Stochastic Integrals and Abelian Processes
Claudio Albanese

TL;DR
This paper develops a triangulation scheme for joint kernels of diffusions and Abelian processes, providing a constructive approach to defining stochastic integrals and analyzing their properties via Fourier transform methods.
Contribution
It introduces a new triangulation-based method for analyzing joint kernels of diffusions and Abelian processes, extending classical theorems without probabilistic assumptions.
Findings
Convergence of Fourier transform of joint kernel in a uniform graph norm.
Smoothness properties of the Fourier transform of the joint kernel.
A constructive framework for defining stochastic integrals in the continuum.
Abstract
We study triangulation schemes for the joint kernel of a diffusion process with uniformly continuous coefficients and an adapted, non-resonant Abelian process. The prototypical example of Abelian process to which our methods apply is given by stochastic integrals with uniformly continuous coeffcients. The range of applicability includes also a broader class of processes of practical relevance, such as the sup process and certain discrete time summations we discuss. We discretize the space coordinate in uniform steps and assume that time is either continuous or finely discretized as in a fully explicit Euler method and the Courant condition is satisfied. We show that the Fourier transform of the joint kernel of a diffusion and a stochastic integral converges in a uniform graph norm associated to the Markov generator. Convergence also implies smoothness properties for the Fourier…
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Taxonomy
TopicsNeural Networks and Applications
