IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v558y2020ics0378437120304726.html
   My bibliography  Save this article

Structural breaks and trend awareness-based interaction in crypto markets

Author

Listed:
  • Telli, Şahin
  • Chen, Hongzhuan

Abstract

This study aims to test multiple structural breaks using Bai-Perron methodology for the crypto markets. We analyze return and volatility (proxied by absolute and squared returns) series of Bitcoin and the following crypto assets: Bitcoin Cash, DASH, Ethereum, IOTA, Litecoin, NEO, XRP. In the analysis, we consider the BTC markets of those 7 altcoins as well as the USD markets. Empirical findings indicate existence of statistically significant structural changes in terms of returns and volatility. Return and volatility series share different dynamics. On the other hand, time series quoted in BTC perform different structural change behavior than the ones quoted in USD. We also observed a clustering of breakpoints in the periods of February–March 2017 and December 2017–March 2018. For the logarithmic returns, in the mean of BTCUSD and series quoted in BTC no structural breaks are available. The mean of logarithmic returns of DASH, ETH and LTC quoted in USD have structural changes corresponding to the beginning of the downtrend in USD price series of the relevant assets. Absolute returns revealed more of structural breaks in terms of volatility than squared returns. Except for IOTA, volatility last longer in the series quoted in USD than the ones quoted in BTC. For the volatility series of ETH, LTC and NEO, regimes in the time series quoted in BTC is affected by the trend change in the USD quoted price series of the asset. We have defined this regime change and trend change relationship as “trend awareness-based interaction”. We also found a specific trend change pattern in the time series. In comparison to the time series of altcoins, BTCUSD is the first one which has the trend change almost in all cases. Besides, the time series of BCH, ETH, LTC and NEO (and DASH partly) follows a specific trend change order as the following: first in USD quoted time series of the altcoin and then in BTC quoted ones, while the time series of IOTA and XRP indicate the opposite.

Suggested Citation

  • Telli, Şahin & Chen, Hongzhuan, 2020. "Structural breaks and trend awareness-based interaction in crypto markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 558(C).
  • Handle: RePEc:eee:phsmap:v:558:y:2020:i:c:s0378437120304726
    DOI: 10.1016/j.physa.2020.124913
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437120304726
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/j.physa.2020.124913?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. David Garcia & Claudio Juan Tessone & Pavlin Mavrodiev & Nicolas Perony, 2014. "The digital traces of bubbles: feedback cycles between socio-economic signals in the Bitcoin economy," Papers 1408.1494, arXiv.org.
    2. Tetsuya Takaishi, 2017. "Statistical properties and multifractality of Bitcoin," Papers 1707.07618, arXiv.org, revised May 2018.
    3. Demir, Ender & Gozgor, Giray & Lau, Chi Keung Marco & Vigne, Samuel A., 2018. "Does economic policy uncertainty predict the Bitcoin returns? An empirical investigation," Finance Research Letters, Elsevier, vol. 26(C), pages 145-149.
    4. Granger, Clive W. J. & Hyung, Namwon, 2004. "Occasional structural breaks and long memory with an application to the S&P 500 absolute stock returns," Journal of Empirical Finance, Elsevier, vol. 11(3), pages 399-421, June.
    5. Kejriwal, Mohitosh & Perron, Pierre, 2010. "Testing for Multiple Structural Changes in Cointegrated Regression Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(4), pages 503-522.
    6. Alvarez-Ramirez, J. & Rodriguez, E. & Ibarra-Valdez, C., 2018. "Long-range correlations and asymmetry in the Bitcoin market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 948-955.
    7. Elie Bouri & Mahamitra Das & Rangan Gupta & David Roubaud, 2018. "Spillovers between Bitcoin and other assets during bear and bull markets," Applied Economics, Taylor & Francis Journals, vol. 50(55), pages 5935-5949, November.
    8. da Silva Filho, Antônio Carlos & Maganini, Natália Diniz & de Almeida, Eduardo Fonseca, 2018. "Multifractal analysis of Bitcoin market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 954-967.
    9. Stosic, Darko & Stosic, Dusan & Ludermir, Teresa B. & Stosic, Tatijana, 2018. "Collective behavior of cryptocurrency price changes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 499-509.
    10. Zhongjun Qu & Pierre Perron, 2007. "Estimating and Testing Structural Changes in Multivariate Regressions," Econometrica, Econometric Society, vol. 75(2), pages 459-502, March.
    11. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
    12. Andrews, Donald W K & Ploberger, Werner, 1994. "Optimal Tests When a Nuisance Parameter Is Present Only under the Alternative," Econometrica, Econometric Society, vol. 62(6), pages 1383-1414, November.
    13. David Garcia & Claudio Tessone & Pavlin Mavrodiev & Nicolas Perony, "undated". "The digital traces of bubbles: feedback cycles between socio-economic signals in the Bitcoin economy," Working Papers ETH-RC-14-001, ETH Zurich, Chair of Systems Design.
    14. Yao, Yi-Ching, 1988. "Estimating the number of change-points via Schwarz' criterion," Statistics & Probability Letters, Elsevier, vol. 6(3), pages 181-189, February.
    15. Luther, William J. & Salter, Alexander W., 2017. "Bitcoin and the bailout," The Quarterly Review of Economics and Finance, Elsevier, vol. 66(C), pages 50-56.
    16. Chandan Sharma & Rajat Setia, 2015. "Macroeconomic fundamentals and dynamics of the Indian rupee-dollar exchange rate," Journal of Financial Economic Policy, Emerald Group Publishing Limited, vol. 7(4), pages 301-326, November.
    17. Kristoufek, Ladislav, 2018. "On Bitcoin markets (in)efficiency and its evolution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 257-262.
    18. Takaishi, Tetsuya, 2018. "Statistical properties and multifractality of Bitcoin," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 507-519.
    19. Stanis{l}aw Dro.zd.z & Robert Gk{e}barowski & Ludovico Minati & Pawe{l} O'swik{e}cimka & Marcin Wk{a}torek, 2018. "Bitcoin market route to maturity? Evidence from return fluctuations, temporal correlations and multiscaling effects," Papers 1804.05916, arXiv.org, revised Jul 2018.
    20. Elie Bouri & Luis A. Gil‐Alana & Rangan Gupta & David Roubaud, 2019. "Modelling long memory volatility in the Bitcoin market: Evidence of persistence and structural breaks," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 24(1), pages 412-426, January.
    21. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
    22. Bouri, Elie & Molnár, Peter & Azzi, Georges & Roubaud, David & Hagfors, Lars Ivar, 2017. "On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier?," Finance Research Letters, Elsevier, vol. 20(C), pages 192-198.
    23. Hyejin Song & Shin, 2015. "Long-memories and mean breaks in realized volatilities," Applied Economics Letters, Taylor & Francis Journals, vol. 22(16), pages 1273-1280, November.
    24. Osamah Al-Khazali & Elie Bouri & David Roubaud, 2018. "The impact of positive and negative macroeconomic news surprises: Gold versus Bitcoin," Economics Bulletin, AccessEcon, vol. 38(1), pages 373-382.
    25. 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.
    26. Ji, Qiang & Bouri, Elie & Gupta, Rangan & Roubaud, David, 2018. "Network causality structures among Bitcoin and other financial assets: A directed acyclic graph approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 70(C), pages 203-213.
    27. 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.
    28. Corbae,Dean & Durlauf,Steven N. & Hansen,Bruce E. (ed.), 2006. "Econometric Theory and Practice," Cambridge Books, Cambridge University Press, number 9780521807234.
    29. Su, Chi-Wei & Li, Zheng-Zheng & Tao, Ran & Si, Deng-Kui, 2018. "Testing for multiple bubbles in bitcoin markets: A generalized sup ADF test," Japan and the World Economy, Elsevier, vol. 46(C), pages 56-63.
    30. Jushan Bai & Pierre Perron, 2003. "Critical values for multiple structural change tests," Econometrics Journal, Royal Economic Society, vol. 6(1), pages 72-78, June.
    31. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-856, July.
    32. Aaron Yelowitz & Matthew Wilson, 2015. "Characteristics of Bitcoin users: an analysis of Google search data," Applied Economics Letters, Taylor & Francis Journals, vol. 22(13), pages 1030-1036, September.
    33. Dastgir, Shabbir & Demir, Ender & Downing, Gareth & Gozgor, Giray & Lau, Chi Keung Marco, 2019. "The causal relationship between Bitcoin attention and Bitcoin returns: Evidence from the Copula-based Granger causality test," Finance Research Letters, Elsevier, vol. 28(C), pages 160-164.
    34. Dyhrberg, Anne Haubo, 2016. "Bitcoin, gold and the dollar – A GARCH volatility analysis," Finance Research Letters, Elsevier, vol. 16(C), pages 85-92.
    35. Mensi, Walid & Lee, Yun-Jung & Al-Yahyaee, Khamis Hamed & Sensoy, Ahmet & Yoon, Seong-Min, 2019. "Intraday downward/upward multifractality and long memory in Bitcoin and Ethereum markets: An asymmetric multifractal detrended fluctuation analysis," Finance Research Letters, Elsevier, vol. 31(C), pages 19-25.
    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. Ke, Rui & Yang, Luyao & Tan, Changchun, 2022. "Forecasting tail risk for Bitcoin: A dynamic peak over threshold approach," Finance Research Letters, Elsevier, vol. 49(C).
    2. Fung, Kennard & Jeong, Jiin & Pereira, Javier, 2022. "More to cryptos than bitcoin: A GARCH modelling of heterogeneous cryptocurrencies," Finance Research Letters, Elsevier, vol. 47(PA).
    3. James, Nick & Menzies, Max & Chan, Jennifer, 2021. "Changes to the extreme and erratic behaviour of cryptocurrencies during COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    4. Nick James, 2021. "Dynamics, behaviours, and anomaly persistence in cryptocurrencies and equities surrounding COVID-19," Papers 2101.00576, arXiv.org, revised Feb 2021.
    5. Nick James & Max Menzies, 2021. "Collective correlations, dynamics, and behavioural inconsistencies of the cryptocurrency market over time," Papers 2107.13926, arXiv.org, revised Dec 2021.
    6. James, Nick, 2021. "Dynamics, behaviours, and anomaly persistence in cryptocurrencies and equities surrounding COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(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. Telli, Şahin & Chen, Hongzhuan, 2020. "Multifractal behavior in return and volatility series of Bitcoin and gold in comparison," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    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. Telli, Şahin & Chen, Hongzhuan, 2021. "Multifractal behavior relationship between crypto markets and Wikipedia-Reddit online platforms," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    4. 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.
    5. Panagiotidis, Theodore & Stengos, Thanasis & Vravosinos, Orestis, 2019. "The effects of markets, uncertainty and search intensity on bitcoin returns," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 220-242.
    6. Kristjanpoller, Werner & Bouri, Elie & Takaishi, Tetsuya, 2020. "Cryptocurrencies and equity funds: Evidence from an asymmetric multifractal analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    7. Alessandro Casini & Pierre Perron, 2018. "Structural Breaks in Time Series," Boston University - Department of Economics - Working Papers Series WP2019-02, Boston University - Department of Economics.
    8. Alaoui, Marwane El & Bouri, Elie & Roubaud, David, 2019. "Bitcoin price–volume: A multifractal cross-correlation approach," Finance Research Letters, Elsevier, vol. 31(C).
    9. Kakinaka, Shinji & Umeno, Ken, 2022. "Asymmetric volatility dynamics in cryptocurrency markets on multi-time scales," Research in International Business and Finance, Elsevier, vol. 62(C).
    10. Pho, Kim Hung & Ly, Sel & Lu, Richard & Hoang, Thi Hong Van & Wong, Wing-Keung, 2021. "Is Bitcoin a better portfolio diversifier than gold? A copula and sectoral analysis for China," International Review of Financial Analysis, Elsevier, vol. 74(C).
    11. Kakinaka, Shinji & Umeno, Ken, 2021. "Exploring asymmetric multifractal cross-correlations of price–volatility and asymmetric volatility dynamics in cryptocurrency markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    12. Mohitosh Kejriwal, 2020. "A Robust Sequential Procedure for Estimating the Number of Structural Changes in Persistence," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(3), pages 669-685, June.
    13. Stefano Martinazzi & Daniele Regoli & Andrea Flori, 2020. "A Tale of Two Layers: The Mutual Relationship between Bitcoin and Lightning Network," Risks, MDPI, vol. 8(4), pages 1-18, December.
    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. Khalid Khan & Jiluo Sun & Sinem Derindere Koseoglu & Ashfaq U. Rehman, 2021. "Revisiting Bitcoin Price Behavior Under Global Economic Uncertainty," SAGE Open, , vol. 11(3), pages 21582440211, August.
    16. Garcia-Jorcano, Laura & Benito, Sonia, 2020. "Studying the properties of the Bitcoin as a diversifying and hedging asset through a copula analysis: Constant and time-varying," Research in International Business and Finance, Elsevier, vol. 54(C).
    17. Parthajit Kayal & G. Balasubramanian, 2021. "Excess Volatility in Bitcoin: Extreme Value Volatility Estimation," IIM Kozhikode Society & Management Review, , vol. 10(2), pages 222-231, July.
    18. 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.
    19. Isah, Kazeem O. & Raheem, Ibrahim D., 2019. "The hidden predictive power of cryptocurrencies and QE: Evidence from US stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    20. Haffar, Adlane & Le Fur, Eric, 2021. "Structural vector error correction modelling of Bitcoin price," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 170-178.

    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:phsmap:v:558:y:2020:i:c:s0378437120304726. 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.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.