Partial differential equations, Probability theory
The averaging principle for stochastic differential equations driven by a Wiener process revisited
Comptes Rendus. Mathématique, Volume 360 (2022) no. G3, pp. 265-273.

We consider a one-dimensional stochastic differential equation driven by a Wiener process, where the diffusion coefficient depends on an ergodic fast process. The averaging principle is satisfied: it is well-known that the slow component converges in distribution to the solution of an averaged equation, with generator determined by averaging the square of the diffusion coefficient.

We propose a new version of the averaging principle, where the solution is interpreted as the sum of two terms: one depending on the average of the diffusion coefficient, the other giving fluctuations around that average. Both the average and fluctuation terms contribute to the limit, which illustrates why it is required to average the square of the diffusion coefficient to find the limit behavior.

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DOI: 10.5802/crmath.297
Classification: 60H10
Bréhier, Charles-Edouard 1

1 Univ Lyon, Université Claude Bernard Lyon 1, CNRS UMR 5208, Institut Camille Jordan, 43 blvd. du 11 novembre 1918, 69622 Villeurbanne cedex, France
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Bréhier, Charles-Edouard. The averaging principle for stochastic differential equations driven by a Wiener process revisited. Comptes Rendus. Mathématique, Volume 360 (2022) no. G3, pp. 265-273. doi : 10.5802/crmath.297. http://www.numdam.org/articles/10.5802/crmath.297/

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