1-stable fluctuations in branching Brownian motion at critical temperature II: general functionals
Pascal Maillard, Michel Pain

TL;DR
This paper investigates the fluctuations of the critical derivative Gibbs measure in branching Brownian motion, revealing stable distribution limits and identifying the particles responsible for these fluctuations, thus confirming a physics conjecture.
Contribution
It provides a detailed analysis of the fluctuations in the convergence of the derivative Gibbs measure, establishing stable law limits and extending results to functional convergence.
Findings
Fluctuations converge to a 1-stable distribution conditioned on the limit measure.
The critical additive martingale fluctuations converge to a Cauchy distribution.
Identifies particles responsible for the fluctuations in the measure.
Abstract
Let denote the critical derivative Gibbs measure of branching Brownian motion at time . It has been proved by Madaule (Stochastic Process. Appl. 126 (2016), no. 2, 470--502) and Maillard and Zeitouni (Ann. Inst. Henri Poincar\'e Probab. Stat. 52 (2016), no. 3, 1144--1160) that converges weakly to the random measure , where is the limit of the derivative martingale. In this paper, we are interested in the fluctuations that occur in this convergence and prove for a large class of functions that \begin{align*} \sqrt{t} \left( \int_{\mathbb R} F d \mu_t - Z_\infty \int_0^\infty F(x) \sqrt{\frac{2}{\pi}} x^2 e^{-x^2/2} d x - \frac{c(F) \log t}{\sqrt{t}} Z_\infty \right) \to S(F), \end{align*} in law, as , where is a constant depending on and, given ,…
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Taxonomy
TopicsStochastic processes and statistical mechanics · Stochastic processes and financial applications · Financial Risk and Volatility Modeling
