Cumulants of Hawkes point processes
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Abstract (arXiv)
We derive explicit, closed-form expressions for the cumulant densities of a multivariate, self-exciting Hawkes point process, generalizing a result of Hawkes in his earlier work on the covariance density and Bartlett spectrum of such processes. To do this, we represent the Hawkes process in terms of a Poisson cluster process and show how the cumulant density formulas can be derived by enumerating all possible "family trees", representing complex interactions between point events. We also consider the problem of computing the integrated cumulants, characterizing the average measure of correlated activity between events of different types, and derive the relevant equations.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Sep 2014.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 0; 2025: 2; 2026: 1.
5 papers.
- 2025Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approachescited by 2
- 2026Causal Representation Meets Stochastic Modeling under Generic Geometrycited by 2
- 2021An algorithm for the computation of joint Hawkes moments with exponential kernelcited by 0
- 2023Detecting Structural Shifts in Multivariate Hawkes Processes with Fr'echet Statisticscited by 0
- 2025Causal Structure Learning in Hawkes Processes with Complex Latent Confounder Networkscited by 0
- 2026Causal Representation Meets Stochastic Modeling under Generic Geometry4 Feb 2026
- 2025Causal Structure Learning in Hawkes Processes with Complex Latent Confounder Networks15 Aug 2025
- 2025Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches24 Jan 2025
- 2023Detecting Structural Shifts in Multivariate Hawkes Processes with Fr'echet Statistics13 Aug 2023
- 2021An algorithm for the computation of joint Hawkes moments with exponential kernel25 Oct 2021