Bernoulli

  • Bernoulli
  • Volume 25, Number 4B (2019), 3234-3275.

Rate of convergence to equilibrium for discrete-time stochastic dynamics with memory

Maylis Varvenne

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Abstract

The main objective of the paper is to study the long-time behavior of general discrete dynamics driven by an ergodic stationary Gaussian noise. In our main result, we prove existence and uniqueness of the invariant distribution and exhibit some upper-bounds on the rate of convergence to equilibrium in terms of the asymptotic behavior of the covariance function of the Gaussian noise (or equivalently to its moving average representation). Then, we apply our general results to fractional dynamics (including the Euler Scheme associated to fractional driven Stochastic Differential Equations). When the Hurst parameter $H$ belongs to $(0,1/2)$ we retrieve, with a slightly more explicit approach due to the discrete-time setting, the rate exhibited by Hairer in a continuous time setting (Ann. Probab. 33 (2005) 703–758). In this fractional setting, we also emphasize the significant dependence of the rate of convergence to equilibrium on the local behaviour of the covariance function of the Gaussian noise.

Article information

Source
Bernoulli, Volume 25, Number 4B (2019), 3234-3275.

Dates
Received: November 2017
Revised: November 2018
First available in Project Euclid: 25 September 2019

Permanent link to this document
https://projecteuclid.org/euclid.bj/1569398765

Digital Object Identifier
doi:10.3150/18-BEJ1089

Mathematical Reviews number (MathSciNet)
MR4010954

Zentralblatt MATH identifier
07110137

Keywords
discrete stochastic dynamics Lyapunov function rate of convergence to equilibrium stationary Gaussian noise Toeplitz operator total variation distance

Citation

Varvenne, Maylis. Rate of convergence to equilibrium for discrete-time stochastic dynamics with memory. Bernoulli 25 (2019), no. 4B, 3234--3275. doi:10.3150/18-BEJ1089. https://projecteuclid.org/euclid.bj/1569398765


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Supplemental materials

  • Supplement to “Rate of convergence to equilibrium for discrete-time stochastic dynamics with memory”. We provide proofs of a few selected results from this paper.