Analyzing Volatility Patterns of Bitcoin Using the GARCH Family Models

被引：0

作者：

Saqib Muneer ^{[1
]}

Cristiana Cerqueira Leal ^{[2
]}

Benilde Oliveira ^{[3
]}

机构：

[1] University of Ha’il,Department of Economics and Finance, College of Business Administration

[2] University of Minho,School of Economics, Management and Political Science & NIPE, Center for Research in Economics and Management

[3] University of Minho,Management Department, School of Economics, Management and Political Science

来源：

Operations Research Forum | / 6卷 / 2期

关键词：

Bitcoin; GARCH models; Volatility; Forecasting; C5; C53;

D O I：

10.1007/s43069-025-00482-5

中图分类号：

学科分类号：

摘要：

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.

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