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Forecasting of Realised Volatility with the Random Forests Algorithm

Author

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  • Chuong Luong

    (School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, GPO Box U1987, Perth 6845, Western Australia, Australia)

  • Nikolai Dokuchaev

    (School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, GPO Box U1987, Perth 6845, Western Australia, Australia)

Abstract

The paper addresses the forecasting of realised volatility for financial time series using the heterogeneous autoregressive model (HAR) and machine learning techniques. We consider an extended version of the existing HAR model with included purified implied volatility. For this extended model, we apply the random forests algorithm for the forecasting of the direction and the magnitude of the realised volatility. In experiments with historical high frequency data, we demonstrate improvements of forecast accuracy for the proposed model.

Suggested Citation

  • Chuong Luong & Nikolai Dokuchaev, 2018. "Forecasting of Realised Volatility with the Random Forests Algorithm," JRFM, MDPI, vol. 11(4), pages 1-15, October.
    • Handle: RePEc:gam:jjrfmx:v:11:y:2018:i:4:p:61-:d:175017
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Yicun Li & Yuanyang Teng, 2023. "The Fama–French Five-Factor Model with Hurst Exponents Compared with Machine Learning Methods," Mathematics, MDPI, vol. 11(13), pages 1-19, July.
    3. Frank, Johannes, 2023. "Forecasting realized volatility in turbulent times using temporal fusion transformers," FAU Discussion Papers in Economics 03/2023, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    4. 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.
    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. Tiago E. Pratas & Filipe R. Ramos & Lihki Rubio, 2023. "Forecasting bitcoin volatility: exploring the potential of deep learning," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 13(2), pages 285-305, June.
    7. Mst. Shapna Akter & Hossain Shahriar & Reaz Chowdhury & M. R. C. Mahdy, 2022. "Forecasting the Risk Factor of Frontier Markets: A Novel Stacking Ensemble of Neural Network Approach," Future Internet, MDPI, vol. 14(9), pages 1-23, August.
    8. Francesco Audrino & Jonathan Chassot, 2024. "HARd to Beat: The Overlooked Impact of Rolling Windows in the Era of Machine Learning," Papers 2406.08041, arXiv.org.
    9. Thanasis Stengos, 2019. "Nonparametric Econometric Methods and Applications," JRFM, MDPI, vol. 12(4), pages 1-3, November.
    10. Takuo Higashide & Katsuyuki Tanaka & Takuji Kinkyo & Shigeyuki Hamori, 2021. "New Dataset for Forecasting Realized Volatility: Is the Tokyo Stock Exchange Co-Location Dataset Helpful for Expansion of the Heterogeneous Autoregressive Model in the Japanese Stock Market?," JRFM, MDPI, vol. 14(5), pages 1-18, May.
    11. Nikita Medvedev & Zhiguang Wang, 2022. "Multistep forecast of the implied volatility surface using deep learning," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(4), pages 645-667, April.
    12. Jung-sik Hong & Hyeongyu Yeo & Nam-Wook Cho & Taeuk Ahn, 2018. "Identification of Core Suppliers Based on E-Invoice Data Using Supervised Machine Learning," JRFM, MDPI, vol. 11(4), pages 1-13, October.
    13. Lamine Diane & Pradeep Brijlal, 2024. "Forecasting Stock Market Realized Volatility using Random Forest and Artificial Neural Network in South Africa," International Journal of Economics and Financial Issues, Econjournals, vol. 14(2), pages 5-14, March.
    14. Ashwin, Julian, 2024. "Financial news media and volatility: Is there more to newspapers than news?," Journal of Financial Markets, Elsevier, vol. 69(C).

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