Bitcoin-Altcoin Price Synchronization Hypothesis: Evidence from Recent Data

Adebayo Adedokun^1,

¹Department of Economics, Obafemi Awolowo University

Cite this paper:
Adebayo Adedokun. Bitcoin-Altcoin Price Synchronization Hypothesis: Evidence from Recent Data. Journal of Finance and Economics. 2019; 7(4):137-147. doi: 10.12691/jfe-7-4-4

Abstract

The study investigates the bitcoin-altcoin price synchronization hypothesis using cointegrating test and VEC Granger Causality/Block Exogeneity Test approaches on the daily data of bitcoin and ten selected alternative coins (altcoins) between August 8, 2015 and December 31, 2018. The data is structurally divided into three distinct periods which are; August 8, 2015 to December 31, 2016; January 1, 2017 to December 31, 2017 and January 1, 2018 to December 31, 2018. The study establishes pure price separation between the altcoin and bitcoin in 2015-2016, price synchronization between bitcoin and each of the selected altcoin in 2017 and dominant altcoin-to-bitcoin price formation in 2018. The study concludes that cryptocurrency buyers are more sensitive in 2018 to the features and quality of project each coin promotes, unlike the indiscriminating choices which dominated cryptocurrency world during the 2017 boom.

Keywords:
Bitcoin Altcoin price synchronization price formation

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]	Baur, D. G., Hong, K., & Lee, A. D. (2018). Bitcoin: Medium of exchange or speculative assets?. Journal of International Financial Markets, Institutions and Money, 54, 177-189.
[2]	Urquhart, A. (2018). What causes the attention of Bitcoin?. Economics Letters, 166, 40-44.
[3]	Bouri, E., Lau, C. K. M., Lucey, B., & Roubaud, D. (2018). Trading volume and the predictability of return and volatility in the cryptocurrency market. Finance Research Letters, 29, 340-346.
[4]	Cagli, E. C. (2018). Explosive behavior in the prices of Bitcoin and altcoins. Finance Research Letters.
[5]	Pavel, C., d'Artis, K., & Miroslava, R. (2017). Virtual Relationships: Short-and Long-run Evidence from BitCoin and Altcoin Markets (No. 201705).
[6]	Chen, Y., Phillips, P. C., & Yu, J. (2017). Inference in continuous systems with mildly explosive regressors. Journal of Econometrics, 201(2), 400-416.
[7]	Brauneis, A., & Mestel, R. (2018). Price discovery of cryptocurrencies: Bitcoin and beyond. Economics Letters, 165, 58-61.
[8]	Yi, S., Xu, Z., & Wang, G. J. (2018). Volatility connectedness in the cryptocurrency market: Is Bitcoin a dominant cryptocurrency?. International Review of Financial Analysis, 60, 98-114.
[9]	Grinberg, R. (2012). Bitcoin: An innovative alternative digital currency. Hastings Sci. & Tech. LJ, 4, 159.
[10]	Barber, S., Boyen, X., Shi, E., & Uzun, E. (2012, February). Bitter to better—how to make bitcoin a better currency. In International Conference on Financial Cryptography and Data Security (pp. 399-414). Springer, Berlin, Heidelberg.
[11]	Ciaian, P., Rajcaniova, M., & Kancs, D. A. (2016). The economics of BitCoin price formation. Applied Economics, 48(19), 1799-1815.
[12]	Moore, T., & Christin, N. (2013, April). Beware the middleman: Empirical analysis of Bitcoin-exchange risk. In International Conference on Financial Cryptography and Data Security (pp. 25-33). Springer, Berlin, Heidelberg.
[13]	Bouoiyour, J., Selmi, R., & Tiwari, A. (2014). Is Bitcoin business income or speculative bubble? Unconditional vs. conditional frequency domain analysis.
[14]	Kristoufek, L. (2015). What are the main drivers of the Bitcoin price? Evidence from wavelet coherence analysis. PloS one, 10(4), e0123923.
[15]	Blau, B. M. (2017). Price dynamics and speculative trading in bitcoin. Research in International Business and Finance, 41, 493-499.
[16]	Brown, R. L., Durbin, J., & Evans, J. M. (1975). Techniques for testing the constancy of regression relationships over time. Journal of the Royal Statistical Society. Series B (Methodological), 149-192.
[17]	Zivot, E. (2006). Time Series Econometrics’ Lesson Note. ••, University of Washington.
[18]	Engle, R. F., & Granger, C. W. (1987). Co-integration and error correction: representation, estimation, and testing. Econometrica: journal of the Econometric Society, 251-276.
[19]	Granger, C. W. (1986). Developments in the study of cointegrated economic variables. Oxford Bulletin of economics and statistics, 48(3), 213-228.
[20]	Johansen, S. (1991). Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models. Econometrica: Journal of the Econometric Society, 1551-1580.