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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.
Highlights
One of the reasons why cryptocurrencies—and in particular Bitcoin introduced by Nakamoto (2009)—became so popular in 2017 has been their huge price increase which caught the attention from both the media and regular people
Our results suggest that: (i) the predictability of Bitcoin realized volatility has increased over time; (ii) including the leverage component helps in predicting future volatility levels; and (iii) predictability varies with the forecast horizon
We analysed Bitcoin returns sampled at high frequency and its realized variance
Summary
One of the reasons why cryptocurrencies—and in particular Bitcoin introduced by Nakamoto (2009)—became so popular in 2017 has been their huge price increase which caught the attention from both the media and regular people. In contrast to Catania and Grassi (2017) and Ardia et al (2018) who find an “inverted” leverage effect, our results show that Bitcoin exhibits a leverage effect similar to that of equity assets when this is measured using the Realized Volatility estimator This last point supports the arguments of Dyhrberg (2016) who classify Bitcoin as an asset and not as an exchange rate. Similar to standard high-frequency financial time-series, raw tick-by-tick Bitcoin prices are contaminated by wrongly reported observations. We find that the outcome of the cleaning procedure is similar to that of Brownlees and Gallo (2006) suggesting that no particular attention should be made when dealing with high-frequency Bitcoin prices compared to the standard procedure employed for other financial series.
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