IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v47y2022ipbs1544612322001155.html
   My bibliography  Save this article

Nonlinear dynamics analysis of cryptocurrency price fluctuations based on Bitcoin

Author

Listed:
  • Tong, Zhongwen
  • Chen, Zhanbo
  • Zhu, Chen

Abstract

The price fluctuation of cryptocurrencies represented by Bitcoin has nonlinear structure characteristics. We select the Bitcoin closing price data from 2013 to 2021, and use GARCH (1,1)-GED to fit the volatility series. We confirm that Bitcoin price Fluctuation has nonlinear dynamics through BDS test, Hurst exponent, correlation dimension test and Lyapunov exponent. We find that the price fluctuation of cryptocurrency does not obey the random walk, and its fluctuation is positively correlated with time. Bullish information and bearish information have basically the same impact on cryptocurrency fluctuations. Cryptocurrency price fluctuations have cyclical trends and inherent long-term unpredictability, as well as certain fractal and chaos characteristics. ARCH effect and long memory characteristics of cryptocurrency return series show that cryptocurrency price fluctuations are Clustering and persistence. These two characteristics constitute the nonlinear dynamic mechanism of Bitcoin price fluctuation. Overall, our study has important implications for investors and regulators within cryptocurrency markets.

Suggested Citation

  • Tong, Zhongwen & Chen, Zhanbo & Zhu, Chen, 2022. "Nonlinear dynamics analysis of cryptocurrency price fluctuations based on Bitcoin," Finance Research Letters, Elsevier, vol. 47(PB).
    • Handle: RePEc:eee:finlet:v:47:y:2022:i:pb:s1544612322001155
    DOI: 10.1016/j.frl.2022.102803
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612322001155
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2022.102803?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. Yang, Liansheng & Zhu, Yingming & Wang, Yudong, 2016. "Multifractal characterization of energy stocks in China: A multifractal detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 357-365.
    2. Urquhart, Andrew, 2016. "The inefficiency of Bitcoin," Economics Letters, Elsevier, vol. 148(C), pages 80-82.
    3. Ben Khelifa, Soumaya & Guesmi, Khaled & Urom, Christian, 2021. "Exploring the relationship between cryptocurrencies and hedge funds during COVID-19 crisis," International Review of Financial Analysis, Elsevier, vol. 76(C).
    4. Lin, Xiaoqiang & Fei, Fangyu, 2013. "Long memory revisit in Chinese stock markets: Based on GARCH-class models and multiscale analysis," Economic Modelling, Elsevier, vol. 31(C), pages 265-275.
    5. Luo, Wenya & Bai, Zhidong & Zheng, Shurong & Hui, Yongchang, 2020. "A modified BDS test," Statistics & Probability Letters, Elsevier, vol. 164(C).
    6. Naeem, Muhammad Abubakr & Bouri, Elie & Peng, Zhe & Shahzad, Syed Jawad Hussain & Vo, Xuan Vinh, 2021. "Asymmetric efficiency of cryptocurrencies during COVID19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    7. Dyhrberg, Anne Haubo, 2016. "Hedging capabilities of bitcoin. Is it the virtual gold?," Finance Research Letters, Elsevier, vol. 16(C), pages 139-144.
    8. Cheah, Eng-Tuck & Fry, John, 2015. "Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin," Economics Letters, Elsevier, vol. 130(C), pages 32-36.
    9. Zhao, Zhen-yu & Zhu, Jiang & Xia, Bo, 2016. "Multi-fractal fluctuation features of thermal power coal price in China," Energy, Elsevier, vol. 117(P1), pages 10-18.
    10. Corbet, Shaen & Meegan, Andrew & Larkin, Charles & Lucey, Brian & Yarovaya, Larisa, 2018. "Exploring the dynamic relationships between cryptocurrencies and other financial assets," Economics Letters, Elsevier, vol. 165(C), pages 28-34.
    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. Zhao, Junming & Zhang, Tianding, 2023. "Exploring the time-varying dependence between Bitcoin and the global stock market: Evidence from a TVP-VAR approach," Finance Research Letters, Elsevier, vol. 58(PA).
    2. Kundan Mukhia & Anish Rai & SR Luwang & Md Nurujjaman & Sushovan Majhi & Chittaranjan Hens, 2024. "Complex network analysis of cryptocurrency market during crashes," Papers 2405.05642, arXiv.org.
    3. Kilic, Emre & Yavuz, Ersin & Pazarci, Sevket & Kar, Asim, 2023. "Analyzing the efficient market hypothesis with asymmetric persistence in cryptocurrencies: Insights from the Fourier non-linear quantile unit root approach," Finance Research Letters, Elsevier, vol. 58(PC).
    4. Wu, Xiangling & Ding, Shusheng, 2023. "The impact of the Bitcoin price on carbon neutrality: Evidence from futures markets," Finance Research Letters, Elsevier, vol. 56(C).
    5. Olanipekun, Ifedolapo Olabisi & Ozkan, Oktay & Olasehinde-Williams, Godwin, 2023. "Is renewable energy use lowering resource-related uncertainties?," Energy, Elsevier, vol. 271(C).

    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. Kajtazi, Anton & Moro, Andrea, 2019. "The role of bitcoin in well diversified portfolios: A comparative global study," International Review of Financial Analysis, Elsevier, vol. 61(C), pages 143-157.
    2. Flori, Andrea, 2019. "News and subjective beliefs: A Bayesian approach to Bitcoin investments," Research in International Business and Finance, Elsevier, vol. 50(C), pages 336-356.
    3. Kurka, Josef, 2019. "Do cryptocurrencies and traditional asset classes influence each other?," Finance Research Letters, Elsevier, vol. 31(C), pages 38-46.
    4. Urquhart, Andrew & Zhang, Hanxiong, 2019. "Is Bitcoin a hedge or safe haven for currencies? An intraday analysis," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 49-57.
    5. Zhou, Siwen, 2018. "Exploring the Driving Forces of the Bitcoin Exchange Rate Dynamics: An EGARCH Approach," MPRA Paper 89445, University Library of Munich, Germany.
    6. Achraf Ghorbel & Wajdi Frikha & Yasmine Snene Manzli, 2022. "Testing for asymmetric non-linear short- and long-run relationships between crypto-currencies and stock markets," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 12(3), pages 387-425, September.
    7. Theodore Pelagidis & Eleftheria Kostika, 2022. "Investigating the role of central banks in the interconnection between financial markets and cryptoassets," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 49(3), pages 481-507, September.
    8. Mensi, Walid & Sensoy, Ahmet & Aslan, Aylin & Kang, Sang Hoon, 2019. "High-frequency asymmetric volatility connectedness between Bitcoin and major precious metals markets," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    9. Ahmed H. Elsayed & Giray Gozgor & Chi Keung Marco Lau, 2022. "Causality and dynamic spillovers among cryptocurrencies and currency markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 2026-2040, April.
    10. Merediz-Solà, Ignasi & Bariviera, Aurelio F., 2019. "A bibliometric analysis of bitcoin scientific production," Research in International Business and Finance, Elsevier, vol. 50(C), pages 294-305.
    11. Corbet, Shaen & Lucey, Brian & Urquhart, Andrew & Yarovaya, Larisa, 2019. "Cryptocurrencies as a financial asset: A systematic analysis," International Review of Financial Analysis, Elsevier, vol. 62(C), pages 182-199.
    12. Wajdi Moussa & Nidhal Mgadmi & Rym Regaïeg & Abdelhafidh Othmani, 2020. "Non-linear adjustment of the Bitcoin–US dollar exchange rate," Digital Finance, Springer, vol. 2(1), pages 143-158, September.
    13. Vidal-Tomás, David, 2021. "The entry and exit dynamics of the cryptocurrency market," Research in International Business and Finance, Elsevier, vol. 58(C).
    14. Wang, Pengfei & Zhang, Wei & Li, Xiao & Shen, Dehua, 2019. "Is cryptocurrency a hedge or a safe haven for international indices? A comprehensive and dynamic perspective," Finance Research Letters, Elsevier, vol. 31(C), pages 1-18.
    15. Katsiampa, Paraskevi, 2019. "Volatility co-movement between Bitcoin and Ether," Finance Research Letters, Elsevier, vol. 30(C), pages 221-227.
    16. Eross, Andrea & McGroarty, Frank & Urquhart, Andrew & Wolfe, Simon, 2019. "The intraday dynamics of bitcoin," Research in International Business and Finance, Elsevier, vol. 49(C), pages 71-81.
    17. Vidal-Tomás, David & Ibañez, Ana, 2018. "Semi-strong efficiency of Bitcoin," Finance Research Letters, Elsevier, vol. 27(C), pages 259-265.
    18. Aurelio F. Bariviera & Ignasi Merediz‐Solà, 2021. "Where Do We Stand In Cryptocurrencies Economic Research? A Survey Based On Hybrid Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 377-407, April.
    19. Duc Huynh, Toan Luu & Burggraf, Tobias & Wang, Mei, 2020. "Gold, platinum, and expected Bitcoin returns," Journal of Multinational Financial Management, Elsevier, vol. 56(C).
    20. Antonakakis, Nikolaos & Chatziantoniou, Ioannis & Gabauer, David, 2019. "Cryptocurrency market contagion: Market uncertainty, market complexity, and dynamic portfolios," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 61(C), pages 37-51.

    More about this item

    Keywords

    Cryptocurrency; Nonlinear dynamics analysis; BDS test; Long memory; Price fluctuations;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

    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:eee:finlet:v:47:y:2022:i:pb:s1544612322001155. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    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.