Liquidity connectedness in cryptocurrency market

Mudassar Hasan (), Muhammad Abubakr Naeem (), Muhammad Arif (), Syed Jawad Hussain Shahzad and Xuan Vinh Vo ()
Additional contact information
Mudassar Hasan: The University of Lahore
Muhammad Abubakr Naeem: University College Dublin
Muhammad Arif: Shaheed Benazir Bhutto University
Xuan Vinh Vo: University of Economics Ho Chi Minh City

Financial Innovation, 2022, vol. 8, issue 1, 1-25

Abstract: Abstract We examine the dynamics of liquidity connectedness in the cryptocurrency market. We use the connectedness models of Diebold and Yilmaz (Int J Forecast 28(1):57–66, 2012) and Baruník and Křehlík (J Financ Econom 16(2):271–296, 2018) on a sample of six major cryptocurrencies, namely, Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Ripple (XRP), Monero (XMR), and Dash. Our static analysis reveals a moderate liquidity connectedness among our sample cryptocurrencies, whereas BTC and LTC play a significant role in connectedness magnitude. A distinct liquidity cluster is observed for BTC, LTC, and XRP, and ETH, XMR, and Dash also form another distinct liquidity cluster. The frequency domain analysis reveals that liquidity connectedness is more pronounced in the short-run time horizon than the medium- and long-run time horizons. In the short run, BTC, LTC, and XRP are the leading contributor to liquidity shocks, whereas, in the long run, ETH assumes this role. Compared with the medium term, a tight liquidity clustering is found in the short and long terms. The time-varying analysis indicates that liquidity connectedness in the cryptocurrency market increases over time, pointing to the possible effect of rising demand and higher acceptability for this unique asset. Furthermore, more pronounced liquidity connectedness patterns are observed over the short and long run, reinforcing that liquidity connectedness in the cryptocurrency market is a phenomenon dependent on the time–frequency connectedness.

Keywords: Liquidity; Time–frequency connectedness; Cryptocurrencies (search for similar items in EconPapers)
JEL-codes: C10 C32 G01 G15 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

Downloads: (external link)
http://link.springer.com/10.1186/s40854-021-00308-3 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:fininn:v:8:y:2022:i:1:d:10.1186_s40854-021-00308-3

Ordering information: This journal article can be ordered from
http://www.springer. ... nomics/journal/40589

DOI: 10.1186/s40854-021-00308-3

Access Statistics for this article

Financial Innovation is currently edited by J. Leon Zhao and Zongyi

More articles in Financial Innovation from Springer, Southwestern University of Finance and Economics
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().