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This paper proposes a new approach for change point detection in causalnetworks of multivariate Hawkes processes using Frechet statistics. Our methodsplits the point process into overlapping windows, estimates kernel matrices ineach window, and reconstructs the signed Laplacians by treating the kernelmatrices as the adjacency matrices of the causal network. We demonstrate theeffectiveness of our method through experiments on both simulated andreal-world cryptocurrency datasets. Our results show that our method is capableof accurately detecting and characterizing changes in the causal structure ofmultivariate Hawkes processes, and may have potential applications in fieldssuch as finance and neuroscience. The proposed method is an extension ofprevious work on Frechet statistics in point process settings and represents animportant contribution to the field of change point detection in multivariatepoint processes.

Fr\'echet Statistics Based Change Point Detection in Multivariate Hawkes Process