This study uses high-frequency (1-min) price data to examine the connectedness among the leading cryptocurrencies (i.e. Bitcoin, Ethereum, Binance, Cardano, Litecoin, and Ripple) at volatility and high-order (third and fourth orders in this paper) moments based on skewness and kurtosis. The sample period is from February 10, 2020, to August 20, 2022, which captures a pandemic, wartime, cryptocurrency market crashes, and the full collapse of a stablecoin. Using a time-varying parameter vector autoregressive (TVP-VAR) connectedness approach, we find that the total dynamic connectedness throughout all realized estimators grows with the time frequency of the data. Moreover, all estimators are time dependent and affected by significant events. As an exception, the Russia–Ukraine War did not increase the total connectedness among cryptocurrencies. Analysis of third- and fourth-order moments reveals additional dynamics not captured by the second moments, highlighting the importance of analyzing higher moments when studying systematic crash and fat-tail risks in the cryptocurrency market. Additional tests show that rolling-window-based VAR models do not reveal these patterns. Regarding the directional risk transmissions, Binance was a consistent net transmitter in all three connectedness systems and it dominated the volatility connectedness network. In contrast, skewness and kurtosis connectedness networks were dominated by Litecoin and Bitcoin and Ripple were net shock receivers in all three networks. These findings are expected to serve as a guide for portfolio optimization, risk management, and policy-making practices.

中文翻译：

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加密货币高阶时刻的时变溢出

本研究使用高频（1 分钟）价格数据来检查主要加密货币（即比特币、以太坊、币安、卡尔达诺、莱特币和瑞波币）在波动性和高阶（本文中的第三阶和第四阶）方面的连通性）基于偏度和峰度的矩。样本期为2020年2月10日至2022年8月20日，涵盖了流行病、战时、加密货币市场崩溃以及稳定币的全面崩溃。使用时变参数向量自回归（TVP-VAR）连通性方法，我们发现所有实现的估计器的总动态连通性随着数据的时间频率而增长。此外，所有估计量都依赖于时间并受到重大事件的影响。作为例外，俄罗斯-乌克兰战争并没有增加加密货币之间的总体联系。对三阶和四阶矩的分析揭示了二阶矩未捕获的额外动态，凸显了在研究加密货币市场中的系统崩溃和厚尾风险时分析更高矩的重要性。其他测试表明，基于滚动窗口的 VAR 模型并未揭示这些模式。在定向风险传输方面，币安在所有三个连通性系统中都是一致的网络传输者，并且在波动性连通性网络中占据主导地位。相比之下，偏度和峰度连通性网络由莱特币主导，比特币和瑞波币是所有三个网络中的净冲击接收者。这些发现预计将成为投资组合优化、风险管理和政策制定实践的指南。