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Analyzing Volatility Patterns of Bitcoin Using the GARCH Family Models

Author

Listed:
  • Saqib Muneer

    (University of Ha’il)

  • Cristiana Cerqueira Leal

    (University of Minho)

  • Benilde Oliveira

    (University of Minho)

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.

Suggested Citation

  • Saqib Muneer & Cristiana Cerqueira Leal & Benilde Oliveira, 2025. "Analyzing Volatility Patterns of Bitcoin Using the GARCH Family Models," SN Operations Research Forum, Springer, vol. 6(2), pages 1-13, June.
    • Handle: RePEc:spr:snopef:v:6:y:2025:i:2:d:10.1007_s43069-025-00482-5
    DOI: 10.1007/s43069-025-00482-5
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    More about this item

    Keywords

    Bitcoin; GARCH models; Volatility; Forecasting;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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