Search arXiv⌕ Search

arXiv · 2610.36473

Risk measures for infinite-server queues driven by a Cox-BESQ process

Abstract

This paper introduces a model for infinite-server queues driven by a Cox process with stochastic intensity given by a squared Bessel (BESQ) process. Exploiting the affine structure of the underlying dynamics, we derive a closed-form expression for the joint moment-generating function (MGF) of the arrival intensity, the number of customers in the system, and the integral of the number of customers in the system over a time interval. This MGF can also be obtained at two (or more) dates, thereby allowing an analysis of the time evolution of infinite-server queues driven by a Cox--BESQ process. The availability of these explicit MGFs enables the development of risk measures for these variables, such as the joint moments of the arrival intensity, the number of customers in the system, and the integral of the number of customers in the system over a time interval, conditional on one of these variables exceeding a critical threshold. These risk measures can be determined at one or two dates. They rely essentially on the explicit form of the MGF of the state variables and on Fourier representations of the risk measures. Numerical experiments demonstrate the practical implementation of the proposed methodology and highlight the relevance of the theoretical results.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

José Da Fonseca, Yajun Shi, Wenjun Zhang. 2026-10-27. Risk measures for infinite-server queues driven by a Cox-BESQ process. https://arxiv.org/abs/2610.36473

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Decay of correlations and thermodynamic limit for the circular Riesz gas

We study the circular Riesz gas, a one-dimensional system of particles with power-law repulsion. For the long-range gas, we prove that the correlation between two gaps at distance $d$ decays like $d^{s-2}$, up to an arbitrarily small loss in the exponent, and that this exponent is sharp. As a consequence, the microscopic point process converges as the number of particles tends to infinity, and its limit is hyperuniform. We also prove the decay of correlations and the convergence of the microscopic point process for the hypersingular gas. The proof analyzes the Helffer-Sjöstrand equation, an elliptic system in high dimension that encodes covariances. It combines the deformation of Combes and Thomas with discrete elliptic regularity estimates and a factorization around the equally spaced configuration.

math.PR↗

A Central Limit Theorem for High-Temperature SYK Model: A case study on mathematical research using AI and Lean

The Sachdev--Ye--Kitaev (SYK) model is a quantum many-body model of Majorana fermions with all-to-all random interactions. It has attracted wide interest in condensed matter and high-energy physics as a solvable model of non-Fermi liquids and holography. Fix an even integer $q\geq4$, and let $Z_N(β)$ be the partition function of the Gaussian $q$-body SYK model. We prove that for all sufficiently small inverse temperatures $β> 0$, there exists $ρ=ρ(β)>0$ such that \[ N^{q/2 -1}(\log Z_N(β)-\mathbb{E}[\log Z_N(β)]) \Longrightarrow \mathcal{N}(0,ρ^2). \] In addition to the mathematical results, we discuss how the proofs were developed and refined through interaction between human researchers and AI, and we describe the subsequent formalization of the main arguments in Lean 4. This work thus serves as a case study of a research workflow that integrates human insight, AI-assisted exploration, iterative proof refinement, and formal verification.

math.PR↗

A sharp survival threshold for branching Brownian motion in a self-generated resource environment

We study branching Brownian motion in a resource environment created by the particles themselves. Particles reproduce at rate $β$ times the resource nearby, resource is consumed at rate $c$ times the number of particles nearby, and each death returns resource mass $m$. Let $θ=βm/c$, which represents the number of births supported by resource from one corpse, assuming perfect efficiency. We prove that the population dies out almost surely if $θ\leq1$, even with perfect recycling $\left(θ=1\right)$. If $θ>1$ and the interaction kernel has finite range and is bounded below at short distances, the population survives with positive probability from every initial state with at least two particles, or with one particle and some resource. The threshold does not depend on the diffusivity, the death rate or the interaction kernel. We prove extinction for $θ\leq 1$ by showing the total mass in the system to be a supermartingale. The proof of survival rests on two estimates: a large colony grows if it consumes its corpses with little waste, and a dense colony recycles corpses efficiently. Together these imply that a large, dense colony grows with exponentially high probability, and a standard doubling and restart argument then yields survival.

math.PR↗