Publication Type
Journal Article
Version
submittedVersion
Publication Date
7-2024
Abstract
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.
Keywords
Partial correlation network, high-frequency data, Bitcoin, FTX, HAR
Discipline
Economics | Finance
Publication
International Review of Financial Analysis
Volume
94
First Page
1
Last Page
16
ISSN
1057-5219
Identifier
10.1016/j.irfa.2024.103246
Publisher
Elsevier
Embargo Period
4-16-2024
Citation
WANG, Yifu; LU, Wanbo; LIU, Min-Bin; REN, Rui; and HARDLE, Wolfgang Karl.
Cross-exchange crypto risk: A high-frequency dynamic network perspective. (2024). International Review of Financial Analysis. 94, 1-16.
Available at: https://ink.library.smu.edu.sg/skbi/40
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/j.irfa.2024.103246