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Multivariate point processes are widely applied to model event-type data such as natural disasters, online message exchanges, financial transactions or neuronal spike trains. One very popular point process model in which the probability of…

Statistics Theory · Mathematics 2023-01-27 Deborah Sulem , Vincent Rivoirard , Judith Rousseau

We generalise the construction of multivariate Hawkes processes to a possibly infinite network of counting processes on a directed graph $\mathbb G$. The process is constructed as the solution to a system of Poisson driven stochastic…

Probability · Mathematics 2014-03-25 Sylvain Delattre , Nicolas Fournier , Marc Hoffmann

We present a Markov approximation for jump-diffusions whose jump part consists in a Hawkes process with intensity driven by a general (possibly non-monotone) kernel. Under minimal integrability conditions, the kernel can be approximated by…

Probability · Mathematics 2025-07-16 Mahmoud Khabou , Mehdi Talbi

In this paper we study the transition density and exponential ergodicity in total variation for an affine process on the canonical state space $\mathbb{R}_{\geq0}^{m}\times\mathbb{R}^{n}$. Under a H\"ormander-type condition for diffusion…

Probability · Mathematics 2020-06-18 Martin Friesen , Peng Jin , Jonas Kremer , Barbara Rüdiger

This paper presents a stochastic model motivated by the study of a virus-like evolving population with different mutation rates. This is a continuous time birth-death model: the birth processes are mutually-exciting Hawkes processes and the…

Probability · Mathematics 2026-03-10 Rahul Roy , Dharmaraja Selvamuthu , Paola Tardelli

Hawkes process is a simple point process that is self-exciting and has clustering effect. The intensity of this point process depends on its entire past history. It has wide applications in finance, neuroscience, social networks,…

Probability · Mathematics 2018-10-02 Xuefeng Gao , Lingjiong Zhu

We introduce a novel class of generative models based on piecewise deterministic Markov processes (PDMPs), a family of non-diffusive stochastic processes consisting of deterministic motion and random jumps at random times. Similarly to…

Machine Learning · Statistics 2024-11-06 Andrea Bertazzi , Dario Shariatian , Umut Simsekli , Eric Moulines , Alain Durmus

We consider a mean-variance portfolio selection problem in a financial market with contagion risk. The risky assets follow a jump-diffusion model, in which jumps are driven by a multivariate Hawkes process with mutual-excitation effect. The…

Mathematical Finance · Quantitative Finance 2021-10-19 Yang Shen , Bin Zou

We are concerned with the asymptotics of the Markov chain given by the post-jump locations of a certain piecewise-deterministic Markov process with a state-dependent jump intensity. We provide sufficient conditions for such a model to…

Probability · Mathematics 2024-03-26 Dawid Czapla , Joanna Kubieniec

Multivariate Hawkes processes are past-dependant point processes originally introduced to model excitation effects, later extended to a nonlinear framework to account for the opposite effect, known as inhibition. Motivated by applications…

Methodology · Statistics 2026-05-12 Sacha Quayle , Anna Bonnet , Maxime Sangnier

An extension of the Hawkes process, the Marked Hawkes process distinguishes itself by featuring variable jump size across each event, in contrast to the constant jump size observed in a Hawkes process without marks. While extensive…

Machine Learning · Statistics 2024-02-08 Sobin Joseph , Shashi Jain

We consider diffusion processes with a spatially varying diffusivity giving rise to anomalous diffusion. Such heterogeneous diffusion processes are analysed for the cases of exponential, power-law, and logarithmic dependencies of the…

Statistical Mechanics · Physics 2017-09-13 Andrey G. Cherstvy , Ralf Metzler

In this paper we find nonasymptotic exponential upper bounds for the deviation in the ergodic theorem for families of homogeneous Markov processes. We find some sufficient conditions for geometric ergodicity uniformly over a parametric…

Probability · Mathematics 2012-05-10 Leonid Galtchouk , Serguei Pergamenchtchikov

We study the ergodic control problem for a class of controlled jump diffusions driven by a compound Poisson process. This extends the results of [SIAM J. Control Optim. 57 (2019), no. 2, 1516-1540] to running costs that are not…

Optimization and Control · Mathematics 2021-01-01 Ari Arapostathis , Guodong Pang , Yi Zheng

In low temperature supercooled liquid, below the ideal mode coupling theory transition temperature, hopping and continuous diffusion are seen to coexist. We present a theory which incorporates interaction between the two processes and shows…

Statistical Mechanics · Physics 2008-07-08 Sarika Maitra Bhattacharyya , Biman Bagchi , Peter G. Wolynes

We consider a multidimensional time-homogeneous dynamical system and add a randomly perturbed time-dependent deterministic signal to some of its components, giving rise to a high-dimensional system of stochastic differential equations,…

Probability · Mathematics 2019-08-02 Simon Holbach

We consider an elliptic and time-inhomogeneous diffusion process with time-periodic coefficients evolving in a bounded domain of $\mathbb{R}^d$ with a smooth boundary. The process is killed when it hits the boundary of the domain (hard…

Probability · Mathematics 2016-03-22 Pierre Del Moral , Denis Villemonais

Dynamic jumps in the price and volatility of an asset are modelled using a joint Hawkes process in conjunction with a bivariate jump diffusion. A state space representation is used to link observed returns, plus nonparametric measures of…

Applications · Statistics 2016-03-10 Worapree Maneesoonthorn , Catherine S. Forbes , Gael M. Martin

We study the stochastic behavior of heterogeneous diffusion processes with the power-law dependence $D(x)\sim|x|^{\alpha}$ of the generalized diffusion coefficient encompassing sub- and superdiffusive anomalous diffusion. Based on…

Statistical Mechanics · Physics 2014-12-24 Andrey G. Cherstvy , Ralf Metzler

Cox processes model overdispersed point process data via a latent stochastic intensity, but both nonparametric estimation of the intensity model and posterior inference over intensity paths are typically intractable, relying on expensive…

Machine Learning · Computer Science 2026-03-02 Xinlong Du , Harsha Honnappa , Vinayak Rao