On the Intraday Behavior of Bitcoin
We analyze the intraday time series of Bitcoin, comparing its features with those of traditional financial assets such as stocks and exchange rates. The results shed light on similarities as well as significant deviations from the standard patterns. In particular, our most interesting finding is the unusual presence of significant negative first-order autocorrelation of returns calculated on medium-frequency timeframes, such as one, two and four hours, signaling the presence of systematic mean reversion. It is also found that larger price movements lead to stronger reversals, in percentage terms. We finally point out the potential exploitability of the phenomenon by implementing a basic algorithmic trading strategy and retroactively applying it to the data. We explain the findings mainly through (i) investor and trader overreaction, (ii) excess volatility and (iii) cascading liquidations due to excessive use of leverage by market participants.
Aslan, A., Sensoy, A. “Intraday Efficiency-Frequency Nexus in the Cryptocurrency Markets.” Finance Research Letters 35 101298 (2020) https://doi.org/10.1016/j.frl.2019.09.013.
Balcilar, M., Bouri, E., Gupta, R., Roubaud, D. “Can Volume Predict Bitcoin Returns and Volatility? A Quantile-Based Approach.” Economic Modelling 64 74–81 (2017) https://doi.org/10.1016/j.econmod.2017.03.019.
Bariviera, A. F. “The Inefficiency of Bitcoin Revisited: A Dynamic Approach.” Economic Letters 161 1–4 (2017) https://doi.org/10.1016/j.econlet.2017.09.013.
Bariviera, A. F., Basgall, M. J., Hasperu´e, W., Naiouf, M. “Some Stylized Facts of the Bitcoin Market.” Physica A: Statistical Mechanics and Its Applications 484 82–90 (2017) https://doi.org/10.1016/j.physa.2017.04.159.
Baur, D. G., Cahill, D., Godfrey, K., Liu, Z. F. “Bitcoin Time-of-Day, Day-of-Week and Month-of-Year Effects in Returns and Trading Volume.” Finance Research Letters 31 78–92 (2019) https://doi.org/10.1016/j.frl.2019.04.023.
Bianco, S., Renò, R. “Dynamics of Intraday Serial Correlation in the Italian Futures Market.” The Journal of Futures Markets 26.1 61–84 (2006) http://dx.doi.org/10.1002/fut.20182.
Black, F. “Studies of Stock Price Volatility Changes.” In Proceedings of the 1976 Meetings of the American Statistical Association, Business and Economics Section 177–181 (1976).
Bookstaber, R. “Understanding and Monitoring the Liquidity Crisis Cycle.” Financial Analysts Journal 56.5 17–22 (2000) https://doi.org/10.2469/faj.v56.n5.2385.
Bouchaud, J., Matacz, A., Potters, M. “The Leverage Effect in Financial Markets: Retarded Volatility and Market Panic.” Physical Review Letters 87.22 228701 (2001) http://dx.doi.org/10.1103/PhysRevLett.87.228701.
Bouri, E., Molnár, P., Azzi, G., Roubaud, D., Hagfors, L. I. “On the Hedge and Safe Haven Properties of Bitcoin: Is It Really More Than a Diversifier?” Finance Research Letters 20 192–198 (2017) https://www.sciencedirect.com/science/article/abs/pii/S1544612316301817.
Bouri, E., Vo, X. V., Saeed, T. “Return Equicorrelation in the Cryptocurrency Market: Analysis and Determinants.” Finance Research Letters 38 101497 (2021) https://doi.org/10.1016/j.frl.2020.101497.
Campbell, J., Lo, A. H., McKinlay, C. The Econometrics of Financial Markets. Princeton University Press (1997).
Caporale, G. M., Gil-Alana, L., Plastun, A. “Short Term Price Overreactions: Identification, Testing, Exploitation.” Computational Economics 51.4 913–940 (2018) https://doi.org/10.1007/s10614-017-9651-2.
Cont, R. “Empirical Properties of Asset Returns: Stylized Facts and Statistical Issues.” Quantitative Finance 1 223–236 (2001) https://doi.org/10.1080/713665670.
Corbet, S., Lucey, B., Urquhart, A., Yarovaya, L. “Cryptocurrencies as a Financial Asset: A Systematic Analysis.” International Review of Financial Analysis 62 182–199 (2019) https://doi.org/10.1016/j.irfa.2018.09.003.
Cromwell, J. B., Labys, W. C., Terraza, M. Univariate Tests for Time Series Models (No. 99). Sage (1994).
Ding, Z., Granger, C. W. J., Engle, R. F. “A Long Memory Property of Stock Market Returns and a New Model.” Journal of Empirical Finance 1.1 https://doi.org/10.1016/0927-5398(93)90006-D.
Dwyer, G. “The Economics of Bitcoin and Similar Private Digital Currencies.” Journal of Financial Stability 17.C 81–91 (2015) https://doi.org/10.1016/j.jfs.2014.11.006.
Dyhrberg, A. H. “Bitcoin, Gold and the Dollar: A GARCH Volatility Analysis.” Finance Research Letters 16 85–92 (2016) https://doi.org/10.1016/j.frl.2015.10.008.
Dyhrberg, A. H. “Hedging Capabilities of Bitcoin. Is It the Virtual Gold?” Finance Research Letters 16 139–144 (2016) https://doi.org/10.1016/j.frl.2015.10.025.
Dyhrberg, A. H., Foley, S., Svec, J. “How Investible Is Bitcoin? Analyzing the Liquidity and Transaction Costs of Bitcoin Markets.” Economics Letters 171 140–143 (2018) https://doi.org/10.1016/j.econlet.2018.07.032.
Eross, A., McGroarty, F., Urquhart, A., Wolfe, S. “The Intraday Dynamics of Bitcoin.” Research in International Business and Finance 49 71–81 (2019) https://doi.org/10.1016/j.ribaf.2019.01.008.
Fama, E. “Efficient Capital Markets: A Review of Theory and Empirical Work.” The Journal of Finance 25.2 383–417 (1970) http://www.jstor.org/stable/2325486?origin=JSTOR-pdf.
Figlewski, S., Wang, X. “Is the Leverage Effect a Leverage Effect?” SSRN (2001) (accessed 1 June 2021) https://dx.doi.org/10.2139/ssrn.256109.
Gebka, B., Wohar, M. “Causality Between Trading Volume and Returns: Evidence from Quantile Regressions.” International Review of Economics and Finance 27.C 144–159 (2013) https://doi.org/10.1016/j.iref.2012.09.009.
Hu, B., McInish, T., Miller, J., Zeng, L. “Intraday Price Behavior of Cryptocurrencies.” Finance Research Letters 28 337–342 (2019) https://doi.org/10.1016/j.frl.2018.06.002.
Jiang, G. J., Oomen, R. C. A. “Testing for Jumps when Asset Prices Are Bbserved with Noise: A Swap Variance Approach.” Journal of Econometrics 144.2 352–370 (2008) https://doi.org/10.1016/j.jeconom.2008.04.009.
Kendall, M., Stuart, A. The Advanced Theory of Statistics, Vol.3. Griffin (1983).
Kielinski, M. (Username: Zielak) “Bitcoin Historical Data: Bitcoin Data at 1-Min Intervals from Select Exchanges, Jan 2012 to March 2021.” Kaggle (accessed 3 June 2021) https://www.kaggle.com/mczielinski/bitcoin-historical-data.
Koutmos, G. “Feedback Trading and the Autocorrelation Pattern of Stock Returns: Further Empirical Evidence.” Journal of International Money and Finance 16.4 625–636 (1997) https://doi.org/10.1016/S0261-5606(97)00021-1.
Kristoufek, L. “What Are the Main Drivers of the Bitcoin Price? Evidence from Wavelet Coherence Analysis.” PLoS ONE 10.4 https://doi.org/10.1371/journal.pone.0123923.
Madhavan, A. “Market Microstructure: A Survey.” Journal of Financial Markets 3.3 205–258 (2000) https://doi.org/10.1016/S1386-4181(00)00007-0.
Nadarajah, S., Chu, J. “On the Inefficiency of Bitcoin.” Economics Letters 150 6–9 (2017) https://doi.org/10.1016/j.econlet.2016.10.033.
Nakamoto, S. “Bitcoin: A Peer-to-Peer Electronic Cash System.” (2008) (accessed 1 June 2021) https://bitcoin.org/bitcoin.pdf.
Pagan, A. “The Econometrics of Financial Markets.” Journal of Empirical Finance 3 15–102 (1996) https://doi.org/10.1016/0927-5398(95)00020-8.
Platanakis, E., Urquhart, A. “Portfolio Management with Cryptocurrencies: The Role of Estimation Risk.” Economics Letters 177 76–80 (2019) https://doi.org/10.1016/j.econlet.2019.01.019.
Roll, R. “A Simple Implicit Measure of the Effective Bid-Ask Spread in an Efficient Market.” The Journal of Finance 39.4 1127–1139 (1984) https://doi.org/10.1111/j.1540-6261.1984.tb03897.x.
Scaillet, O., Treccani, A., Trevisan, C. “High-Frequency Jump Analysis of the Bitcoin Market.” Journal of Financial Econometrics 18.2 209–232 (2020) https://doi.org/10.1093/jjfinec/nby013.
Scharnowski, S. “Understanding Bitcoin Liquidity.” Finance Research Letters 38 101477 (2021) https://doi.org/10.1016/j.frl.2020.101477.
Sensoy, A. “The Inefficiency of Bitcoin Revisited: A High-Frequency Analysis with Alternative Currencies.” Finance Research Letters 28 68–73 (2019) https://doi.org/10.1016/j.frl.2018.04.002.
Sewell, M. “Characterization of Financial Time Series.” UCL Department of Computer Science (Research Note 11/01) (2011) (accessed 1 June 2021) http://www.cs.ucl.ac.uk/fileadmin/UCL-CS/research/Research_Notes/RN_11_01.pdf.
Tankov, P., Voltchova, E. Jump-Diffusion Models: A Practitioner’s Guide. Banque et Marches (2009).
Taylor, S. J. Modelling Financial Time Series. John Wiley and Sons, Ltd. (1986).
Urquhart, A. “The Inefficiency of Bitcoin.” Economics Letters 148 80–82 (2016) https://doi.org/10.1016/j.econlet.2016.09.019.
Zhou, B. “High-Frequency Data and Volatility in Foreign-Exchange Rates.” Journal of Business & Economic Statistics 14.1 45–52 (1996) https://doi.org/10.1080/07350015.1996.10524628.
Copyright (c) 2021 Giacomo De Nicola
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- The Author retains copyright in the Work, where the term “Work” shall include all digital objects that may result in subsequent electronic publication or distribution.
- Upon acceptance of the Work, the author shall grant to the Publisher the right of first publication of the Work.
- The Author shall grant to the Publisher and its agents the nonexclusive perpetual right and license to publish, archive, and make accessible the Work in whole or in part in all forms of media now or hereafter known under a Creative Commons Attribution 4.0 International License or its equivalent, which, for the avoidance of doubt, allows others to copy, distribute, and transmit the Work under the following conditions:
- Attribution—other users must attribute the Work in the manner specified by the author as indicated on the journal Web site;
- The Author is able to enter into separate, additional contractual arrangements for the nonexclusive distribution of the journal's published version of the Work (e.g., post it to an institutional repository or publish it in a book), as long as there is provided in the document an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post online a prepublication manuscript (but not the Publisher’s final formatted PDF version of the Work) in institutional repositories or on their Websites prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work. Any such posting made before acceptance and publication of the Work shall be updated upon publication to include a reference to the Publisher-assigned DOI (Digital Object Identifier) and a link to the online abstract for the final published Work in the Journal.
- Upon Publisher’s request, the Author agrees to furnish promptly to Publisher, at the Author’s own expense, written evidence of the permissions, licenses, and consents for use of third-party material included within the Work, except as determined by Publisher to be covered by the principles of Fair Use.
- The Author represents and warrants that:
- the Work is the Author’s original work;
- the Author has not transferred, and will not transfer, exclusive rights in the Work to any third party;
- the Work is not pending review or under consideration by another publisher;
- the Work has not previously been published;
- the Work contains no misrepresentation or infringement of the Work or property of other authors or third parties; and
- the Work contains no libel, invasion of privacy, or other unlawful matter.
- The Author agrees to indemnify and hold Publisher harmless from Author’s breach of the representations and warranties contained in Paragraph 6 above, as well as any claim or proceeding relating to Publisher’s use and publication of any content contained in the Work, including third-party content.
- The Author agrees to digitally sign the Publisher’s final formatted PDF version of the Work.
Revised 7/16/2018. Revision Description: Removed outdated link.