Bitcoin return volatility forecasting using nonparametric GARCH models

Sami Mestiri ()
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Sami Mestiri: EAS-Mahdia Research Unit, Faculty of Science Mahdia Management and Economics, University of Monastir, Monastir, Tunisia

International Journal of Financial Engineering (IJFE), 2024, vol. 11, issue 04, 1-15

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 characterize the volatility of Bitcoin returns is widely observed in the empirical literature. In this paper, we consider an alternative approach involving nonparametric method to model and forecast Bitcoin return volatility. We show that the out-of-sample volatility forecast of the nonparametric 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 nonparametric 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: C01 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S242478632450018X

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