Publication

An extension of the stochastic sewing lemma and applications to fractional stochastic calculus

Toyomu Matsuda
2024
Journal Articles

Abstract

We give an extension of Le's stochastic sewing lemma. The stochastic sewing lemma proves convergence in $L_m$ of Riemann type sums $\sum _{[s,t] \in \pi } A_{s,t}$ for an adapted two-parameter stochastic process A, under certain conditions on the moments of $A_{s,t}$ and of conditional expectations of $A_{s,t}$ given $\mathcal F_s$ . Our extension replaces the conditional expectation given $\mathcal F_s$ by that given $\mathcal F_v$ for $v

Official source

https://infoscience.epfl.ch/entities/publication/fe3b9961-2352-451c-a3bd-12db631507b1

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Toyomu Matsuda
2024
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/fe3b9961-2352-451c-a3bd-12db631507b1

About this result

Related publications (10)

An extension of the stochastic sewing lemma and applications to fractional stochastic calculus

Chat with Graph Search

Quantifying uncertain system outputs via the multi-level Monte Carlo method --- distribution and robustness measures

Differential Entropy of the Conditional Expectation Under Additive Gaussian Noise

Differential Entropy of the Conditional Expectation under Gaussian Noise