arXiv subjects

Joshué Helí Ricalde-Guerrero

Publications and source records attributed to Joshué Helí Ricalde-Guerrero.

6 recordsLinked to original sources

arXiv2026

Stochastic Optimal Control of Hawkes Jump-Diffusion Systems

This paper is devoted to developing a framework for stochastic growth models with environmental risk, in which rare but catastrophic shocks interact with capital accumulation and pollution. Building on the Poisson point process formulation studied in arXiv:2511.13568, we extend the model to disasters driven by a marked Hawkes process, allowing past disasters to temporarily increase the likelihood of subsequent shocks. Our work focuses on a subcritical Markovian Hawkes specification, in which the state space is augmented by the self-excitation component of disaster risk. We establish the well-posedness and nonexplosion of the resulting controlled Hawkes dynamics. Using the Hamilton-Jacobi-Bellman characterization of the corresponding Poisson control problem, we obtain quantitative estimates for the Hawkes excitation process and prove, under a small-excitation scaling, that the Hawkes value function converges to its Poisson counterpart as the magnitude of self-excitation vanishes. This provides a rigorous Poisson approximation of the stochastic control problem and quantifies the effect of self-excitation on optimal growth under environmental disaster risk.

math.OC↗

arXiv2026

Infinite-Horizon Optimal Control of Jump-Diffusion Models for Pollution-Dependent Disasters

This paper is devoted to developing a unified framework for stochastic growth models with environmental risk, in which rare but catastrophic shocks interact with capital accumulation and pollution. The analysis is based upon a general Poisson point process formulation, leading to non-local Hamilton-Jacobi-Bellman (HJB) equations that admit closed-form candidate solutions and yield a composite state variable capturing exposure to rare shocks. We consider cases where disaster risk is endogenized through a pollution-dependent intensity and, in the more general cases, it also accommodates for state-dependent events of varying magnitude. Our formulation captures how environmental degradation amplifies macroeconomic vulnerability and strengthens incentives for abatement. From a technical perspective, it provides tractable jump-diffusion control problems whose HJB equation decomposes naturally into capital and pollution components under power-type value function.

math.OC↗

arXiv2025

Branching Stein Variational Gradient Descent for sampling multimodal distributions

We propose a novel particle-based variational inference method designed to work with multimodal distributions. Our approach, referred to as Branched Stein Variational Gradient Descent (BSVGD), extends the classical Stein Variational Gradient Descent (SVGD) algorithm by incorporating a random branching mechanism that encourages the exploration of the state space. In this work, a theoretical guarantee for the convergence in distribution is presented, as well as numerical experiments to validate the suitability of our algorithm. Performance comparisons between the BSVGD and the SVGD are presented using the Wasserstein distance between samples and the corresponding computational times.

cs.LG↗

arXiv2024

Conditional McKean-Vlasov Differential Equations with Common Poissonian Noise: Propagation of Chaos

A model for the evolution of a large population interacting system is considered in which a marked Poisson processes influences their evolution, together with a Brownian motion. Mean field McKean-Vlasov limits of such system are formulated studying first both systems individually. Letting the population size growing to infinite, the weak convergence of the solutions of such systems is proved; in other words, propagation of chaos of such systems is obtained.

math.PR↗

arXiv2024

Mean-Field Games with common Poissonian noise: a Maximum Principle approach

The theory of Mean-Field Games is interested in the behaviour of interacting particle systems in which the individual interaction between particles (players) decreases as the size of the population increases. In recent years, it was introduced an interesting structure for this type of games, assuming a correlated continuous source of randomness, which are called Mean-Field Games with Common Noise. In this paper, we extend this concept and provide a precise definition of a Mean-Field Game with Common Poissoninan Noise and its equilibrium. That is, a common self-exciting Poissonian structure is considered within the dynamics of the population. Then, we address the problem of optimization for jump-diffusions with random environments that lies within the definition of the MFG, and develop a stochastic version of the Pontryagin's Maximum Principle to obtain a set of necessary conditions that an optimal control must satisfy. Under additional convexity assumptions it is also shown that these conditions are also sufficient.

math.OC↗

arXiv2024

Coupled forward-backward stochastic differential equations with jumps in random environments

In this paper we obtain results for the existence and uniqueness of solutions to coupled Forward-Backward Stochastic Differential Equations (FBSDEs) with jumps defined on a random environment. This environment corresponds to a measured-valued process, similar to the one found in Conditional McKean-Vlasov Differential Equations and Mean-Field Games with Common Noise. The jump term in the FBSDE is dependent on the environment through a stochastic intensity process. We provide examples which relate our model with FBSDEs driven by Cox and Hawkes processes, as well as regime-switching Conditional McKean-Vlasov differential equations.

math.PR↗