IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v514y2019icp511-521.html
   My bibliography  Save this article

Bitcoin and investor sentiment: Statistical characteristics and predictability

Author

Listed:
  • Eom, Cheoljun
  • Kaizoji, Taisei
  • Kang, Sang Hoon
  • Pichl, Lukas

Abstract

This study empirically investigates the statistical characteristics and predictability of Bitcoin return and volatility. The distribution of Bitcoin returns and volatility display a fat right tail and high central parts. Bitcoin does not show the dynamic property of volatility persistence, contrary to stylized facts in financial time series. Also, the autoregressive model using past volatility does not well work in predicting changes in Bitcoin volatility for future periods. Investor sentiment regarding Bitcoin has a significant information value for explaining changes in Bitcoin volatility for future periods. These results suggest that Bitcoin appears to be an investment asset with high volatility and dependence on investor sentiment rather than a monetary asset.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:514:y:2019:i:c:p:511-521
    DOI: 10.1016/j.physa.2018.09.063
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437118311956
    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.2018.09.063?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. Ole E. Barndorff-Nielsen & Neil Shephard, 2006. "Econometrics of Testing for Jumps in Financial Economics Using Bipower Variation," The Journal of Financial Econometrics, Society for Financial Econometrics, vol. 4(1), pages 1-30.
    2. Tetsuya Takaishi, 2017. "Statistical properties and multifractality of Bitcoin," Papers 1707.07618, arXiv.org, revised May 2018.
    3. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2007. "Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 701-720, November.
    4. Malcolm Baker & Jeffrey Wurgler, 2006. "Investor Sentiment and the Cross‐Section of Stock Returns," Journal of Finance, American Finance Association, vol. 61(4), pages 1645-1680, August.
    5. 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.
    6. Vozlyublennaia, Nadia, 2014. "Investor attention, index performance, and return predictability," Journal of Banking & Finance, Elsevier, vol. 41(C), pages 17-35.
    7. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March.
    8. Andersen, Torben G. & Bollerslev, Tim & Diebold, Francis X. & Ebens, Heiko, 2001. "The distribution of realized stock return volatility," Journal of Financial Economics, Elsevier, vol. 61(1), pages 43-76, July.
    9. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    10. Takaishi, Tetsuya, 2018. "Statistical properties and multifractality of Bitcoin," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 507-519.
    11. 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.
    12. 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.
    13. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    14. French, Kenneth R. & Schwert, G. William & Stambaugh, Robert F., 1987. "Expected stock returns and volatility," Journal of Financial Economics, Elsevier, vol. 19(1), pages 3-29, September.
    15. 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.
    16. Ole E. Barndorff-Nielsen, 2004. "Power and Bipower Variation with Stochastic Volatility and Jumps," Journal of Financial Econometrics, Oxford University Press, vol. 2(1), pages 1-37.
    17. Alvarez-Ramirez, J. & Rodriguez, E., 2018. "AR(p)-based detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 49-57.
    18. Dyhrberg, Anne Haubo, 2016. "Bitcoin, gold and the dollar – A GARCH volatility analysis," Finance Research Letters, Elsevier, vol. 16(C), pages 85-92.
    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. 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.
    21. Dyhrberg, Anne Haubo, 2016. "Hedging capabilities of bitcoin. Is it the virtual gold?," Finance Research Letters, Elsevier, vol. 16(C), pages 139-144.
    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. T. Takaishi, 2021. "Power-Law Return-Volatility Cross Correlations of Bitcoin," Papers 2102.08187, arXiv.org.
    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. Tetsuya Takaishi, 2021. "Time-varying properties of asymmetric volatility and multifractality in Bitcoin," Papers 2102.07425, arXiv.org.
    4. 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).
    5. Fantazzini, Dean & Shangina, Tamara, 2019. "The importance of being informed: forecasting market risk measures for the Russian RTS index future using online data and implied volatility over two decades," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 55, pages 5-31.
    6. 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).
    7. Francesco Audrino & Yujia Hu, 2016. "Volatility Forecasting: Downside Risk, Jumps and Leverage Effect," Econometrics, MDPI, vol. 4(1), pages 1-24, February.
    8. Takaishi, Tetsuya & Adachi, Takanori, 2018. "Taylor effect in Bitcoin time series," Economics Letters, Elsevier, vol. 172(C), pages 5-7.
    9. Ahmed, Walid M.A., 2020. "Is there a risk-return trade-off in cryptocurrency markets? The case of Bitcoin," Journal of Economics and Business, Elsevier, vol. 108(C).
    10. 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).
    11. 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).
    12. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2017. "The contribution of jumps to forecasting the density of returns," Post-Print halshs-01442618, HAL.
    13. Konstantinos Gkillas & Dimitrios Vortelinos & Christos Floros & Alexandros Garefalakis & Nikolaos Sariannidis, 2020. "Greek sovereign crisis and European exchange rates: effects of news releases and their providers," Annals of Operations Research, Springer, vol. 294(1), pages 515-536, November.
    14. Chaboud, Alain P. & Chiquoine, Benjamin & Hjalmarsson, Erik & Loretan, Mico, 2010. "Frequency of observation and the estimation of integrated volatility in deep and liquid financial markets," Journal of Empirical Finance, Elsevier, vol. 17(2), pages 212-240, March.
    15. Bollerslev, Tim & Gibson, Michael & Zhou, Hao, 2011. "Dynamic estimation of volatility risk premia and investor risk aversion from option-implied and realized volatilities," Journal of Econometrics, Elsevier, vol. 160(1), pages 235-245, January.
    16. Busch, Thomas & Christensen, Bent Jesper & Nielsen, Morten Ørregaard, 2011. "The role of implied volatility in forecasting future realized volatility and jumps in foreign exchange, stock, and bond markets," Journal of Econometrics, Elsevier, vol. 160(1), pages 48-57, January.
    17. Sévi, Benoît, 2014. "Forecasting the volatility of crude oil futures using intraday data," European Journal of Operational Research, Elsevier, vol. 235(3), pages 643-659.
    18. 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.
    19. 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).
    20. Tetsuya Takaishi & Takanori Adachi, 2020. "Market Efficiency, Liquidity, and Multifractality of Bitcoin: A Dynamic Study," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 27(1), pages 145-154, March.

    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:514:y:2019:i:c:p:511-521. 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.