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arXiv · 2512.13841

Parameter Estimation for Partially Observed Stable Continuous-State Branching Processes

Eduardo Gutiérrez-Peña·Carlos Octavio Pérez-Mendoza·Alan Riva Palacio·Arno Siri-Jégousse
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Abstract

In this article, we present a novel inference framework for estimating the parameters of Continuous-State Branching Processes (CSBPs). We do so by leveraging their subordinator representation. Our method reformulates the estimation problem by shifting the stochastic dynamics to the associated subordinator, enabling a parametric estimation procedure without requiring additional assumptions. This reformulation allows for efficient numerical recovery of the likelihood function via Laplace transform inversion, even in models where closed-form transition densities are unavailable. In addition to offering a flexible approach to parameter estimation, we propose a dynamic simulation framework that generates discrete-time trajectories of CSBPs using the same subordinator-based structure.

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Eduardo Gutiérrez-Peña, Carlos Octavio Pérez-Mendoza, Alan Riva Palacio, Arno Siri-Jégousse. 2025-12-15. Parameter Estimation for Partially Observed Stable Continuous-State Branching Processes. https://arxiv.org/abs/2512.13841

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