On the Intraday Behavior of Bitcoin

  • Giacomo De Nicola LMU Munich
Keywords: Market Efficiency, Stylized Facts, Behavioral Finance

Abstract

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.

References

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.

Published
2021-07-12
How to Cite
De Nicola, G. (2021). On the Intraday Behavior of Bitcoin. Ledger, 6. https://doi.org/10.5195/ledger.2021.213
Section
Research Articles