Averaging principle for slow-fast stochastic differential equations with time dependent locally Lipschitz coefficients

By Wei Liu, Michael Röckner, Xiaobin Sun, Yingchao Xie. 2018

123 citations recorded by the source corpus.

This paper is devoted to studying the averaging principle for stochastic differential equations with slow and fast time-scales, where the drift coefficients satisfy local Lipschitz conditions with respect to the slow and fast variables, and the coefficients in the slow equation depend on time $t$ and $\omega$. Making use of the techniques of time discretization and truncation, we prove that the slow component strongly converges to the solution of the corresponding averaged equation.

Subfields: Finance, Modeling and Simulation, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/d7c14080747e2a796bcb9d52764db805a607bef9

Citations in the corpus

• Strong convergence order for slow–fast McKean–Vlasov stochastic differential equations (2019, unknown): “, [19, 20, 21, 24, 26, 38] for further developments.”
• Averaging principle for fast-slow system driven by mixed fractional Brownian rough path (2020, unknown): “[6, 7, 30, 31, 32, 33, 19] and the references therein.”