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The strong convergence of the semi-implicit Euler-Maruyama (EM) method for stochastic differential equations with non-linear coefficients driven by a class of L\'evy processes is investigated. The dependence of the convergence order of the…

Numerical Analysis · Mathematics 2023-11-21 Xiaotong Li , Wei Liu , Hongjiong Tian

We study the approximation of the ergodic measure of the following stochastic differential equation (SDE) on $\mathbb{R}^d$: \begin{eqnarray}\label{e:SDEE} d X_t &=& (b_1(X_t)+b_2(X_t)) d t+\sigma(X_t) d W_t, \end{eqnarray} where $W_t$ is a…

Probability · Mathematics 2023-01-24 Xinghu Jin , Wei Wang , Lihu Xu , Tusheng Zhang

This paper deals with asymptotic errors, limit theorems for errors between numerical and exact solutions of stochastic differential equation (SDE) driven by one-dimensional fractional Brownian motion (fBm). The Euler-Maruyama, higher-order…

Numerical Analysis · Mathematics 2024-10-01 Kento Ueda

With the rapid development of computational techniques and scientific tools, great progress of data-driven analysis has been made to extract governing laws of dynamical systems from data. Despite the wide occurrences of non-Gaussian…

Dynamical Systems · Mathematics 2022-10-12 Yubin Lu , Yang Li , Jinqiao Duan

We consider high frequency samples from ergodic L\'evy driven stochastic differential equation (SDE) with drift coefficient $a(x,\alpha)$ and scale coefficient $c(x,\gamma)$ involving unknown parameters $\alpha$ and $\gamma$. We suppose…

Statistics Theory · Mathematics 2016-01-12 Hiroki Masuda , Yuma Uehara

Obtaining reliable numerical simulations of turbulent fluids is a challenging problem in computational fluid mechanics. The Large Eddy Simulations (LES) models are efficient tools to approximate turbulent fluids and an important step in the…

Analysis of PDEs · Mathematics 2018-05-23 Luigi C. Berselli , Stefano Spirito

We address the problem of simulation and parameter inference for chemical reaction networks described by the chemical Langevin equation, a stochastic differential equation (SDE) representation of the dynamics of the chemical species. This…

Methodology · Statistics 2025-08-18 Petar Jovanovski , Andrew Golightly , Umberto Picchini , Massimiliano Tamborrino

In this paper, we consider stochastic differential equations whose drift coefficient is superlinearly growing and piece-wise continuous, and whose diffusion coefficient is superlinearly growing and locally H\"older continuous. We first…

Probability · Mathematics 2023-05-15 Minh-Thang Do , Hoang-Long Ngo , Nhat-An Pho

This paper establishes a discretization scheme for a large class of stochastic differential equations driven by a time-changed Brownian motion with drift, where the time change is given by a general inverse subordinator. The scheme involves…

Probability · Mathematics 2015-11-13 Ernest Jum , Kei Kobayashi

Consider a process satisfying a stochastic differential equation with unknown drift parameter, and suppose that discrete observations are given. It is known that a simple least squares estimator (LSE) can be consistent, but numerically…

Statistics Theory · Mathematics 2017-03-17 Yasutaka Shimizu

We construct an estimator of the L\'evy density of a pure jump L\'evy process, possibly of infinite variation, from the discrete observation of one trajectory at high frequency. The novelty of our procedure is that we directly estimate the…

Probability · Mathematics 2020-04-06 Céline Duval , Ester Mariucci

We establish the large deviation principle for the slow variables in slow-fast dynamical system driven by both Brownian noises and L\'evy noises. The fast variables evolve at much faster time scale than the slow variables, but they are…

Dynamical Systems · Mathematics 2022-11-22 Shenglan Yuan , René Schilling , Jinqiao Duan

Constructions of numerous approximate sampling algorithms are based on the well-known fact that certain Gibbs measures are stationary distributions of ergodic stochastic differential equations (SDEs) driven by the Brownian motion. However,…

Probability · Mathematics 2020-07-07 Lu-Jing Huang , Mateusz B. Majka , Jian Wang

We recently proposed a method for estimation of states and parameters in stochastic differential equations, which included intermediate time points between observations and used the Laplace approximation to integrate out these intermediate…

Probability · Mathematics 2025-04-01 Uffe Høgsbro Thygesen

This work develops Monte Carlo Euler adaptive time stepping methods for the weak approximation problem of jump diffusion driven stochastic differential equations. The main result is the derivation of a new expansion for the omputational…

Numerical Analysis · Mathematics 2007-05-23 E. Mordecki , A. Szepessy , R. Tempone , G. E. Zouraris

In this paper we extend existing results on the numerical approximation of one-dimensional SDEs with drift in a negative order Besov space and driven by Brownian motion. Using the Yamada-Watanabe approximation technique, we prove rates in…

Probability · Mathematics 2026-02-03 Matteo Cagnotti

We study the discrete-time approximation for solutions of quadratic forward back- ward stochastic differential equations (FBSDEs) driven by a Brownian motion and a jump process which could be dependent. Assuming that the generator has a…

Optimization and Control · Mathematics 2012-11-28 Idris Kharroubi , Thomas Lim

We investigate existence, uniqueness and approximation of solutions to stochastic delay differential equations (SDDEs) under Carath\'eodory-type drift coefficients. Moreover, we also assume that both drift $f=f(t,x,z)$ and diffusion…

Numerical Analysis · Mathematics 2023-06-16 Paweł Przybyłowicz , Yue Wu , Xinheng Xie

Motivated by the multilevel Monte Carlo method introduced by Giles [5], we study the asymptotic behavior of the normalized error process $u_{n,m}(X^n-X^{nm})$ where $X^n$ and $X^{nm}$ are respectively Euler approximations with time steps…

Probability · Mathematics 2021-04-29 Mohamed Ben Alaya , Ahmed Kebaier , Thi Bao Tram Ngo

The LIBOR market model is very popular for pricing interest rate derivatives, but is known to have several pitfalls. In addition, if the model is driven by a jump process, then the complexity of the drift term is growing exponentially fast…

Computational Finance · Quantitative Finance 2015-03-19 Antonis Papapantoleon , John Schoenmakers , David Skovmand
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