A Multi-level Monte Carlo simulation for invariant distribution of Markovian switching Lévy-driven SDEs with super-linearly growth coefficients

Fuente: arXiv
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Auteurs principaux: Nguyen, Hoang-Viet, Kieu, Trung-Thuy, Luong, Duc-Trong, Ngo, Hoang-Long, Khue, Tran Ngoc
Format: Preprint
Publié: 2024
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author Nguyen, Hoang-Viet
Kieu, Trung-Thuy
Luong, Duc-Trong
Ngo, Hoang-Long
Khue, Tran Ngoc
author_facet Nguyen, Hoang-Viet
Kieu, Trung-Thuy
Luong, Duc-Trong
Ngo, Hoang-Long
Khue, Tran Ngoc
contents This paper concerns the numerical approximation for the invariant distribution of Markovian switching Lévy-driven stochastic differential equations. By combining the tamed-adaptive Euler-Maruyama scheme with the Multi-level Monte Carlo method, we propose an approximation scheme that can be applied to stochastic differential equations with super-linear growth drift and diffusion coefficients.
format Preprint
id arxiv_https___arxiv_org_abs_2411_04081
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle A Multi-level Monte Carlo simulation for invariant distribution of Markovian switching Lévy-driven SDEs with super-linearly growth coefficients
Nguyen, Hoang-Viet
Kieu, Trung-Thuy
Luong, Duc-Trong
Ngo, Hoang-Long
Khue, Tran Ngoc
Probability
Numerical Analysis
This paper concerns the numerical approximation for the invariant distribution of Markovian switching Lévy-driven stochastic differential equations. By combining the tamed-adaptive Euler-Maruyama scheme with the Multi-level Monte Carlo method, we propose an approximation scheme that can be applied to stochastic differential equations with super-linear growth drift and diffusion coefficients.
title A Multi-level Monte Carlo simulation for invariant distribution of Markovian switching Lévy-driven SDEs with super-linearly growth coefficients
topic Probability
Numerical Analysis
url https://arxiv.org/abs/2411.04081