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Leopoldo Catania

Bitcoin at High Frequency

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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 hours. 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
Original languageEnglish
Article number36
JournalJournal of Risk and Financial Management
Number of pages20
Publication statusPublished - 2019

    Research areas

  • ANYTHING BEAT, HAR, MODELS, RETURNS, RISK, VOLATILITY, bitcoin, high frequency, realized volatility

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