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Forecasting Realized Volatility A Review

Author

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  • Andrea BUCCI

    (Department of Economics and Social Sciences Marche Polytechnic University Ancona Italy)

Abstract

Modeling financial volatility is an important part of empirical finance This paper provides a literature review of the most relevant volatility models with a particular focus on forecasting models We firstly discuss the empirical foundations of different kinds of volatility The paper then analyses the non parametric measure of volatility named realized variance and its empirical applications A wide range of realized volatility models both univariate and multivariate is presented such as time series models MIDAS and GARCH MIDAS models Realized GARCH and HEAVY models We further discuss forecasting evaluation methods specifically suited for volatility models

Suggested Citation

  • Andrea BUCCI, 2017. "Forecasting Realized Volatility A Review," Journal of Advanced Studies in Finance, ASERS Publishing, vol. 8(2), pages 94-138.
    • Handle: RePEc:srs:jasf00:v:8:y:2017:i:2:p:94-138
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    Cited by:

    1. Hardik A. Marfatia & Qiang Ji & Jiawen Luo, 2022. "Forecasting the volatility of agricultural commodity futures: The role of co‐volatility and oil volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 383-404, March.
    2. Nikitopoulos, Christina Sklibosios & Thomas, Alice Carole & Wang, Jianxin, 2023. "The economic impact of daily volatility persistence on energy markets," Journal of Commodity Markets, Elsevier, vol. 30(C).
    3. 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.
    4. Jingyi Wei & Steve Yang & Zhenyu Cui, 2025. "Unified GARCH-Recurrent Neural Network in Financial Volatility Forecasting," Papers 2504.09380, arXiv.org, revised Nov 2025.
    5. Isaenko, Sergey, 2023. "Trading strategies and the frequency of time-series," The Quarterly Review of Economics and Finance, Elsevier, vol. 90(C), pages 267-283.

    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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