Weak error approximation for rough and Gaussian mean-reverting stochastic volatility models

Fuente: arXiv
Saved in:
Bibliographic Details
Main Authors: Alfonsi, Aurélien, Kebaier, Ahmed
Format: Preprint
Published: 2026
Subjects:
Online Access:
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866911459429056512
author Alfonsi, Aurélien
Kebaier, Ahmed
author_facet Alfonsi, Aurélien
Kebaier, Ahmed
contents For a class of stochastic models with Gaussian and rough mean-reverting volatility that embeds the genuine rough Stein-Stein model, we study the weak approximation rate when using a Euler type scheme with integrated kernels. Our first result is a weak convergence rate for the discretised rough Ornstein-Uhlenbeck process, that is essentially in $\min(3α-1,1)$, where $\frac{t^{α-1}}{Γ(α)} $ is the fractional convolution kernel with $α\in (1/2,1)$. Then, our main result is to obtain the same convergence rate for the corresponding stochastic rough volatility model with polynomial test functions.
format Preprint
id arxiv_https___arxiv_org_abs_2602_18234
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Weak error approximation for rough and Gaussian mean-reverting stochastic volatility models
Alfonsi, Aurélien
Kebaier, Ahmed
Probability
Computational Finance
60H35 60G22 60L90 91G60
For a class of stochastic models with Gaussian and rough mean-reverting volatility that embeds the genuine rough Stein-Stein model, we study the weak approximation rate when using a Euler type scheme with integrated kernels. Our first result is a weak convergence rate for the discretised rough Ornstein-Uhlenbeck process, that is essentially in $\min(3α-1,1)$, where $\frac{t^{α-1}}{Γ(α)} $ is the fractional convolution kernel with $α\in (1/2,1)$. Then, our main result is to obtain the same convergence rate for the corresponding stochastic rough volatility model with polynomial test functions.
title Weak error approximation for rough and Gaussian mean-reverting stochastic volatility models
topic Probability
Computational Finance
60H35 60G22 60L90 91G60
url https://arxiv.org/abs/2602.18234