Periodicity in Bitcoin returns: A time-varying volatility approach

Aknouche, Abdelhakim and Dimitrakopoulos, Stefanos (2018): Periodicity in Bitcoin returns: A time-varying volatility approach.

Abstract

We examine if the day-of-the-week effect is present in Bitcoin return series. The model specification in use accounts for conditional heteroscedasticity, which is captured in the form of a stochastic volatility process that allows for periodic time-varying parameters. We find periodicity in Bitcoin returns, which is evidence against the market efficiency of Bitcoin.

• Item Type: MPRA Paper
• Language: English
• Keywords: Bitcoin series, periodicity, stochastic volatility model.
• Subjects: C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C58 - Financial Econometrics; G - Financial Economics > G3 - Corporate Finance and Governance > G38 - Government Policy and Regulation

References

Aknouche, A. 2017. Periodic autoregressive stochastic volatility. Statistical Inference for Stochastic Processes, 20, 139--177.

Aknouche, A. 2015. Explosive strong periodic autoregressions with multiplicity one. Journal of Statistical Planning and Inference, 161, 50-72.

Aknouche, A., Almohaimeed, B.S. and Dimitrakopoulos, S. 2022. Periodic autoregressive conditional duration. Journal of Time Series Analysis, 43, 5-29.

Aknouche, A., Demmouche, N., Dimitrakopoulos, S. and Touche, N. 2020. Bayesian MCMC analysis of periodic asymmetric power GARCH models. Studies in Nonlinear Dynamics and Econometrics, 24, 20180112.

Balcilar, M., Bouri, E., Gupta, R., and Roubaud, D. 2017. Can volume predict Bitcoin returns and volatility? A quantiles-based approach. Economic Modelling, 64, 74--81.

Bouoiyour, J., Selmi, R. 2015. Bitcoin price: Is it really that new round of volatility can be on way? MPRA Paper (No. 65580). https://mpra.ub.uni-muenchen.de/65580/.

Bouoiyour, J., Selmi, R. 2016. Bitcoin: A beginning of a new phase?. Economics Bulletin, 36, 1430-1440.

Bouri, E., Azzi, G., and Dyhrberg, A.H. 2017a. On the return-volatility relationship in the Bitcoin market around the price crash of 2013. Discussion Paper (No. 2016-41).

Bouri, E., Molnr, P., Azzi, G., Roubaud, D., and Hagfors, L.I. 2017b. On the hedge and safe haven properties of bitcoin: Is it really more than a diversifier?. Finance Research Letters, 20, 192--198.

Coinmarket 2016 Crypto-Currency Market Capitalizations. https://coinmarketcap. com/currencies/.

Dyhrberg, A.H., 2016a. Bitcoin, gold and the dollar - A GARCH volatility analysis. Finance Research Letters, 16, 85-92.

Dyhrberg, A.H. 2016b. Hedging capabilities of Bitcoin. Is it the virtual gold?. Finance Research Letters, 16, 139--144.

Fama, E.F. 1965. Behavior of stock market prices. Journal of Business, 38, 34--105.

Geweke, J. 1989. Bayesian inference in econometric models using Monte Carlo integration. Econometrica, 57, 1317--1339.

Glaser, F., Zimmarmann, K., Haferhorn, M., Weber, M.C., and Siering, M. 2014. Bitcoin - Asset or currency? Revealing usersâl hidden intentions. (ECIS 2014, Tel Aviv). Available at https://ssrn.com/abstract=2425247.

Gronwald, M. 2014. The economics of Bitcoins - Market characteristics and price jumps. CESifo Working Paper, (No. 5121). Available at https://ssrn.com/abstract=2548999.

Katsiampa, P. 2017. Volatility estimation for Bitcoin: A comparison of GARCH models. Economics Letters, 158, 3-6.

Mbanga, C.L. 2018. The day-of-the-week pattern of price clustering in Bitcoin. Applied Economics Letters, 1--6.

Nadarajah, S., Chu, J. 2017. On the inefficiency of Bitcoin. Economics Letters, 150, 6--9.

Naimy, V.Y., Hayek, M.R. 2018. Modelling and predicting the Bitcoin volatility using GARCH models. International Journal of Mathematical Modelling and Numerical Optimisation, 8, 197--215.

Nakamoto, S. 2009. Bitcoin: A peer-to-peer electronic cash system. https://bitcoin. org/bitcoin.pdf/.

Phillip, A., Chan, J., and Peiris, S. 2018. A new look at cryptocurrencies. Economics Letters, 163, 6--9.

Spiegelhalter, D., Best, N., Carlin, B., Van Der Linde, A. 2002. Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64, 583--639.

Taylor, S. 1986. Modelling Financial Time Series. Wiley, Chichester.

Urquhart, A. 2016. The inefficiency of Bitcoin. Economics Letters, 148, 80--82.