On the predictive power of ARJI volatility forecasts for Bitcoin

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

Applied Economics, 2019, vol. 51, issue 44, 4849-4855

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.

Date: 2019
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DOI: 10.1080/00036846.2019.1602714

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