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

2017/09/19 by Maylis Varvenne, Varvenne, Maylis
Economics, Econometrics and Finance · Mathematics · #FOS: Mathematics #Financial Risk and Volatility Modeling #Fractional Differential Equations Solutions #Probability (math.PR) #Stochastic processes and financial applications

paper · doi:10.48550/arxiv.1709.06413

openalex publication_date 2017/09/19 · openalex created_date 2022/10/03 · openalex updated_date 2026/07/28

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). Whenthe 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. In this fractional setting, we also emphasize the significant dependence of the rate of convergence to equilibriumon the local behaviour of the covariance function of the Gaussian noise.

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