Results 111 to 120 of about 932 (203)
On a high-dimensional nonlinear stochastic partial differential equation
18 pagesIn this paper we investigate a nonlinear stochastic partial differential equation (spde in short) perturbed by a space-correlated Gaussian noise in arbitrary dimension, with a non-Lipschitz coefficient noisy term.
Mellouk, Mohamed, Boulanba, Lahcen
core
Recently, Hairer et. al (2012) showed that there exist SDEs with infinitely often differentiable and globally bounded coefficient functions whose solutions fail to be locally Lipschitz continuous in the strong L^p-sense with respect to the initial value ...
Cox, Sonja +2 more
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Comparison theorems for solutions of one-dimensional backward stochastic differential equations were established by Peng and Cao-Yan, where the coefficients were, respectively, required to be Lipschitz and Dini continuous. In this work, we generalize the
Liu, Jicheng, Ren, Jiagang
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On Solutions of Backward Stochastic Volterra Integral Equations with Jumps in Hilbert Spaces
This paper studies the existence, uniqueness and stability of the adapted solutions to backward stochastic Volterra integral equations (BSVIEs) driven by a cylindrical Brownian motion on a separable Hilbert space and a Poisson random measure with non ...
Ren, Y (15524684)
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Nonlinear fractional stochastic delay modeling and computational analysis of herpes simplex virus type II dynamics. [PDF]
Raza A +3 more
europepmc +1 more source
Beyond diagonal noise: A better predator-prey modeling framework with cross-covariance. [PDF]
Yu J, Wang LS.
europepmc +1 more source
Square integrable solutions and stability of a second-order stochastic integro-differential equation. [PDF]
Oudjedi-Damerdji LF +4 more
europepmc +1 more source
S-shaped Utility Maximization with VaR Constraint and Partial Information. [PDF]
Zhu D, Davey A, Zheng H.
europepmc +1 more source
The Euler scheme is one of the standard schemes to obtain numerical approximations of stochastic differential equations (SDEs). Its convergence properties are well-known in the case of globally Lipschitz continuous coefficients.
Göttlich, Simone +2 more
core
Random Neural Networks for Rough Volatility. [PDF]
Jacquier A, Žurič Ž.
europepmc +1 more source

