Modeling the volatility of Bitcoin returns using Nonparametric GARCH models

Sami Mestiri ()

Journal of Academic Finance, 2022, vol. 13, issue 1, 2-16

Abstract:

Objective: The purpose of this paper is to demonstrate the effectiveness of the nonparametric GARCH model for the prediction of future Bitcoin prices. \n

\n

Methodology: The parametric GARCH models to characterize the volatility of Bitcoin returns are widely used in the empirical literature. Alternatively, we consider a non-parametric approach to model and forecast the volatility of Bitcoin returns. \n

\n

Results: We show that the volatility forecast of the nonparametric GARCH model yields superior performance compared to an extended class of parametric GARCH models. \n

\n

Originality / relevance: The improved accuracy of forecasting the volatility of Bitcoin returns based on the nonparametric GARCH model suggests that this method offers an attractive and viable alternative to commonly used GARCH parametric models.

JEL-codes: G3 M1 N8 (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.scientific-society.com/AF/article/view/489 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jaf:journl:v:13:y:2022:i:1:n:489

Access Statistics for this article

Journal of Academic Finance is currently edited by Jamel Henchiri

More articles in Journal of Academic Finance from RED research unit, university of Gabes, Tunisia Contact information at EDIRC.
Bibliographic data for series maintained by Oussama Quentin Kasseh ().