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Forecasting the Volatility of the Cryptocurrency Market by GARCH and Stochastic Volatility

Author

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  • Jong-Min Kim

    (Division of Science and Mathematics, University of Minnesota-Morris, Morris, MN 56267, USA)

  • Chulhee Jun

    (Department of Finance, Bloomsburg University of Pennsylvania, Bloomsburg, PA 17815, USA)

  • Junyoup Lee

    (School of Business Administration, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea)

Abstract

This study examines the volatility of nine leading cryptocurrencies by market capitalization—Bitcoin, XRP, Ethereum, Bitcoin Cash, Stellar, Litecoin, TRON, Cardano, and IOTA-by using a Bayesian Stochastic Volatility (SV) model and several GARCH models. We find that when we deal with extremely volatile financial data, such as cryptocurrencies, the SV model performs better than the GARCH family models. Moreover, the forecasting errors of the SV model, compared with the GARCH models, tend to be more accurate as forecast time horizons are longer. This deepens our insight into volatility forecast models in the complex market of cryptocurrencies.

Suggested Citation

  • Jong-Min Kim & Chulhee Jun & Junyoup Lee, 2021. "Forecasting the Volatility of the Cryptocurrency Market by GARCH and Stochastic Volatility," Mathematics, MDPI, vol. 9(14), pages 1-16, July.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:14:p:1614-:d:590878
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    References listed on IDEAS

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    1. Ha, Le Thanh & Nham, Nguyen Thi Hong, 2022. "An application of a TVP-VAR extended joint connected approach to explore connectedness between WTI crude oil, gold, stock and cryptocurrencies during the COVID-19 health crisis," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    2. Rico-Peña, Juan Jesús & Arguedas-Sanz, Raquel & López-Martin, Carmen, 2023. "Models used to characterise blockchain features. A systematic literature review and bibliometric analysis," Technovation, Elsevier, vol. 123(C).
    3. Mensi, Walid & El Khoury, Rim & Ali, Syed Riaz Mahmood & Vo, Xuan Vinh & Kang, Sang Hoon, 2023. "Quantile dependencies and connectedness between the gold and cryptocurrency markets: Effects of the COVID-19 crisis," Research in International Business and Finance, Elsevier, vol. 65(C).
    4. Shafiqah Azman & Dharini Pathmanathan & Aerambamoorthy Thavaneswaran, 2022. "Forecasting the Volatility of Cryptocurrencies in the Presence of COVID-19 with the State Space Model and Kalman Filter," Mathematics, MDPI, vol. 10(17), pages 1-15, September.
    5. Samir Poudel & Rajendra Paudyal & Burak Cankaya & Naomi Sterlingsdottir & Marissa Murphy & Shital Pandey & Jorge Vargas & Khem Poudel, 2023. "Cryptocurrency price and volatility predictions with machine learning," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 642-660, December.
    6. Apostolos Ampountolas, 2022. "Cryptocurrencies Intraday High-Frequency Volatility Spillover Effects Using Univariate and Multivariate GARCH Models," IJFS, MDPI, vol. 10(3), pages 1-22, July.

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