Cross-exchange Crypto Risk : A High-frequency Dynamic Network Perspective

Cross-exchange trading induces risk spillover in the crypto market, especially for centralized exchanges, which compound crypto volatility and counterparty risk. We propose a Multivariate Heterogeneous AutoRegression for Crypto Market (MHAR-CM) to specifically investigate interconnectedness among 9 different exchanges on Bitcoin in a high-frequency resolution via dynamic partial correlation networks. MHAR-CM accommodates the stylized facts of Bitcoin, such as trading 24/7 and the long-memory effect on returns. The monthly dependence coefficients of MHAR-CM reflect the evolution of Bitcoin returns and the persistent influence of events.We find that the scale of an exchange determines its influence towards the others, and the interconnectedness among these exchanges is stronger during extreme events in the Bitcoin market, e.g., large price drop on 2021-05-19. We also conclude with some remarks on FTX due to its recent bankruptcy. Furthermore, faced with the challenging cross-exchange arbitrage in practice, we construct portfolios to incorporate the network information into the risk diversification and demonstrate that portfolios considering the dynamics of partial correlations or eigenvector centralities offer a promising result in terms of risk measures