Cross-exchange Crypto Risk: A High-frequency Dynamic Network Perspective
We design a Multivariate Heterogeneous AutoRegression for High-Frequency data (MHAR-HF) to construct a dynamic partial correlation network of cryptocurrency exchange. The dynamic takes cryptocurrency stylized facts into account and capture events in cryptocurrency market.
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PhD candidate in Southwestern University of Finance and Economics, visiting PhD in Humboldt University of Berlin