Nonparametric bayesian estimation of a hölder continuous diffusion coefficient

Shota Gugushvili, Frank van der Meulen, Moritz Schauer, Peter Spreij

Research output: Contribution to journalArticleAcademicpeer-review


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.

Original languageEnglish
Pages (from-to)537-559
Number of pages23
JournalBrazilian Journal of Probability and Statistics
Issue number3
Publication statusPublished - Aug 2020


  • Diffusion coefficient
  • Gaussian likelihood
  • Non-parametric Bayesian estimation
  • Posterior contraction rate
  • Pseudo-likelihood
  • Stochastic differential equation
  • Volatility


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