skip to main content
10.1145/3537693.3537712acmotherconferencesArticle/Chapter ViewAbstractPublication PagesConference Proceedings
research-article

Forecasting Cryptocurrency Volatility Using GARCH and ARCH Model

Authors Info & Claims
Published:09 July 2022Publication History

ABSTRACT

This research aims to analyze the calculation of volatility stage from five cryptocurrency products, which are Bitcoin, Ethereum, Binance Coin, Dashcoin, and Litecoin from 1st January 2018 to 1st April 2021 where it consists of calculation of each of the cryptocurrency products' volatility. The research method is a quantitative method by gaining data from Investing.com. Then, analyzing the data using Autoregressive Conditional Heteroscedasticity (ARCH) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models. This research aims to know whether ARCH and GARCH models apply to daily life situations in the field. The result shows that the data from ARCH and GARCH models are not suitable on daily basis. Further research should calculate cryptocurrency products to use differentiated GARCH models, such as GJR-GARCH or GARCH-MIDAS. It is also better to calculate the volatility of cryptocurrency products annually. According to some thesis, the volatility cryptocurrency products are more suitable to calculate annually than daily.

References

  1. Morrar, R., Arman, H., & Mousa, S., "The fourth industrial revolution (Industry 4.0): A social innovation perspective." Technology Innovation Management Review vol. 7, no. 11, pp. 12-20, Nov 2017.Google ScholarGoogle Scholar
  2. Lucke, Dominik, Carmen Constantinescu, and Engelbert Westkämper. "Smart factory-a step towards the next generation of manufacturing." Manufacturing systems and technologies for the new frontier, Springer, London, pp. 115-118, 2008.Google ScholarGoogle Scholar
  3. Ginantra, N. L. W. S. R., Simarmata, J., Purba, R. A., Tojiri, M. Y., Duwila, A. A., Siregar, M. N. H., and Siswanti, I., Teknologi Finansial: Sistem Finansial Berbasis Teknologi di Era Digital, Indonesia: Yayasan Kita Menulis, 2020.Google ScholarGoogle Scholar
  4. Rumondang, A., Sudirman, A., Effendy, F., Simarmata, J., & Agustin, T., Fintech: Inovasi Sistem Keuangan di Era Digital, Indonesia: Yayasan Kita Menulis, 2019.Google ScholarGoogle Scholar
  5. Nakamoto, Satoshi. "Bitcoin: A peer-to-peer electronic cash system." Decentralized Business Review (2008): 21260.Google ScholarGoogle Scholar
  6. Conrad, C., Custovic, A., Ghysels, E. Long- and Short-Term Cryptocurrency Volatility Components: A GARCH-MIDAS Analysis (May 6, 2018). Available at SSRN: https://ssrn.com/abstract=3161264 or http://dx.doi.org/10.2139/ssrn.3161264. 2018Google ScholarGoogle Scholar
  7. Handoko, B.L., Ayuanda, N., Marpaung, A.T. Development of Cryptocurrency in the Indonesian Economy. 2021 7th International Conference on E-Business and Applications (ICEBA 2021), February 24-26, 2021, Singapore, ACM, New York, NY, USA. 2021Google ScholarGoogle Scholar
  8. Yusuf, A. M., Metode penelitian kuantitatif, kualitatif & penelitian gabungan, IDN: Prenada Media, 2016, ch. 2.Google ScholarGoogle Scholar
  9. Chu, J., Chan, S., Nadarajah, S., & Osterrieder, J. (2017). GARCH modelling of cryptocurrencies. Journal of Risk and Financial Management, 10(4), 17.Google ScholarGoogle ScholarCross RefCross Ref
  10. Dyhrberg, A.H. Bitcoin, Gold, and the Dollar – A GARCH Volatility Analysis. Finance Research Letters. Elsevier. 2016Google ScholarGoogle Scholar
  11. Ghozali, H. I., Ratmono, D., Analisis Multivariat dan Ekonometrika Teori, Konsep, dan Aplikasi Dengan EViews 10, 2nd ed. Semarang, IDN: Badan Penerbit Universitas Diponegoro, 2017Google ScholarGoogle Scholar
  12. Engle, R.F. Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica Vol 50 No 4. 1982Google ScholarGoogle Scholar
  13. Bollerslev, T. Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, volume 31 issue 3. 1986Google ScholarGoogle ScholarCross RefCross Ref
  14. Gyamerah, S. A. 2019. Modelling the volatility of Bitcoin returns using GARCH models. Quant Financ Econ, 3, 739-753.Google ScholarGoogle ScholarCross RefCross Ref
  15. Anderson, M. The Future of Bitcoin in a Volatile World. https://crosscut.com/2014/01/future-bitcoin-volatile-world. 2014Google ScholarGoogle Scholar
  16. Khan, S.M., Arshad, A., Mushtag, G., Khalique A., Husein T. Implementation of Decentralized Blockchain E-voting. EAI Endorsed Transaction on Smart Cities. 2020Google ScholarGoogle Scholar

Index Terms

  1. Forecasting Cryptocurrency Volatility Using GARCH and ARCH Model

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Other conferences
      ICEEG '22: Proceedings of the 6th International Conference on E-Commerce, E-Business and E-Government
      April 2022
      439 pages
      ISBN:9781450396523
      DOI:10.1145/3537693

      Copyright © 2022 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 9 July 2022

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Research
      • Refereed limited

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format .

    View HTML Format