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.
Highlights
Liquidity is a crucial facet of today’s financial markets that encompasses ease, speed, and affordability that an investor can trade
Liquidity is of great relevance to investors and policymakers, as a systematic liquidity factor exists in many financial markets (Chordia et al 2001; Marshall et al 2013)
An asset’s liquidity is linked to market-wide liquidity—an idea often known as liquidity commonality (Chordia et al 2000; Chuliá et al 2020)
Summary
Liquidity is a crucial facet of today’s financial markets that encompasses ease, speed, and affordability that an investor can trade. Many studies capitalized on transaction data for capturing liquidity in the cryptocurrency market while mainly focusing on BTC, such as Loi (2018), Wei (2018), and Brauneis and Mestel (2018) Overall, this literature strand has explored various facets of cryptocurrency liquidity and predominantly investigates the linkage between liquidity and efficiency. This study takes the literature on cryptocurrency liquidity one step further by exploring the liquidity linkages among cryptocurrencies, thereby adding to the previous works on crypto liquidity or its linkage with price efficiency (Kim 2017; Dyhrberg et al 2018; Loi 2018; Smales 2019; Wei 2018; Brauneis and Mestel 2018; Koutmos 2018; Baur et al 2019; Bouri et al 2019d). Baruník and Křehlík (2018); Continuous Wavelet Transform; Rolling-Window Wavelet Correlation
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