Estimation of several parameters in discretely-observed stochastic differential equations with additive fractional noise

El Mehdi Haress () and Alexandre Richard ()
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El Mehdi Haress: Université Paris-Saclay
Alexandre Richard: Université Paris-Saclay

Statistical Inference for Stochastic Processes, 2024, vol. 27, issue 3, No 4, 691 pages

Abstract: Abstract We investigate the problem of joint statistical estimation of several parameters for a stochastic differential equation driven by an additive fractional Brownian motion. Based on discrete-time observations of the model, we construct an estimator of the Hurst parameter, the diffusion parameter and the drift, which lies in a parametrised family of coercive drift coefficients. Our procedure is based on the assumption that the stationary distribution of the SDE and of its increments permits to identify the parameters of the model. Under this assumption, we prove consistency results and derive a rate of convergence for the estimator. Finally, we show that the identifiability assumption is satisfied in the case of a family of fractional Ornstein–Uhlenbeck processes and illustrate our results with some numerical experiments.

Keywords: Fractional Brownian motion; Parametric estimation; Ergodicity; 60H10; 60G22; 60G10; 62F12; 37M25 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11203-024-09311-8

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