Modelling the volatility of Bitcoin returns using Nonparametric GARCH models

MPRA Paper from University Library of Munich, Germany

Abstract: Bitcoin has received a lot of attention from both investors and analysts, as it forms the highest market capitalization in the cryptocurrency market. The use of parametric GARCH models to characterise the volatility of Bitcoin returns is widely observed in the empirical literature. In this paper, we consider an alternative approach involving non-parametric method to model and forecast Bitcoin return volatility. We show that the out-of-sample volatility forecast of the non-parametric GARCH model yields superior performance relative to an extensive class of parametric GARCH models. The improvement in forecasting accuracy of Bitcoin return volatility based on the non-parametric GARCH model suggests that this method offers an attractive and viable alternative to the commonly used parametric GARCH models.

Keywords: Bitcoin; volatility; GARCH; Nonparametric; Forecasting. (search for similar items in EconPapers)
JEL-codes: C14 C53 C58 (search for similar items in EconPapers)
Date: 2021-12-13
New Economics Papers: this item is included in nep-cwa, nep-ets, nep-fmk, nep-for, nep-ore, nep-pay and nep-rmg
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Related works:
Journal Article: Modeling the volatility of Bitcoin returns using Nonparametric GARCH models (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:111116

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