Nonparametric Bayesian estimation of a Hölder continuous diffusion coefficient
Artikel i vetenskaplig tidskrift, 2020

We consider a nonparametric Bayesian approach to estimate the diffusion coefficient of a stochastic differential equation given discrete time observations over a fixed time interval. As a prior on the diffusion coefficient, we employ a histogram-type prior with piecewise constant realisations on bins forming a partition of the time interval. Specifically, these constants are realizations of independent inverse Gamma distributed randoma variables. We justify our approach by deriving the rate at which the corresponding posterior distribution asymptotically concentrates around the data-generating diffusion coefficient. This posterior contraction rate turns out to be optimal for estimation of a Hölder-continuous diffusion coefficient with smoothness parameter 0<λ≤1. Our approach is straightforward to implement, as the posterior distributions turn out to be inverse Gamma again, and leads to good practical results in a wide range of simulation examples. Finally, we apply our method on exchange rate data sets.

posterior contraction rate

stochastic differential equation

Diffusion coefficient


Gaussian likelihood

non-parametric Bayesian estimation


Shota Gugushvili

Wageningen University and Research

Frank van der Meulen

TU Delft

Moritz Schauer

Universiteit Leiden

Peter Spreij

Radboud Universiteit

Universiteit Van Amsterdam

Brazilian Journal of Probability and Statistics

0103-0752 (ISSN)

Vol. 34 3 537-579


Grundläggande vetenskaper


Sannolikhetsteori och statistik



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