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What Coins Lead in the Cryptocurrency Market: Using Copula and Neural Networks Models

Author

Listed:
  • Steve Hyun

    (Division of Mathematics and Computer Science, University of South Carolina Upstate, Spartanburg, SC 29303, USA)

  • Jimin Lee

    (Department of Mathematics, University of North Carolina Asheville, Asheville, NC 28804, USA)

  • Jong-Min Kim

    (Statistics Discipline, Division of Science and Mathematics, University of Minnesota-Morris, Morris, MN 56267, USA)

  • Chulhee Jun

    (Department of Finance, Ziegler College of Business, Bloomsburg University, Bloomsburg, PA 17815, USA)

Abstract

Exploring dependence structures between financial time series has been important within a wide range of applications. The main aim of this paper is to examine dependence relationships among five well-known cryptocurrencies—Bitcoin, Ethereum, Litecoin, Ripple, and Stella—by a copula directional dependence (CDD). By employing a neural network autoregression model to avoid the serial dependence in each individual cryptocurrency, we generate residuals of the fitted models with time series of daily log-returns in percentage of the five cryptocurrencies and then we apply a Gaussian copula marginal beta regression model to the residuals to explore the CDD. The results show that the CDD from Bitcoin to Litecoin is highest among all ordered directional dependencies and the CDDs from Ethereum to the other four cryptocurrencies are relatively higher than the CDDs to Ethereum from those cryptocurrencies. This finding implies that the return shocks of Bitcoin have the most effect on Litecoin and the return shocks of Ethereum relatively influence the shocks on the other four cryptocurrencies instead of being affected by them. This allows investors to build the market-timing strategies by observing the directional flow of return shocks among cryptocurrencies.

Suggested Citation

  • Steve Hyun & Jimin Lee & Jong-Min Kim & Chulhee Jun, 2019. "What Coins Lead in the Cryptocurrency Market: Using Copula and Neural Networks Models," JRFM, MDPI, vol. 12(3), pages 1-14, August.
    • Handle: RePEc:gam:jjrfmx:v:12:y:2019:i:3:p:132-:d:255984
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    References listed on IDEAS

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    Cited by:

    1. Νikolaos A. Kyriazis & Paraskevi Prassa, 2019. "Which Cryptocurrencies Are Mostly Traded in Distressed Times?," JRFM, MDPI, vol. 12(3), pages 1-12, August.
    2. Jong-Min Kim & Seong-Tae Kim & Sangjin Kim, 2020. "On the Relationship of Cryptocurrency Price with US Stock and Gold Price Using Copula Models," Mathematics, MDPI, vol. 8(11), pages 1-15, October.
    3. Wang, Jinghua & Ngene, Geoffrey M., 2020. "Does Bitcoin still own the dominant power? An intraday analysis," International Review of Financial Analysis, Elsevier, vol. 71(C).
    4. Ha, Le Thanh & Nham, Nguyen Thi Hong, 2022. "An application of a TVP-VAR extended joint connected approach to explore connectedness between WTI crude oil, gold, stock and cryptocurrencies during the COVID-19 health crisis," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    5. Mensi, Walid & El Khoury, Rim & Ali, Syed Riaz Mahmood & Vo, Xuan Vinh & Kang, Sang Hoon, 2023. "Quantile dependencies and connectedness between the gold and cryptocurrency markets: Effects of the COVID-19 crisis," Research in International Business and Finance, Elsevier, vol. 65(C).
    6. Jong-Min Kim & Chulhee Jun & Junyoup Lee, 2021. "Forecasting the Volatility of the Cryptocurrency Market by GARCH and Stochastic Volatility," Mathematics, MDPI, vol. 9(14), pages 1-16, July.
    7. Chika Anastesia Anisiuba & Obiamaka P. Egbo & Felix C. Alio & Chuka Ifediora & Ebele C. Igwemeka & C. O. Odidi & Hillary Chijindu Ezeaku, 2021. "Analysis of Cryptocurrency Dynamics in the Emerging Market Economies: Does Reinforcement or Substitution Effect Prevail?," SAGE Open, , vol. 11(1), pages 21582440211, March.
    8. Helder Sebastião & Pedro Godinho, 2021. "Forecasting and trading cryptocurrencies with machine learning under changing market conditions," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-30, December.

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