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Forecast comparison of volatility models on Russian stock market


  • Aganin, Artem

    (National Research University Higher School of Economics, Moscow, Russian Federation)


This article is dedicated to multivariate comparison of big number of GARCH, ARFIMA and HAR-RV families’ models considering their one-day ahead realized volatility, which is known to be a consistent measure of daily volatility. A total of 102 models from three families were included in comparison. Comparison was completed with the help of Model Confidence Set test using 3 different loss functions on 10 Russian stock assets, including eight stock assets and two stock market indices. Received results strongly suggest HAR-RV superior performance to other two families of volatility models on Russian stock market and confirm local findings of previous studies

Suggested Citation

  • Aganin, Artem, 2017. "Forecast comparison of volatility models on Russian stock market," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 48, pages 63-84.
  • Handle: RePEc:ris:apltrx:0331

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    References listed on IDEAS

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    Cited by:

    1. Nagapetyan, Artur, 2019. "Precondition stock and stock indices volatility modeling based on market diversification potential: Evidence from Russian market," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 56, pages 45-61.
    2. Wang, Jiqian & Lu, Xinjie & He, Feng & Ma, Feng, 2020. "Which popular predictor is more useful to forecast international stock markets during the coronavirus pandemic: VIX vs EPU?," International Review of Financial Analysis, Elsevier, vol. 72(C).
    3. Fan, Lina & Yang, Hao & Zhai, Jia & Zhang, Xiaotao, 2023. "Forecasting stock volatility during the stock market crash period: The role of Hawkes process," Finance Research Letters, Elsevier, vol. 55(PA).
    4. Fantazzini, Dean & Shangina, Tamara, 2019. "The importance of being informed: forecasting market risk measures for the Russian RTS index future using online data and implied volatility over two decades," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 55, pages 5-31.
    5. Vladimir Pyrlik & Pavel Elizarov & Aleksandra Leonova, 2021. "Forecasting Realized Volatility Using Machine Learning and Mixed-Frequency Data (the Case of the Russian Stock Market)," CERGE-EI Working Papers wp713, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    6. Telegin, O., 2022. "Bank of Russia regular communications and volatility short-term effects in financial markets," Journal of the New Economic Association, New Economic Association, vol. 54(2), pages 130-155.

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    More about this item


    GARCH; realized volatility; HAR-RV; MCS;
    All these keywords.

    JEL classification:

    • 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
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics


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