IDEAS home Printed from https://ideas.repec.org/a/gam/jrisks/v13y2025i5p98-d1660024.html
   My bibliography  Save this article

Historical Perspectives in Volatility Forecasting Methods with Machine Learning

Author

Listed:
  • Zhiang Qiu

    (Seaver College, Pepperdine University, Malibu, CA 90263, USA)

  • Clemens Kownatzki

    (Pepperdine Graziadio Business School, Pepperdine University, Malibu, CA 90263, USA)

  • Fabien Scalzo

    (Seaver College, Pepperdine University, Malibu, CA 90263, USA)

  • Eun Sang Cha

    (Seaver College, Pepperdine University, Malibu, CA 90263, USA
    Institute of Advanced Machinery, Design & Technology, Korea University, Seoul 136-713, Republic of Korea)

Abstract

Volatility forecasting for financial institutions plays a pivotal role across a wide range of domains, such as risk management, option pricing, and market making. For instance, banks can incorporate volatility forecasts into stress testing frameworks to ensure they are holding sufficient capital during extreme market conditions. However, volatility forecasting is challenging because volatility can only be estimated, and different factors influence volatility, ranging from macroeconomic indicators to investor sentiments. While recent works show promising advances in machine learning and artificial intelligence for volatility forecasting, a comprehensive assessment of current statistical and learning-based methods is lacking. Thus, this paper aims to provide a comprehensive survey of the historical evolution of volatility forecasting with a comparative benchmark of key landmark models, such as implied volatility, GARCH, LSTM, and Transformer. We open-source our benchmark code to further research in learning-based methods for volatility forecasting.

Suggested Citation

  • Zhiang Qiu & Clemens Kownatzki & Fabien Scalzo & Eun Sang Cha, 2025. "Historical Perspectives in Volatility Forecasting Methods with Machine Learning," Risks, MDPI, vol. 13(5), pages 1-24, May.
    • Handle: RePEc:gam:jrisks:v:13:y:2025:i:5:p:98-:d:1660024
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-9091/13/5/98/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-9091/13/5/98/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pengfei Zhao & Haoren Zhu & Wilfred Siu Hung NG & Dik Lun Lee, 2024. "From GARCH to Neural Network for Volatility Forecast," Papers 2402.06642, arXiv.org.
    2. F R Johnston & J E Boyland & M Meadows & E Shale, 1999. "Some properties of a simple moving average when applied to forecasting a time series," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(12), pages 1267-1271, December.
    3. Engle, Robert F & Ng, Victor K, 1993. "Measuring and Testing the Impact of News on Volatility," Journal of Finance, American Finance Association, vol. 48(5), pages 1749-1778, December.
    4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    5. Taylor, James W., 2004. "Volatility forecasting with smooth transition exponential smoothing," International Journal of Forecasting, Elsevier, vol. 20(2), pages 273-286.
    6. James W. Taylor, 2004. "Smooth transition exponential smoothing," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 385-404.
    7. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
    8. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
    9. C. W. J. Granger & Roselyne Joyeux, 1980. "An Introduction To Long‐Memory Time Series Models And Fractional Differencing," Journal of Time Series Analysis, Wiley Blackwell, vol. 1(1), pages 15-29, January.
    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. Roberto Ferulano, 2009. "A Mixed Historical Formula to forecast volatility," Journal of Asset Management, Palgrave Macmillan, vol. 10(2), pages 124-136, June.
    2. Debabrata Mukhopadhyay & Nityananda Sarkar, 2013. "Stock Returns Under Alternative Volatility and Distributional Assumptions: The Case for India," International Econometric Review (IER), Econometric Research Association, vol. 5(1), pages 1-19, April.
    3. Prateek Sharma & Vipul _, 2015. "Forecasting stock index volatility with GARCH models: international evidence," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 32(4), pages 445-463, October.
    4. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    5. George-Jason Siouris & Alex Karagrigoriou, 2017. "A Low Price Correction for Improved Volatility Estimation and Forecasting," Risks, MDPI, vol. 5(3), pages 1-14, August.
    6. Stentoft, Lars, 2005. "Pricing American options when the underlying asset follows GARCH processes," Journal of Empirical Finance, Elsevier, vol. 12(4), pages 576-611, September.
    7. Charles, Amélie, 2010. "The day-of-the-week effects on the volatility: The role of the asymmetry," European Journal of Operational Research, Elsevier, vol. 202(1), pages 143-152, April.
    8. Catania, Leopoldo & Proietti, Tommaso, 2020. "Forecasting volatility with time-varying leverage and volatility of volatility effects," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1301-1317.
    9. Chiang, Min-Hsien & Huang, Hsin-Yi, 2011. "Stock market momentum, business conditions, and GARCH option pricing models," Journal of Empirical Finance, Elsevier, vol. 18(3), pages 488-505, June.
    10. Nazarian, Rafik & Gandali Alikhani, Nadiya & Naderi, Esmaeil & Amiri, Ashkan, 2013. "Forecasting Stock Market Volatility: A Forecast Combination Approach," MPRA Paper 46786, University Library of Munich, Germany.
    11. Kanungo, Rama Prasad, 2021. "Uncertainty of M&As under asymmetric estimation," Journal of Business Research, Elsevier, vol. 122(C), pages 774-793.
    12. Zhao, Yixiu & Upreti, Vineet & Cai, Yuzhi, 2021. "Stock returns, quantile autocorrelation, and volatility forecasting," International Review of Financial Analysis, Elsevier, vol. 73(C).
    13. Daglis, Theodoros & Konstantakis, Konstantinos N. & Michaelides, Panayotis G. & Papadakis, Theodoulos Eleftherios, 2020. "The forecasting ability of solar and space weather data on NASDAQ’s finance sector price index volatility," Research in International Business and Finance, Elsevier, vol. 52(C).
    14. Hiremath, Gourishankar S & Bandi, Kamaiah, 2010. "Long Memory in Stock Market Volatility:Evidence from India," MPRA Paper 48519, University Library of Munich, Germany.
    15. Muhammad Sheraz & Imran Nasir, 2021. "Information-Theoretic Measures and Modeling Stock Market Volatility: A Comparative Approach," Risks, MDPI, vol. 9(5), pages 1-20, May.
    16. Malay Bhattacharyya & Dileep Kumar M & Ramesh Kumar, 2009. "Optimal sampling frequency for volatility forecast models for the Indian stock markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(1), pages 38-54.
    17. Subrata Roy, 2020. "Stock Market Asymmetry and Investors’ Sensation on Prime Minister: Indian Evidence," Jindal Journal of Business Research, , vol. 9(2), pages 148-161, December.
    18. Subrata ROY, 2021. "Volatility Forecasting, Market Efficiency and Effect of Recession of SRI Indices," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(2(627), S), pages 259-284, Summer.
    19. Liu, Min & Taylor, James W. & Choo, Wei-Chong, 2020. "Further empirical evidence on the forecasting of volatility with smooth transition exponential smoothing," Economic Modelling, Elsevier, vol. 93(C), pages 651-659.
    20. Trapero, Juan R., 2016. "Calculation of solar irradiation prediction intervals combining volatility and kernel density estimates," Energy, Elsevier, vol. 114(C), pages 266-274.

    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:gam:jrisks:v:13:y:2025:i:5:p:98-:d:1660024. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.