Forecasting cryptocurrency volatility

【Author】 Catania, Leopoldo; Grassi, Stefano

【Source】INTERNATIONAL JOURNAL OF FORECASTING

【影响因子】7.022

【Abstract】This paper studies the behavior of cryptocurrencies' financial time series, of which Bitcoin is the most prominent example. The dynamics of these series are quite complex, displaying extreme observations, asymmetries, and several nonlinear characteristics that are difficult to model and forecast. We develop a new dynamic model that is able to account for long memory and asymmetries in the volatility process, as well as for the presence of time-varying skewness and kurtosis. The empirical application, carried out on 606 cryptocurrencies, indicates that a robust filter for the volatility of cryptocurrencies is strongly required. Forecasting results show that the inclusion of time-varying skewness systematically improves volatility, density, and quantile predictions at different horizons. (C) 2021 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.

【Keywords】Cryptocurrency; Bitcoin; Score-driven model; Density prediction; Volatility prediction; Leverage effect; Long memory; Higher-order moments

【发表时间】2022 JUL-SEP

【收录时间】2022-08-28

【文献类型】实证数据

【主题类别】

区块链治理-市场治理-市场分析