IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v513y2019icp598-612.html
   My bibliography  Save this article

Stylised facts for high frequency cryptocurrency data

Author

Listed:
  • Zhang, Yuanyuan
  • Chan, Stephen
  • Chu, Jeffrey
  • Nadarajah, Saralees

Abstract

The term ‘stylised facts’ has been extensively researched through the analysis of many different financial datasets. More recently, cryptocurrencies have been investigated as a new type of financial asset, and provide an interesting example, with a current market value of over $500 billion. Here, we analyse the stylised facts in terms of the Hurst exponent, using both the DFA and R/S methods, of the four most popular cryptocurrencies ranked according to their market capitalisation. The analysis is conducted on high frequency returns data with varying lags. In addition to using the Hurst exponent, our analysis also considers features of dependence between the different cryptocurrencies.

Suggested Citation

  • Zhang, Yuanyuan & Chan, Stephen & Chu, Jeffrey & Nadarajah, Saralees, 2019. "Stylised facts for high frequency cryptocurrency data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 598-612.
    • Handle: RePEc:eee:phsmap:v:513:y:2019:i:c:p:598-612
    DOI: 10.1016/j.physa.2018.09.042
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437118311567
    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.2018.09.042?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. W. Breymann & A. Dias & P. Embrechts, 2003. "Dependence structures for multivariate high-frequency data in finance," Quantitative Finance, Taylor & Francis Journals, vol. 3(1), pages 1-14.
    2. Richard T. Baillie & Young‐Wook Han & Robert J. Myers & Jeongseok Song, 2007. "Long memory models for daily and high frequency commodity futures returns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 27(7), pages 643-668, July.
    3. Garcia, René & Tsafack, Georges, 2011. "Dependence structure and extreme comovements in international equity and bond markets," Journal of Banking & Finance, Elsevier, vol. 35(8), pages 1954-1970, August.
    4. Richard T. Baillie & Young-Wook Han & Robert J. Myers & Jeongseok Song, 2007. "Long Memory and FIGARCH Models for Daily and High Frequency Commodity Prices," Working Papers 594, Queen Mary University of London, School of Economics and Finance.
    5. Serinaldi, Francesco, 2010. "Use and misuse of some Hurst parameter estimators applied to stationary and non-stationary financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(14), pages 2770-2781.
    6. Schmid, Friedrich & Schmidt, Rafael, 2007. "Multivariate conditional versions of Spearman's rho and related measures of tail dependence," Journal of Multivariate Analysis, Elsevier, vol. 98(6), pages 1123-1140, July.
    7. Stjepan Beguv{s}i'c & Zvonko Kostanjv{c}ar & H. Eugene Stanley & Boris Podobnik, 2018. "Scaling properties of extreme price fluctuations in Bitcoin markets," Papers 1803.08405, arXiv.org.
    8. Richard T. Baillie & Young-Wook Han & Robert J. Myers & Jeongseok Song, 2007. "Long Memory and FIGARCH Models for Daily and High Frequency Commodity Prices," Working Papers 594, Queen Mary University of London, School of Economics and Finance.
    9. 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.
    10. Kotkatvuori-Örnberg, Juha & Nikkinen, Jussi & Äijö, Janne, 2013. "Stock market correlations during the financial crisis of 2008–2009: Evidence from 50 equity markets," International Review of Financial Analysis, Elsevier, vol. 28(C), pages 70-78.
    11. Stephen Chan & Jeffrey Chu & Saralees Nadarajah & Joerg Osterrieder, 2017. "A Statistical Analysis of Cryptocurrencies," JRFM, MDPI, vol. 10(2), pages 1-23, May.
    12. Cajueiro, Daniel O & Tabak, Benjamin M, 2004. "The Hurst exponent over time: testing the assertion that emerging markets are becoming more efficient," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 336(3), pages 521-537.
    13. William J. Luther, 2016. "Cryptocurrencies, Network Effects, And Switching Costs," Contemporary Economic Policy, Western Economic Association International, vol. 34(3), pages 553-571, July.
    14. Katsiampa, Paraskevi, 2017. "Volatility estimation for Bitcoin: A comparison of GARCH models," Economics Letters, Elsevier, vol. 158(C), pages 3-6.
    15. Dyhrberg, Anne Haubo, 2016. "Bitcoin, gold and the dollar – A GARCH volatility analysis," Finance Research Letters, Elsevier, vol. 16(C), pages 85-92.
    16. Lothian, James R., 1998. "Some new stylized facts of floating exchange rates," Journal of International Money and Finance, Elsevier, vol. 17(1), pages 29-39, February.
    17. Grau-Carles, Pilar, 2000. "Empirical evidence of long-range correlations in stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 287(3), pages 396-404.
    18. Jeffrey Chu & Saralees Nadarajah & Stephen Chan, 2015. "Statistical Analysis of the Exchange Rate of Bitcoin," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-27, July.
    19. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    20. Jamal Bouoiyour & Refk Selmi, 2016. "Bitcoin: a beginning of a new phase?," Economics Bulletin, AccessEcon, vol. 36(3), pages 1430-1440.
    21. Begušić, Stjepan & Kostanjčar, Zvonko & Eugene Stanley, H. & Podobnik, Boris, 2018. "Scaling properties of extreme price fluctuations in Bitcoin markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 400-406.
    22. Fry, John & Cheah, Eng-Tuck, 2016. "Negative bubbles and shocks in cryptocurrency markets," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 343-352.
    23. Ling Hu, 2006. "Dependence patterns across financial markets: a mixed copula approach," Applied Financial Economics, Taylor & Francis Journals, vol. 16(10), pages 717-729.
    24. Huber, Jürgen & Kleinlercher, Daniel & Kirchler, Michael, 2012. "The impact of a financial transaction tax on stylized facts of price returns—Evidence from the lab," Journal of Economic Dynamics and Control, Elsevier, vol. 36(8), pages 1248-1266.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. T. Takaishi, 2021. "Power-Law Return-Volatility Cross Correlations of Bitcoin," Papers 2102.08187, arXiv.org.
    3. 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.
    4. da Cunha, C.R. & da Silva, R., 2020. "Relevant stylized facts about bitcoin: Fluctuations, first return probability, and natural phenomena," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
    5. Celeste, Valerio & Corbet, Shaen & Gurdgiev, Constantin, 2020. "Fractal dynamics and wavelet analysis: Deep volatility and return properties of Bitcoin, Ethereum and Ripple," The Quarterly Review of Economics and Finance, Elsevier, vol. 76(C), pages 310-324.
    6. Tetsuya Takaishi, 2021. "Time-varying properties of asymmetric volatility and multifractality in Bitcoin," Papers 2102.07425, arXiv.org.
    7. Li, Mu-Yao & Cai, Qing & Gu, Gao-Feng & Zhou, Wei-Xing, 2019. "Exponentially decayed double power-law distribution of Bitcoin trade sizes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    8. Kumar, Anoop S. & Anandarao, S., 2019. "Volatility spillover in crypto-currency markets: Some evidences from GARCH and wavelet analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 448-458.
    9. Stosic, Darko & Stosic, Dusan & Ludermir, Teresa B. & Stosic, Tatijana, 2019. "Exploring disorder and complexity in the cryptocurrency space," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 548-556.
    10. Eom, Cheoljun & Kaizoji, Taisei & Kang, Sang Hoon & Pichl, Lukas, 2019. "Bitcoin and investor sentiment: Statistical characteristics and predictability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 511-521.
    11. 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.
    12. Takaishi, Tetsuya & Adachi, Takanori, 2018. "Taylor effect in Bitcoin time series," Economics Letters, Elsevier, vol. 172(C), pages 5-7.
    13. 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).
    14. Leandro Maciel, 2021. "Cryptocurrencies value‐at‐risk and expected shortfall: Do regime‐switching volatility models improve forecasting?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 4840-4855, July.
    15. Anoop S Kumar & Taufeeq Ajaz, 2019. "Co-movement in crypto-currency markets: evidences from wavelet analysis," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-17, December.
    16. Arthur A. B. Pessa & Matjaz Perc & Haroldo V. Ribeiro, 2023. "Age and market capitalization drive large price variations of cryptocurrencies," Papers 2302.12319, arXiv.org.
    17. Yi, Eojin & Ahn, Kwangwon & Choi, M.Y., 2022. "Cryptocurrency: Not far from equilibrium," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    18. Stosic, Darko & Stosic, Dusan & Ludermir, Teresa B. & Stosic, Tatijana, 2019. "Multifractal behavior of price and volume changes in the cryptocurrency market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 54-61.
    19. 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).
    20. Yang (Greg) Hou & Mark Holmes, 2020. "Do higher order moments of return distribution provide better decisions in minimum-variance hedging? Evidence from US stock index futures," Australian Journal of Management, Australian School of Business, vol. 45(2), pages 240-265, May.

    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:513:y:2019:i:c:p:598-612. 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.