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August 2020 Nonparametric Bayesian estimation of a Hölder continuous diffusion coefficient
Shota Gugushvili, Frank van der Meulen, Moritz Schauer, Peter Spreij
Braz. J. Probab. Stat. 34(3): 537-579 (August 2020). DOI: 10.1214/19-BJPS433

Abstract

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 $0<\lambda \leq 1$. 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.

Citation

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Shota Gugushvili. Frank van der Meulen. Moritz Schauer. Peter Spreij. "Nonparametric Bayesian estimation of a Hölder continuous diffusion coefficient." Braz. J. Probab. Stat. 34 (3) 537 - 579, August 2020. https://doi.org/10.1214/19-BJPS433

Information

Received: 1 July 2018; Accepted: 1 January 2019; Published: August 2020
First available in Project Euclid: 20 July 2020

zbMATH: 07232912
MathSciNet: MR4124540
Digital Object Identifier: 10.1214/19-BJPS433

Rights: Copyright © 2020 Brazilian Statistical Association

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Vol.34 • No. 3 • August 2020
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