An analytic BPHZ theorem for regularity structures
Ajay Chandra, Martin Hairer

TL;DR
This paper establishes a comprehensive, automated framework for proving stochastic convergence of renormalized models in nonlinear stochastic PDEs using regularity structures, extending to non-Gaussian fields.
Contribution
It introduces a black box approach for stochastic estimates in regularity structures, incorporating multi-scale analysis and handling positive renormalizations and non-Gaussian noise.
Findings
The BPHZ lift is continuous in law for stationary fields with many moments.
The approach simplifies stochastic convergence proofs for a wide class of SPDEs.
It extends the theory to non-Gaussian driving fields.
Abstract
We prove a general theorem on the stochastic convergence of appropriately renormalized models arising from nonlinear stochastic PDEs. The theory of regularity structures gives a fairly automated framework for studying these problems but previous works had to expend significant effort to obtain these stochastic estimates in an ad-hoc manner. In contrast, the main result of this article operates as a black box which automatically produces these estimates for nearly all of the equations that fit within the scope of the theory of regularity structures. Our approach leverages multi-scale analysis strongly reminiscent to that used in constructive field theory, but with several significant twists. These come in particular from the presence of "positive renormalizations" caused by the recentering procedure proper to the theory of regularity structure, from the difference in the action of the…
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Taxonomy
TopicsStochastic processes and financial applications · Financial Risk and Volatility Modeling · Risk and Portfolio Optimization
