Transaction activity and bitcoin realized volatility |
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Institution: | 1. Department of Management Science and Technology, University of Patras, Greece;2. Department of Economics, University of Patras, 26504 Rio, Greece |
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Abstract: | We study the predictive value of transaction activity in the bitcoin network for the realized volatility of bitcoin returns constructed by high-frequency data. As an alternative modeling approach to the popular linear heterogeneous autoregressive model, we provide out-of-sample forecasts for realized volatility of bitcoin returns employing machine learning algorithms, and in particular by Random Forests. Our findings reveal that on-blockchain transaction activity does improve the out-of-sample forecast accuracy at all the forecast horizons considered. |
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Keywords: | Bitcoin Random Forests Realized volatility Transaction activity |
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