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Liquidity connectedness in cryptocurrency market

机译：加密货币上的流动性连锁性

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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 K?ehlík (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.
机译：我们研究了加密货币市场中流动性连锁性的动态。我们使用Diebold和Yilmaz的关联模型（int J预测28（1）：57-66,2012）和Baruník和k？ehlík（j·普通经济学7（2）：271-296,2018）在六个样本上主要加密货，即比特币（BTC），LiteCoin（LTC），Etereum（Eth），Ripple（XRP），Monero（XMR）和破折号。我们的静态分析揭示了我们样本加密货币中的中等流动性连锁性，而BTC和LTC在连通幅度中发挥着重要作用。对于BTC，LTC和XRP，eC，XMR和划线，观察到不同的流动性簇也形成另一个不同的流动性簇。频域分析表明，在短期时间范围内比中期和长期时间和长期时间视线更加明显流动性连锁。在短期下，BTC，LTC和XRP是流动性冲击的主要贡献者，而在长期之后，Eth致力于这种作用。与中期相比，在短期和长条款中发现了紧密的流动性聚类。时变分析表明加密货币中的流动性连锁性随着时间的推移而增加，指出了对这种独特资产的上升和更高可接受性的可能影响。此外，在短期和长期运行中观察到更明显的流动性连锁图案，加强了加密货币中的流动性连锁性是依赖于时频连接的现象。

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