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

• Main Authors: WANG, Yifu, LU, Wanbo, LIU, Min-Bin, REN, Rui, HARDLE, Wolfgang Karl
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Partial correlation network; high-frequency data; Bitcoin; FTX; HAR; Economics; Finance
• Online Access: https://ink.library.smu.edu.sg/skbi/40; https://ink.library.smu.edu.sg/context/skbi/article/1039/viewcontent/SSRN_id4308825.pdf
• Institution: Singapore Management University

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.