IDEAS home Printed from https://ideas.repec.org/a/spr/snopef/v6y2025i2d10.1007_s43069-025-00482-5.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s43069-025-00482-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s43069-025-00482-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kevin Dowd & Martin Hutchinson, 2015. "Bitcoin Will Bite the Dust," Cato Journal, Cato Journal, Cato Institute, vol. 35(2), pages 357-382, Spring/Su.
    2. Yechen Zhu & David Dickinson & Jianjun Li, 2017. "Erratum to: Analysis on the influence factors of Bitcoin’s price based on VEC model," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 3(1), pages 1-1, December.
    3. Thierry Ane, 2006. "Short and long term components of volatility in Hong Kong stock returns," Applied Financial Economics, Taylor & Francis Journals, vol. 16(6), pages 439-460.
    4. Loredana MAFTEI, 2014. "Bitcoin - Between Legal And Informal," CES Working Papers, Centre for European Studies, Alexandru Ioan Cuza University, vol. 6(3), pages 53-59, September.
    5. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    6. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    7. Muhammad MOHSIN & Sobia NASEEM & Larisa IVAȘCU & Lucian-Ionel CIOCA & Muddassar SARFRAZ & Nicolae Cristian STĂNICĂ, 2021. "Gauging the Effect of Investor Sentiment on Cryptocurrency Market: An Analysis of Bitcoin Currency," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 86-102, December.
    8. Athanasia Gavala & Nikolay Gospodinov & Deming Jiang, 2006. "Forecasting volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(6), pages 381-400.
    9. Urquhart, Andrew, 2016. "The inefficiency of Bitcoin," Economics Letters, Elsevier, vol. 148(C), pages 80-82.
    10. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.
    11. Engle, Robert F. & White (the late), Halbert (ed.), 1999. "Cointegration, Causality, and Forecasting: Festschrift in Honour of Clive W. J. Granger," OUP Catalogue, Oxford University Press, number 9780198296836, Decembrie.
    12. repec:eme:mfppss:eb018539 is not listed on IDEAS
    13. repec:hal:journl:hal-04448652 is not listed on IDEAS
    14. Muhammad MOHSIN & Sobia NASEEM & Larisa IVAȘCU & Lucian-Ionel CIOCA & Muddassar SARFRAZ & Nicolae Cristian STĂNICĂ, 2021. "Gauging the Effect of Investor Sentiment on Cryptocurrency Market: An Analysis of Bitcoin Currency," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 87-102, December.
    15. Ahmet Faruk Aysan & Asad Ul Islam Khan & Humeyra Topuz, 2021. "Bitcoin and Altcoins Price Dependency: Resilience and Portfolio Allocation in COVID-19 Outbreak," Risks, MDPI, vol. 9(4), pages 1-13, April.
    16. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    17. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    18. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    19. Bariviera, Aurelio F. & Basgall, María José & Hasperué, Waldo & Naiouf, Marcelo, 2017. "Some stylized facts of the Bitcoin market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 484(C), pages 82-90.
    20. Muhammad Mohsin & Li Naiwen & Muhammad Zia-UR-Rehman & Sobia Naseem & Sajjad Ahmad Baig, 2020. "The volatility of bank stock prices and macroeconomic fundamentals in the Pakistani context: an application of GARCH and EGARCH models," Oeconomia Copernicana, Institute of Economic Research, vol. 11(4), pages 609-636, December.
    21. Stephen Chan & Jeffrey Chu & Saralees Nadarajah & Joerg Osterrieder, 2017. "A Statistical Analysis of Cryptocurrencies," JRFM, MDPI, vol. 10(2), pages 1-23, May.
    22. Bourghelle, David & Jawadi, Fredj & Rozin, Philippe, 2022. "Do collective emotions drive bitcoin volatility? A triple regime-switching vector approach," Journal of Economic Behavior & Organization, Elsevier, vol. 196(C), pages 294-306.
    23. Zakoian, Jean-Michel, 1994. "Threshold heteroskedastic models," Journal of Economic Dynamics and Control, Elsevier, vol. 18(5), pages 931-955, September.
    24. Katsiampa, Paraskevi, 2017. "Volatility estimation for Bitcoin: A comparison of GARCH models," Economics Letters, Elsevier, vol. 158(C), pages 3-6.
    25. Dyhrberg, Anne Haubo, 2016. "Bitcoin, gold and the dollar – A GARCH volatility analysis," Finance Research Letters, Elsevier, vol. 16(C), pages 85-92.
    26. Jiri Kukacka & Ladislav Kristoufek, 2023. "Fundamental and speculative components of the cryptocurrency pricing dynamics," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-23, December.
    27. Yechen Zhu & David Dickinson & Jianjun Li, 2017. "Analysis on the influence factors of Bitcoin’s price based on VEC model," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 3(1), pages 1-13, December.
    28. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
    29. T. Ane, 2006. "Short and long term components of volatility in Hong Kong stock returns," Post-Print hal-00170780, HAL.
    30. S. M. Abdullah & Salina Siddiqua & Muhammad Shahadat Hossain Siddiquee & Nazmul Hossain, 2017. "Modeling and forecasting exchange rate volatility in Bangladesh using GARCH models: a comparison based on normal and Student’s t-error distribution," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 3(1), pages 1-19, December.
    31. Chowdhury, Abdur, 2014. "Is Bitcoin the 'Paris Hilton' of the Currency World? Or Are the Early Investors onto Something That Will Make Them Rich?," Working Papers and Research 2014-01, Marquette University, Center for Global and Economic Studies and Department of Economics.
    32. Liam Gallagher, 1999. "A multi-country analysis of the temporary and permanent components of stock prices," Applied Financial Economics, Taylor & Francis Journals, vol. 9(2), pages 129-142.
    33. Brandvold, Morten & Molnár, Peter & Vagstad, Kristian & Andreas Valstad, Ole Christian, 2015. "Price discovery on Bitcoin exchanges," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 36(C), pages 18-35.
    34. Jiqian Wang & Feng Ma & Elie Bouri & Yangli Guo, 2023. "Which factors drive Bitcoin volatility: Macroeconomic, technical, or both?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 970-988, July.
    35. Michael Hughes & Drew Winters & Jerry Rawls, 2005. "What is the source of different levels of time-series return volatility? the intraday U-shaped pattern or time-series persistence," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 29(3), pages 300-312, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Trucíos, Carlos, 2019. "Forecasting Bitcoin risk measures: A robust approach," International Journal of Forecasting, Elsevier, vol. 35(3), pages 836-847.
    2. Roy Cerqueti & Massimiliano Giacalone & Raffaele Mattera, 2020. "Skewed non-Gaussian GARCH models for cryptocurrencies volatility modelling," Papers 2004.11674, arXiv.org.
    3. Jeffrey Chu & Stephen Chan & Saralees Nadarajah & Joerg Osterrieder, 2017. "GARCH Modelling of Cryptocurrencies," JRFM, MDPI, vol. 10(4), pages 1-15, October.
    4. Cheikh, Nidhaleddine Ben & Zaied, Younes Ben & Chevallier, Julien, 2020. "Asymmetric volatility in cryptocurrency markets: New evidence from smooth transition GARCH models," Finance Research Letters, Elsevier, vol. 35(C).
    5. Andrea Flori, 2019. "Cryptocurrencies In Finance: Review And Applications," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(05), pages 1-22, August.
    6. Cristina Chinazzo & Vahidin Jeleskovic, 2024. "Forecasting Bitcoin Volatility: A Comparative Analysis of Volatility Approaches," Papers 2401.02049, arXiv.org.
    7. Nikolaos A. Kyriazis, 2021. "A Survey on Volatility Fluctuations in the Decentralized Cryptocurrency Financial Assets," JRFM, MDPI, vol. 14(7), pages 1-46, June.
    8. Klein, Tony & Pham Thu, Hien & Walther, Thomas, 2018. "Bitcoin is not the New Gold – A comparison of volatility, correlation, and portfolio performance," International Review of Financial Analysis, Elsevier, vol. 59(C), pages 105-116.
    9. Mehmet Sahiner, 2022. "Forecasting volatility in Asian financial markets: evidence from recursive and rolling window methods," SN Business & Economics, Springer, vol. 2(10), pages 1-74, October.
    10. Nikolaos A. Kyriazis, 2019. "A Survey on Efficiency and Profitable Trading Opportunities in Cryptocurrency Markets," JRFM, MDPI, vol. 12(2), pages 1-17, April.
    11. Gavriilidis, Konstantinos & Kambouroudis, Dimos S. & Tsakou, Katerina & Tsouknidis, Dimitris A., 2018. "Volatility forecasting across tanker freight rates: The role of oil price shocks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 118(C), pages 376-391.
    12. Muhammad Sheraz & Imran Nasir, 2021. "Information-Theoretic Measures and Modeling Stock Market Volatility: A Comparative Approach," Risks, MDPI, vol. 9(5), pages 1-20, May.
    13. Abakah, Emmanuel Joel Aikins & Gil-Alana, Luis Alberiko & Madigu, Godfrey & Romero-Rojo, Fatima, 2020. "Volatility persistence in cryptocurrency markets under structural breaks," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 680-691.
    14. Köksal, Bülent, 2009. "A Comparison of Conditional Volatility Estimators for the ISE National 100 Index Returns," MPRA Paper 30510, University Library of Munich, Germany.
    15. Tae-Hwy Lee & Yong Bao & Burak Saltoğlu, 2007. "Comparing density forecast models Previous versions of this paper have been circulated with the title, 'A Test for Density Forecast Comparison with Applications to Risk Management' since October 2003;," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(3), pages 203-225.
    16. Siwen Zhou, 2021. "Exploring the driving forces of the Bitcoin currency exchange rate dynamics: an EGARCH approach," Empirical Economics, Springer, vol. 60(2), pages 557-606, February.
    17. El Jebari, Ouael & Hakmaoui, Abdelati, 2018. "GARCH Family Models vs EWMA: Which is the Best Model to Forecast Volatility of the Moroccan Stock Exchange Market? || Modelos de la familia GARCH vs EWMA: ¿cuál es el mejor modelo para pronosticar la ," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 26(1), pages 237-249, Diciembre.
    18. Walther, Thomas & Klein, Tony & Bouri, Elie, 2019. "Exogenous drivers of Bitcoin and Cryptocurrency volatility – A mixed data sampling approach to forecasting," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 63(C).
    19. Turgut Kısınbay, 2010. "Predictive ability of asymmetric volatility models at medium-term horizons," Applied Economics, Taylor & Francis Journals, vol. 42(30), pages 3813-3829.
    20. Harvey,Andrew C., 2013. "Dynamic Models for Volatility and Heavy Tails," Cambridge Books, Cambridge University Press, number 9781107630024, June.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:snopef:v:6:y:2025:i:2:d:10.1007_s43069-025-00482-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.