Cross-exchange crypto trading presents inherent risks, particularly for centralized exchanges. Investors observe exacerbating crypto volatility and counterparty risk and would like to quantify these elements of crypto trades. The multiple exchanges require a multivariate view on the structures of risk spillover across exchanges. Here, a Multivariate Heterogeneous AutoRegression (MHAR) model is designed and analyzed, accommodating the stylized facts of crypto markets, including 24/7 trading and the long-memory effect on return variations. The proposed MHAR approach clearly reveals the intensity of interconnectedness among exchanges during extreme events, e.g., the Bitcoin market. Additionally, one observes extremely volatile eigenvector centralities of Futures Exchange Ltd (FTX), suggesting potential implications for its bankruptcy. Furthermore, portfolios that account for the dynamics of partial correlations or eigenvector centralities offer promising results in terms of risk measures.

中文翻译：

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跨交易所加密风险：高频动态网络视角


跨交易所加密货币交易存在固有风险，特别是对于中心化交易所而言。投资者观察到加密货币波动性和交易对手风险加剧，并希望量化加密货币交易的这些要素。多个交易所需要对跨交易所的风险溢出结构采取多元观点。这里，设计并分析了多元异质自回归 (MHAR) 模型，以适应加密货币市场的程式化事实，包括 24/7 交易和对回报变化的长记忆效应。拟议的 MHAR 方法清楚地揭示了极端事件（例如比特币市场）期间交易所之间相互关联的强度。此外，人们观察到期货交易所有限公司（FTX）的特征向量中心性极其不稳定，这表明其破产的潜在影响。此外，考虑部分相关性或特征向量中心性动态的投资组合在风险度量方面提供了有希望的结果。