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On the predictive power of ARJI volatility forecasts for Bitcoin

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  • Jying-Nan Wang
  • Hung-Chun Liu
  • Shu-Mei Chiang
  • Yuan-Teng Hsu

Abstract

Motivated by the recent literature on cryptocurrency volatility dynamics, this paper adopts the ARJI, GARCH, EGARCH, and CGARCH models to explore their capabilities to make out-of-sample volatility forecasts for Bitcoin returns over a daily horizon from 2013 to 2018. The empirical results indicate that the ARJI jump model can cope with the extreme price movements of Bitcoin, showing comparatively superior in-sample goodness-of-fit, as well as out-of-sample predictive performance. However, due to the excessive volatility swings on the cryptocurrency market, the realized volatility of Bitcoin prices is only marginally explained by the GARCH genre of employed models.

Suggested Citation

  • Jying-Nan Wang & Hung-Chun Liu & Shu-Mei Chiang & Yuan-Teng Hsu, 2019. "On the predictive power of ARJI volatility forecasts for Bitcoin," Applied Economics, Taylor & Francis Journals, vol. 51(44), pages 4849-4855, September.
    • Handle: RePEc:taf:applec:v:51:y:2019:i:44:p:4849-4855
    DOI: 10.1080/00036846.2019.1602714
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    Citations

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    Cited by:

    1. Júlio Lobão, 2022. "Herding Behavior in the Market for Green Cryptocurrencies: Evidence from CSSD and CSAD Approaches," Sustainability, MDPI, vol. 14(19), pages 1-17, October.
    2. Hung, Jui-Cheng & Liu, Hung-Chun & Yang, J. Jimmy, 2020. "Improving the realized GARCH’s volatility forecast for Bitcoin with jump-robust estimators," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    3. Parthajit Kayal & G. Balasubramanian, 2021. "Excess Volatility in Bitcoin: Extreme Value Volatility Estimation," IIM Kozhikode Society & Management Review, , vol. 10(2), pages 222-231, July.
    4. Yaojie Zhang & Mengxi He & Danyan Wen & Yudong Wang, 2022. "Forecasting Bitcoin volatility: A new insight from the threshold regression model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 633-652, April.
    5. Zhang, Chuanhai & Chen, Haicui & Peng, Zhe, 2022. "Does Bitcoin futures trading reduce the normal and jump volatility in the spot market? Evidence from GARCH-jump models," Finance Research Letters, Elsevier, vol. 47(PB).
    6. Li Wei & Ming-Chih Lee & Wan-Hsiu Cheng & Chia-Hsien Tang & Jing-Wun You, 2023. "Evaluating the Efficiency of Financial Assets as Hedges against Bitcoin Risk during the COVID-19 Pandemic," Mathematics, MDPI, vol. 11(13), pages 1-19, June.
    7. Jiqian Wang & Feng Ma & Elie Bouri & Yangli Guo, 2023. "Which factors drive Bitcoin volatility: Macroeconomic, technical, or both?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 970-988, July.
    8. Pho, Kim Hung & Ly, Sel & Lu, Richard & Hoang, Thi Hong Van & Wong, Wing-Keung, 2021. "Is Bitcoin a better portfolio diversifier than gold? A copula and sectoral analysis for China," International Review of Financial Analysis, Elsevier, vol. 74(C).

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