Analyzing Volatility Patterns of Bitcoin Using the GARCH Family Models

Saqib Muneer (), Cristiana Cerqueira Leal () and Benilde Oliveira ()
Additional contact information
Saqib Muneer: University of Ha’il
Cristiana Cerqueira Leal: University of Minho
Benilde Oliveira: University of Minho

SN Operations Research Forum, 2025, vol. 6, issue 2, 1-13

Abstract: Abstract This study addresses the issue of modeling and forecasting Bitcoin volatility using daily closing prices from 18th July 18, 2015, to 04th September 4, 2023. This study endeavored to model the dynamics following AR (1)-GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) (1,1), AR (1)-PGARCH (1,1), AR (1)-EGARCH (1,1), AR (1)-(TGARCH) (Threshold Generalized Autoregressive Conditional Heteroscedasticity) (1,1), AR (1)-CGARCH (Component AutoRegressive Conditional Heteroskedasticity) (1,1), and AR (1)-ACGARCH (1,1) processes under a normal Gaussian distribution for errors. The results show that the AR (1)-ACGARCH (1,1) model is the best for modeling gold volatility and AR (1)-APARCH (1,1) for forecasting. Bitcoin can be an expedient tool for portfolio and risk management, and the results of this study will help investors make informed decisions.

Keywords: Bitcoin; GARCH models; Volatility; Forecasting (search for similar items in EconPapers)
JEL-codes: C5 C53 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s43069-025-00482-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:snopef:v:6:y:2025:i:2:d:10.1007_s43069-025-00482-5

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/43069

DOI: 10.1007/s43069-025-00482-5

Access Statistics for this article

SN Operations Research Forum is currently edited by Marco Lübbecke

More articles in SN Operations Research Forum from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().