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

Section: Problems and Opinions

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.
Google Scholar

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

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

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

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.
Google Scholar

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

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.
Google Scholar

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.
Google Scholar

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
Google Scholar

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
Google Scholar

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

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
Google Scholar

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.
Google Scholar

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
Google Scholar

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.
Google Scholar

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
Google Scholar

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
Google Scholar

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

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

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
Google Scholar

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

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

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.
Google Scholar

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

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
Google Scholar

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

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.
Google Scholar

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

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
Google Scholar

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.
Google Scholar

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

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.
Google Scholar

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
Google Scholar

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

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

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
Google Scholar

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

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

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.
Google Scholar

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.
Google Scholar

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.
Google Scholar

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

Vol. 73 No. 4 (2018)
Published: 2019-02-25

ISSN: 1429-2939

eISSN: 2544-7068

10.26354

Publisher

Bankowy Fundusz Gwarancyjny

Licence CC

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

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