IDEAS home Printed from https://ideas.repec.org/a/gam/jrisks/v10y2022i12p237-d1002469.html
   My bibliography  Save this article

Forecasting Bitcoin Volatility Using Hybrid GARCH Models with Machine Learning

Author

Listed:
  • Mamoona Zahid

    (Department of Statistics, University of Balochistan, Quetta 87300, Pakistan)

  • Farhat Iqbal

    (Department of Statistics, University of Balochistan, Quetta 87300, Pakistan)

  • Dimitrios Koutmos

    (Department of Accounting, Finance, and Business Law, College of Business, Texas A&M University-Corpus Christi, Corpus Christi, TX 78412, USA)

Abstract

The time series movements of Bitcoin prices are commonly characterized as highly nonlinear and volatile in nature across economic periods, when compared to the characteristics of traditional asset classes, such as equities and commodities. From a risk management perspective, such behaviors pose challenges, given the difficulty in quantifying and modeling Bitcoin’s price volatility. In this study, we propose hybrid analytical techniques that combine the strengths of the non-stationary properties of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models with the nonlinear modeling capabilities of deep learning algorithms, such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM) algorithms with single, double, and triple layer network architectures to forecast Bitcoin’s realized price volatility. Our findings, both in-sample and out-of-sample, show that such hybrid models can generate accurate forecasts of Bitcoin’s price volatility.

Suggested Citation

  • Mamoona Zahid & Farhat Iqbal & Dimitrios Koutmos, 2022. "Forecasting Bitcoin Volatility Using Hybrid GARCH Models with Machine Learning," Risks, MDPI, vol. 10(12), pages 1-18, December.
    • Handle: RePEc:gam:jrisks:v:10:y:2022:i:12:p:237-:d:1002469
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-9091/10/12/237/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-9091/10/12/237/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hyun Sik Sim & Hae In Kim & Jae Joon Ahn, 2019. "Is Deep Learning for Image Recognition Applicable to Stock Market Prediction?," Complexity, Hindawi, vol. 2019, pages 1-10, February.
    2. Ze Shen & Qing Wan & David J. Leatham, 2021. "Bitcoin Return Volatility Forecasting: A Comparative Study between GARCH and RNN," JRFM, MDPI, vol. 14(7), pages 1-18, July.
    3. Faisal Mohammad & Young-Chon Kim, 2020. "Energy load forecasting model based on deep neural networks for smart grids," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(4), pages 824-834, August.
    4. Jeffrey Chu & Stephen Chan & Saralees Nadarajah & Joerg Osterrieder, 2017. "GARCH Modelling of Cryptocurrencies," JRFM, MDPI, vol. 10(4), pages 1-15, October.
    5. Al-Yahyaee, Khamis Hamed & Mensi, Walid & Al-Jarrah, Idries Mohammad Wanas & Hamdi, Atef & Kang, Sang Hoon, 2019. "Volatility forecasting, downside risk, and diversification benefits of Bitcoin and oil and international commodity markets: A comparative analysis with yellow metal," The North American Journal of Economics and Finance, Elsevier, vol. 49(C), pages 104-120.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Malvina Marchese & María Dolores Martínez-Miranda & Jens Perch Nielsen & Michael Scholz, 2024. "Robustifying and simplifying high-dimensional regression with applications to yearly stock return and telematics data," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-16, December.

    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. Nikolaos A. Kyriazis, 2021. "A Survey on Volatility Fluctuations in the Decentralized Cryptocurrency Financial Assets," JRFM, MDPI, vol. 14(7), pages 1-46, June.
    2. Yaojie Zhang & Mengxi He & Danyan Wen & Yudong Wang, 2022. "Forecasting Bitcoin volatility: A new insight from the threshold regression model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 633-652, April.
    3. Theo Berger & Jana Koubová, 2024. "Forecasting Bitcoin returns: Econometric time series analysis vs. machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2904-2916, November.
    4. Walid Chkili, 2021. "Modeling Bitcoin price volatility: long memory vs Markov switching," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 11(3), pages 433-448, September.
    5. Köchling, Gerrit & Schmidtke, Philipp & Posch, Peter N., 2020. "Volatility forecasting accuracy for Bitcoin," Economics Letters, Elsevier, vol. 191(C).
    6. Pierre J. Venter & Eben Maré, 2020. "GARCH Generated Volatility Indices of Bitcoin and CRIX," JRFM, MDPI, vol. 13(6), pages 1-15, June.
    7. Angerer, Martin & Hoffmann, Christian Hugo & Neitzert, Florian & Kraus, Sascha, 2021. "Objective and subjective risks of investing into cryptocurrencies," Finance Research Letters, Elsevier, vol. 40(C).
    8. Chen, Yan & Zhang, Lei & Bouri, Elie, 2024. "Can a self-exciting jump structure better capture the jump behavior of cryptocurrencies? A comparative analysis with the S&P 500," Research in International Business and Finance, Elsevier, vol. 69(C).
    9. Okorie, David Iheke & Lin, Boqiang, 2020. "Crude oil price and cryptocurrencies: Evidence of volatility connectedness and hedging strategy," Energy Economics, Elsevier, vol. 87(C).
    10. Panayiotis Theodossiou & Polina Ellina & Christos S. Savva, 2022. "Stochastic properties and pricing of bitcoin using a GJR-GARCH model with conditional skewness and kurtosis components," Review of Quantitative Finance and Accounting, Springer, vol. 59(2), pages 695-716, August.
    11. Wang, Xuetong & Fang, Fang & Ma, Shiqun & Xiang, Lijin & Xiao, Zumian, 2024. "Dynamic volatility spillover among cryptocurrencies and energy markets: An empirical analysis based on a multilevel complex network," The North American Journal of Economics and Finance, Elsevier, vol. 69(PA).
    12. Samuel Asante Gyamerah, 2019. "Estimating the volatility of Bitcoin using GARCH models," Papers 1909.04903, arXiv.org, revised Oct 2019.
    13. C. Y. Tan & Y. B. Koh & K. H. Ng & K. H. Ng, 2019. "Structural Change Analysis of Active Cryptocurrency Market," Papers 1909.10679, arXiv.org.
    14. Omane-Adjepong, Maurice & Alagidede, Paul & Akosah, Nana Kwame, 2019. "Wavelet time-scale persistence analysis of cryptocurrency market returns and volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 105-120.
    15. Naeem, Muhammad & Bouri, Elie & Boako, Gideon & Roubaud, David, 2020. "Tail dependence in the return-volume of leading cryptocurrencies," Finance Research Letters, Elsevier, vol. 36(C).
    16. 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.
    17. Eska, Fabian E. & Shi, Yanghua & Theissen, Erik & Uhrig-Homburg, Marliese, 2024. "Do design features explain the volatility of cryptocurrencies?," Finance Research Letters, Elsevier, vol. 66(C).
    18. Lepomäki, Laura & Kanniainen, Juho & Hansen, Henri, 2021. "Retaliation in Bitcoin networks," Economics Letters, Elsevier, vol. 203(C).
    19. Gregor Dorfleitner & Carina Lung, 2018. "Cryptocurrencies from the perspective of euro investors: a re-examination of diversification benefits and a new day-of-the-week effect," Journal of Asset Management, Palgrave Macmillan, vol. 19(7), pages 472-494, December.
    20. Matej Steinbacher, 2023. "Predicting Stock Price Movement as an Image Classification Problem," Papers 2303.01111, arXiv.org.

    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:gam:jrisks:v:10:y:2022:i:12:p:237-:d:1002469. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.