Price, liquidity and information spillover within the cryptocurrency market. The case of Bitfinex

Agata  Kliber

doi:10.26354/bb.4.4.73.2018

Published : 2019-02-25

Vol. 73 No. 4 (2018)

Price, liquidity and information spillover within the cryptocurrency market. The case of Bitfinex

Agata Kliber

https://orcid.org/0000-0003-1996-5550

DOI: https://doi.org/10.26354/bb.4.4.73.2018

Abstract

The aim of the research was to investigate price, liquidity and information spillover within the cryptocurrency market. Since from the introduction of bitcoin, many other cryptocurrencies have emerged, there appears a question, whether the market is and will be dominated by Bitcoin, while other cryptocurrencies are only marginal and follow the price, liquidity and overall dynamics of Bitcoin, or can they be possibly used to portfolio diversification. The article contributes also to the debate on the possibility of contagion across the cryptocurrency market. By measuring and quantifying the spillovers of prices, information and liquidity among the cryptocurrencies, we try to investigate the strength of influence of the separate currencies on the whole system. The following cryptocurrencies traded in Bitfinex were taken it account: Bitcoin, Ether, Litecoin, Dashcoin and Monero. All the prices were expressed in US dollars. The period of the study covers one year, from May 2017 to May 2018. Liquidity was measured by Volume over Volatility measure, while information inflow through volume traded. Volume of spillovers were computed according to the methodology proposed by Diebold and Yilmaz. The study suggest strong co-movement across the currencies and high and relatively stable value of spillover indices.

Keywords:

cryptocurrencies, Bitcoin, DASH, Ether, Litecoin, Monero, spillover index, liquidity

JEL Codes

G11, G15, G19

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Kliber, A. . (2019). Price, liquidity and information spillover within the cryptocurrency market. The case of Bitfinex. Safe Bank, 73(4), 62–79. https://doi.org/10.26354/bb.4.4.73.2018

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References

Balciclar M., Bouri E., Gupta R., Roubaud D. Can volume predict Bitcoin returns and volatility? A quantiles-based approach, “Economic Modelling” 2017 Vol. 64: 74 – 61.

Baur D.G., Hong K., Lee A.D. Bitcoin: Medium of exchange or speculative assets?

“Journal of International Financial Markets, Institutions and Money” 2018 Vol. 54: 177-189.

Będowska-Sójka B. Wpływ informacji na ceny instrumentów finansowych. Analiza danych śróddziennych, Wydawnictwo Uniwersytetu Ekonomicznego w Poznaniu, Poznań 2014

Będowska-Sójka B. The coherence of Liquidity Measures. The Evidence from the Emerging Market, “Finance Research Letters” 2018 10.1016/j.frl.2018.02.014.

Bollen, J., Mao, H., Zeng, X. Twitter mood predicts the stock market, “Journal of Computational Science”, 2011, Vol. 2(1):1–8.

Bordino I., Battiston S., Caldarelli, G., Cristelli M., Ukkonen A., Weber I. Web search queries can predict stock market volumes “PloS one” 2012, Vol. 7(7):e40014, 1- 17.

Bouri, E., Azzi, G. Dhyrberg, A. On the return-volatility relationship in the Bitcoin market around the price crash of 2013, “Economics E-Journal” 2017a, Vol.11(2): 1 – 16.

Bouri E., Gupta R., Tiwari A.K., Roubaud D. Does bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions, “Finance Research Letters” 2017b, Vol. 23: 87-95

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” 2017c, Vol. 20: 192-198

Brauneis A., Mestel R. Price Discovery of Cryptocurrencies: Bitcoin and Beyond, “Economic Letters” 2018, Vol. 165, 58 – 61.

Cheah E.-T., Fry J. Speculative bubbles in bitcoin markets? An empirical investigation into the fundamental value of bitcoin, “Economics Letters” 2015, Vol. 130: 32-36

Conrad C., Custovic A., Ghysels E .Long- and short-term cryptocurrency volatility components: A GARCH-MIDAS analysis, “Journal of Risk and Financial Management” 2018, Vol. 11 (2): 23 – 35.

Corbet S., Lucey B., Urquhart A., Yarovaya L. Cryptocurrencies as a financial asset: A systematic analysis, “International Review of Financial Analysis 2018 (in press) https://doi.org/10.1016/j.irfa.2018.09.003

Diebold, F. X. , Yilmaz, K., Measuring financial asset return and volatility spillovers, with application to global equity markets, “Economic Journal” 2009, Vol. 199(534): 158-171.

Diebold F.X, Yilmaz K., Errata to Measuring financial asset return and volatility spillovers, with application to global equity markets, “Economic Journal”, 2010, Vol. 120: F354 – F355, doi: 10.1111/j.1468-0297.2010.02386.x

Dimpfl, T., Peter F.J. Group transfer entropy with an application to cryptocurrencies, “Physica A: Statistical Mechanics and its Applications”, 2018, in press: https://doi.org/10.1016/j.physa.2018.10.048

Dhyrberg A.H. Bitcoin, gold and the dollar—A GARCH volatility analysis “Finance Research Letters” 2016, Vol. 16: 85-92

Easley, D., De Prado M.L, O’Hara M. Discerning Information from Trade Data, “Journal of Financial Economics” 2016, Vol. 120(2): 269 – 286.

Fong K.Y.L., Holden C.W., Tobek O., Are Volatility Over Volume Liquidity Proxies Useful For Global Or US Research, Kelley School of Business Research Paper No. 17-49, 2017

Fry J., Cheah E.-T. Negative bubbles and shocks in cryptocurrency markets “International Review of Financial Analysis” 2016, Vol. 47: 343-35

Goyenko R.Y., Holden C.W., Trzcinka C.A. Do Liquidity Measures Measure Liquidity? “Journal of Finacial Economics” 2009, Vol. 92(2): 153 – 181.

Jennings R.H., Starks L., Fellingham J. An Equilibrium Model of Asset Trading with Sequential Information Arrival, “Journal of Finance”, 1981, Vol. 36(1): 143 – 61.

Jones M.C., Kaul G., Lipson M.L. Transactin, Volumes and Volatility “The Review of Financial Studies”, 1994, Vol. 7, No. 4: 631-651

Karpoff J.M. The relation between price changes and trading volume: a survey. “The Journal of Financial and Quantitative Analysis” 1987,Vol. 22:109-126, doi: 10.2307/2330874

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

Klein T., Thu H., Walther T. Bitcoin is not the new gold – A comparison of volatility, correlation, and portfolio performance International Review of Financial Analysis, 59 (2018): 105-116.

Kloessner S, Wagner S., fastSOM: Calculation of Spillover Measures. R package version 1.0.0. , 2016, URL: http://CRAN.R-project.org/package=fastSOM,

Kloessner S., Wagner S., Exploring All VAR Orderings for Calculating Spillovers? Yes, We Can! – A Note on Diebold and Yilmaz (2009), “Journal of Applied Econometrics” 2012, Vol. 29(1): 172-179

Kliber A., Marszałek P., Musiałkowska I, Świerczyńska K., Bitcoin: Safe Haven Hedge or Diversifier? Perception of Bitcoin in the Context of a Country’s Economic Situation, presented at 25th EBES Conference, May 23 – 25, 2018, Berlin.

Koutmos, D. Return and Volatility Spillovers Among Cryptocurrencies, “Economic Letters” 2018, Vol. 173: 122 – 127

Marshall B.R., Nguyen N.H. Visaltanachoti N. Do Liquidity Proxies Measure Liquidity Accurately in ETFs? “Journal of International Financial Markets, Institutions and Money”, Vol. 55: 94 – 111.

Matta M., Lunescu M.I., Marchesi M. The Predictor Impact of Web Search Media on Bitcoin Trading Volumes, “Proceedings of the7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management”, 2015 DOI: 10.5220/0005618606200626

Pavlus J., Świat Bitcoina, Świat Nauki 2018, Vol. 2(318): 32 – 37.

Rutnik M., What is Dash – a Short Guide, “Android Authority”, 18.02.2018, https://www.androidauthority.com/what-is-dash-820943/

Vidal-Tomas, D., Ibanez A.M., Farinos J.E. Herding in the Cryptocurrency Market: CSSD and CSAD Approaches, “Finance Research Letters” 2018 (in press), https://doi.org/10.1016/j.frl.2018.09.008

Trapletti A., Hornik K. tseries: Time Series Analysis and Computational Finance. R package version 0.10-44, 2018

Wei W.C, Liquidity and market efficiency in cryptocurrencies, “Economics Letters”, 2018, vol. 168(C): 21-24.

Yi S., Xu Z., Wang G-J. Volatility connectedness in the cryptocurrency market: Is Bitcoin a dominant cryptocurrency? “International Review of Financial Analysis”, 2018, Vol. 60: 98 – 114.

Zhang W., Wang P., Li X., Shen D. The inefficiency of cryptocurrency and its cross-correlation with Dow Jones Industrial Average, “Physica A: Statistical Mechanics and its Applications”, 2018, vol. 510(C): 658-670.

Zhang W., Chan S., Chu J., Nadarajah S. Stylized Facts for High Frequency Currency Data “Physica A: Statistical Mechanics and its Applications” 2019, vol. 513(1): 598 – 612.

Zięba D., Śledziewska K. Are demand shocks in Bitcoin contagious? Working Papers 2018-17, Faculty of Economic Sciences, University of Warsaw, 2018.