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Quasi-likelihood analysis for marked point processes and application to marked Hawkes processes

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  • Simon Clinet

Abstract

We develop a quasi-likelihood analysis procedure for a general class of multivariate marked point processes. As a by-product of the general method, we establish under stability and ergodicity conditions the local asymptotic normality of the quasi-log likelihood, along with the convergence of moments of quasi-likelihood and quasi-Bayesian estimators. To illustrate the general approach, we then turn our attention to a class of multivariate marked Hawkes processes with generalized exponential kernels, comprising among others the so-called Erlang kernels. We provide explicit conditions on the kernel functions and the mark dynamics under which a certain transformation of the original process is Markovian and $V$-geometrically ergodic. We finally prove that the latter result, which is of interest in its own right, constitutes the key ingredient to show that the generalized exponential Hawkes process falls under the scope of application of the quasi-likelihood analysis.

Suggested Citation

  • Simon Clinet, 2020. "Quasi-likelihood analysis for marked point processes and application to marked Hawkes processes," Papers 2001.11624, arXiv.org, revised Aug 2021.
    • Handle: RePEc:arx:papers:2001.11624
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    References listed on IDEAS

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    1. P. Blanc & J. Donier & J.-P. Bouchaud, 2017. "Quadratic Hawkes processes for financial prices," Quantitative Finance, Taylor & Francis Journals, vol. 17(2), pages 171-188, February.
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    Cited by:

    1. Masatoshi Goda, 2023. "Sparse estimation for generalized exponential marked Hawkes process," Statistical Inference for Stochastic Processes, Springer, vol. 26(1), pages 139-169, April.

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