatility, VIX index, can be forecast. Multiple forecasts are generated based on standard econometric models, but, more importantly, on several machine learning techniques. Their statistical significance is assessed by a plethora of performance evaluation measures, while real-time investment strategies are devised to appraise the investment implications of the underlying modeling approaches. The main conclusion of the analysis is that the implementation of machine learning techniques in implied volatility forecasting can be more effective compared to mainstream econometric models and model selection techniques, as they are superior both in a statistical and an economic evaluation setting.

Suggested Citation

  • Spyridon D. Vrontos & John Galakis & Ioannis D. Vrontos, 2021. "Implied volatility directional forecasting: a machine learning approach," Quantitative Finance, Taylor & Francis Journals, vol. 21(10), pages 1687-1706, October.
    • Handle: RePEc:taf:quantf:v:21:y:2021:i:10:p:1687-1706
    DOI: 10.1080/14697688.2021.1905869
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    Cited by:

    1. Gongyue Jiang & Gaoxiu Qiao & Lu Wang & Feng Ma, 2024. "Hybrid forecasting of crude oil volatility index: The cross‐market effects of stock market jumps," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 2378-2398, September.
    2. Guo, Xiaozhu & Huang, Dengshi & Li, Xiafei & Liang, Chao, 2023. "Are categorical EPU indices predictable for carbon futures volatility? Evidence from the machine learning method," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 672-693.
    3. Qiu, Zhiguo & Lazar, Emese & Nakata, Keiichi, 2024. "VaR and ES forecasting via recurrent neural network-based stateful models," International Review of Financial Analysis, Elsevier, vol. 92(C).
    4. Gaoxiu Qiao & Wanmei Cui & Yijie Zhou & Chao Liang, 2025. "Volatility of Volatility and VIX Forecasting: New Evidence Based on Jumps, the Short‐Term and Long‐Term Volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 45(1), pages 23-46, January.
    5. Gunnarsson, Elias Søvik & Isern, Håkon Ramon & Kaloudis, Aristidis & Risstad, Morten & Vigdel, Benjamin & Westgaard, Sjur, 2024. "Prediction of realized volatility and implied volatility indices using AI and machine learning: A review," International Review of Financial Analysis, Elsevier, vol. 93(C).
    6. Noorshanaaz Khodabaccus & Aslam A. E. F. Saib, 2024. "volatilityforecastingpackage: A Financial Volatility Package in Mathematica," Computational Economics, Springer;Society for Computational Economics, vol. 63(6), pages 2307-2324, June.
    7. Liao, Cunfei & Ma, Tian, 2024. "From fundamental signals to stock volatility: A machine learning approach," Pacific-Basin Finance Journal, Elsevier, vol. 84(C).
    8. Qiao, Gaoxiu & Ma, Xuekun & Jiang, Gongyue & Wang, Lu, 2024. "Crude oil volatility index forecasting: New evidence based on positive and negative jumps from Chinese stock market," International Review of Economics & Finance, Elsevier, vol. 92(C), pages 415-437.
    9. Wen, Conghua & Zhai, Jia & Wang, Yinuo & Cao, Yi, 2024. "Implied volatility is (almost) past-dependent: Linear vs non-linear models," International Review of Financial Analysis, Elsevier, vol. 95(PB).
    10. 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).
    11. Luo, Qin & Bu, Jinfeng & Xu, Weiju & Huang, Dengshi, 2023. "Stock market volatility prediction: Evidence from a new bagging model," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 445-456.

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