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Forecasting realised volatility using ARFIMA and HAR models

Author

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  • Marwan Izzeldin
  • M. Kabir Hassan
  • Vasileios Pappas
  • Mike Tsionas

Abstract

Recent literature provides mixed empirical evidence with respect to the forecasting performance of ARFIMA and HAR models. This paper compares the forecasting performance of both models using high frequency data of 100 stocks representing 10 business sectors for the period 2000-2010. We allow for different sectors, changing market conditions, variation in the sampling frequency and forecasting horizons. For the overall sample and using the 300 sec sampling frequency, the forecasting performance of both models is indistinguishable. However, differences arise under different market regimes, forecasting horizons and sampling frequencies. ARFIMA models are superior for the crisis and pre-crisis sub-samples. HAR forecasts are less sensitive to regime change and to longer forecasting horizons. Variations in forecasting performance could also be explained using differences in the levels of persistence underlying each model.

Suggested Citation

  • Marwan Izzeldin & M. Kabir Hassan & Vasileios Pappas & Mike Tsionas, 2019. "Forecasting realised volatility using ARFIMA and HAR models," Quantitative Finance, Taylor & Francis Journals, vol. 19(10), pages 1627-1638, October.
    • Handle: RePEc:taf:quantf:v:19:y:2019:i:10:p:1627-1638
    DOI: 10.1080/14697688.2019.1600713
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    Citations

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    Cited by:

    1. Alessio Brini & Giacomo Toscano, 2024. "SpotV2Net: Multivariate Intraday Spot Volatility Forecasting via Vol-of-Vol-Informed Graph Attention Networks," Papers 2401.06249, arXiv.org, revised Aug 2024.
    2. Chao Zhang & Yihuang Zhang & Mihai Cucuringu & Zhongmin Qian, 2022. "Volatility forecasting with machine learning and intraday commonality," Papers 2202.08962, arXiv.org, revised Feb 2023.
    3. Verena Monschang & Bernd Wilfling, 2022. "A procedure for upgrading linear-convex combination forecasts with an application to volatility prediction," CQE Working Papers 9722, Center for Quantitative Economics (CQE), University of Muenster.
    4. Chuxuan Xiao & Winifred Huang & David P. Newton, 2024. "Predicting expected idiosyncratic volatility: Empirical evidence from ARFIMA, HAR, and EGARCH models," Review of Quantitative Finance and Accounting, Springer, vol. 63(3), pages 979-1006, October.
    5. Reschenhofer, Erhard & Mangat, Manveer Kaur & Stark, Thomas, 2020. "Volatility forecasts, proxies and loss functions," Journal of Empirical Finance, Elsevier, vol. 59(C), pages 133-153.
    6. Damian Kisiel & Denise Gorse, 2021. "A Meta-Method for Portfolio Management Using Machine Learning for Adaptive Strategy Selection," Papers 2111.05935, arXiv.org.
    7. Caio Mário Mesquita & Cristiano Arbex Valle & Adriano César Machado Pereira, 2024. "Scenario Generation for Financial Data with a Machine Learning Approach Based on Realized Volatility and Copulas," Computational Economics, Springer;Society for Computational Economics, vol. 63(5), pages 1879-1919, May.

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