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Modelling the volatility of Bitcoin returns using Nonparametric GARCH models

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  • Mestiri, Sami

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.

Suggested Citation

  • Mestiri, Sami, 2021. "Modelling the volatility of Bitcoin returns using Nonparametric GARCH models," MPRA Paper 111116, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:111116
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    More about this item

    Keywords

    Bitcoin; volatility; GARCH; Nonparametric; Forecasting.;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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