Education, Science, Technology, Innovation and Life
Open Access
Sign In

Liquidity spillover in cryptocurrency markets

Download as PDF

DOI: 10.23977/ferm.2023.060715 | Downloads: 31 | Views: 395

Author(s)

Zhanyi Ren 1

Affiliation(s)

1 University of Warwick, Coventry, CV4 7AL, United Kingdom

Corresponding Author

Zhanyi Ren

ABSTRACT

This study will investigate the liquidity spillover effects of five cryptocurrencies: Bitcoin, Ether, Binance-coin, Ripple, and Tether. Firstly, the researcher utilizes the Amihud illiquidity ratio to quantify the liquidity performance of the five currencies, which we treat as weekly for the purposes of our study due to data collecting constraints. Secondly, to quantify the liquidity spillover effect in the cryptocurrency market over the period of 2017-2022, the researcher employs Diebold and Yilmaz's spillover index. The results identify the senders and receivers of liquidity spillovers on an individual and pairwise basis for the five major currencies and demonstrate the presence of time variation. Additionally, this paper evaluates the news report-based cryptocurrency uncertainty index (UCRY). This includes the price of cryptocurrencies (UCRY price) and the uncertainty surrounding cryptocurrency policy (UCRY policy). Considering the constructed index follows the same path as the largest cryptocurrency, Bitcoin, it is therefore recommended that the Bitcoin price can be used to forecast the cryptocurrency uncertainty index. Overall, this study has filled a gap in the literature by conducting research on liquidity spillovers in cryptocurrency markets, and it presents some preliminary conclusions. However, in order to verify the validity of our findings and to provide more meaningful results, additional research is required over a longer time horizon and with additional cryptocurrency types.

KEYWORDS

Liquidity; spillover effect; cryptocurrency

CITE THIS PAPER

Zhanyi Ren, Liquidity spillover in cryptocurrency markets. Financial Engineering and Risk Management (2023) Vol. 6: 113-124. DOI: http://dx.doi.org/10.23977/ferm.2023.060715.

REFERENCES

[1] Böyükaslan A, Ecer F. Determination of drivers for investing in cryptocurrencies through a fuzzy full consistency method-Bonferroni (FUCOM-F’B) framework [J]. Technology in society, 2021, 67: 101745.
[2] Hileman G, Rauchs M. 2017 global cryptocurrency benchmarking study [J]. Available at SSRN 2965436, 2017.
[3] Yarovaya L, Matkovskyy R, Jalan A. The effects of a "black swan" event (COVID-19) on herding behavior in cryptocurrency markets [J]. Journal of International Financial Markets, Institutions and Money, 2021, 75: 101321.
[4] Mnif E, Jarboui A, Mouakhar K. How the cryptocurrency market has performed during COVID 19? A multifractal analysis [J]. Finance research letters, 2020, 36: 101647.
[5] Lucey B M, Vigne S A, Yarovaya L, et al. The cryptocurrency uncertainty index [J]. Finance Research Letters, 2022, 45: 102147.
[6] Guo S, Li C. Excess liquidity, housing price booms and policy challenges in China [J]. China & World Economy, 2011, 19(6): 76-91.
[7] Evans W N, Moore T J. Liquidity, economic activity, and mortality [J]. Review of Economics and Statistics, 2012, 94(2): 400-418.
[8] Giansante S, Chiarella C, Sordi S, et al. Structural contagion and vulnerability to unexpected liquidity shortfalls [J]. Journal of Economic Behavior & Organization, 2012, 83(3): 558-569. 
[9] Smimou K. Consumer attitudes, stock market liquidity, and the macro economy: A Canadian perspective [J]. International Review of Financial Analysis, 2014, 33: 186-209.
[10] Zheng W, Lou Y, Chen Y. On the unsustainable macroeconomy with increasing inequality of firms induced by excessive liquidity [J]. Sustainability, 2019, 11(11): 3075.

Downloads: 16470
Visits: 337875

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.