Bitcoin at High Frequency

Leopoldo Catania and Mads Sandholdt
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Leopoldo Catania: Department of Economics and Business Economics, Aarhus University and CREATES, Aarhus BSS, Fuglesangs Allé 4, DK-8210 Aarhus V, Denmark
Mads Sandholdt: Tvilum A/S, Egon Kristiansens Allé 2, DK-8882 Faarvang, Denmark

JRFM, 2019, vol. 12, issue 1, 1-20

Abstract: This paper studies the behaviour of Bitcoin returns at different sample frequencies. We consider high frequency returns starting from tick-by-tick price changes traded at the Bitstamp and Coinbase exchanges. We find evidence of a smooth intra-daily seasonality pattern, and an abnormal trade- and volatility intensity at Thursdays and Fridays. We find no predictability for Bitcoin returns at or above one day, though, we find predictability for sample frequencies up to 6 h. Predictability of Bitcoin returns is also found to be time–varying. We also study the behaviour of the realized volatility of Bitcoin. We document a remarkable high percentage of jumps above 80 % . We also find that realized volatility exhibits: (i) long memory; (ii) leverage effect; and (iii) no impact from lagged jumps. A forecast study shows that: (i) Bitcoin volatility has become more easy to predict after 2017; (ii) including a leverage component helps in volatility prediction; and (iii) prediction accuracy depends on the length of the forecast horizon.

Keywords: bitcoin; realized volatility; HAR; high frequency (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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