Cross-exchange crypto risk: A high-frequency dynamic network perspective

• Published in: International review of financial analysis 2024-07, Vol.94, p.103246, Article 103246
• Main Authors: Wang, Yifu, Lu, Wanbo, Lin, Min-Bin, Ren, Rui, Härdle, Wolfgang Karl
• Format: Article
• Language: English
• Subjects: Bitcoin; FTX; HAR; High-frequency data; Partial correlation network
• ISSN: 1057-5219; 1873-8079
• DOI: 10.1016/j.irfa.2024.103246

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. •We propose a model that captures the evolutions of cross-exchange crypto risks.•The methodology combines matrix logarithm and HAR-type model.•The empirical findings indicate that Bitcoin market is sensitive to negative shocks.•Our results also confirm the volatile position of FTX in the whole market.•The two portfolios based on the proposed method perform better in risk measures.