Transaction activity and bitcoin realized volatility

【Author】 Gkillas, Konstantinos; Tantoula, Maria; Tzagarakis, Manolis

【Source】OPERATIONS RESEARCH LETTERS

【影响因子】1.151

【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. (C) 2021 Elsevier B.V. All rights reserved.

【Keywords】Bitcoin; Random Forests; Realized volatility; Transaction activity

【发表时间】2021 SEP

【收录时间】2022-01-02

【文献类型】

【主题类别】

--