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Using High Frequency Data to Calculate, Model and Forecast Realized Volatility

Author

Listed:
  • Roel Oomen

Abstract

The objective of this paper is to calculate, model, and forecast realized volatility using high-frequency stock-market index data. The approach differs from existing ones in several ways. First, it is shown that the decay of the serial dependence of high-frequency returns on the sampling frequency is consistent with an ARMA process under temporal aggregation. This is important in modelling high-frequency returns and chosing the optimal sampling frequency when calculating realized volatility. Second, as a result of several test statistics for long memory in realized volatility, it is found that the realized volatility series can be modelled as an ARFIMA process. The ARFIMA's forecasting performance is assessed in a simulation study, and, although it outperforms representative GARCH models, it does so with greater complexity and data intensiveness that may not be worthwhile relative to GARCH's simplicity and flexibility.

Suggested Citation

  • Roel Oomen, 2001. "Using High Frequency Data to Calculate, Model and Forecast Realized Volatility," Computing in Economics and Finance 2001 75, Society for Computational Economics.
    • Handle: RePEc:sce:scecf1:75
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    Citations

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    Cited by:

    1. Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2020. "Forecasting realized oil-price volatility: The role of financial stress and asymmetric loss," Journal of International Money and Finance, Elsevier, vol. 104(C).
    2. Degiannakis, Stavros & Livada, Alexandra, 2013. "Realized volatility or price range: Evidence from a discrete simulation of the continuous time diffusion process," Economic Modelling, Elsevier, vol. 30(C), pages 212-216.
    3. Riza Demirer & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2022. "Risk aversion and the predictability of crude oil market volatility: A forecasting experiment with random forests," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 73(8), pages 1755-1767, August.
    4. van Mierlo, J.G.A., 2001. "Over de verhouding tussen overheid, marktwerking en privatisering. Een economische meta-analyse," Research Memorandum 014, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    5. Giot, Pierre & Laurent, Sebastien, 2004. "Modelling daily Value-at-Risk using realized volatility and ARCH type models," Journal of Empirical Finance, Elsevier, vol. 11(3), pages 379-398, June.
    6. Su, Fei & Wang, Xinyi & Yuan, Yulin, 2022. "The intraday dynamics and intraday price discovery of bitcoin," Research in International Business and Finance, Elsevier, vol. 60(C).
    7. Degiannakis, Stavros & Filis, George, 2016. "Forecasting oil price realized volatility: A new approach," MPRA Paper 69105, University Library of Munich, Germany.
    8. Degiannakis, Stavros & Xekalaki, Evdokia, 2004. "Autoregressive Conditional Heteroskedasticity (ARCH) Models: A Review," MPRA Paper 80487, University Library of Munich, Germany.
    9. Konstantinos Gkillas & Christoforos Konstantatos & Costas Siriopoulos, 2021. "Uncertainty Due to Infectious Diseases and Stock–Bond Correlation," Econometrics, MDPI, vol. 9(2), pages 1-18, April.

    More about this item

    Keywords

    High Frequency Data; Long Memory; GARCH; Realized Volatility;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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