Home > Research > Publications & Outputs > Parameter estimation with increased precision f...
View graph of relations

Parameter estimation with increased precision for elliptic and hypo-elliptic diffusions

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Published

Standard

Parameter estimation with increased precision for elliptic and hypo-elliptic diffusions. / Iguchi, Y.; Beskos, A.; Graham, M.
In: Bernoulli, Vol. 31, No. 1, 01.02.2025, p. 333-358.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

APA

Vancouver

Iguchi Y, Beskos A, Graham M. Parameter estimation with increased precision for elliptic and hypo-elliptic diffusions. Bernoulli. 2025 Feb 1;31(1):333-358. Epub 2024 Oct 30. doi: 10.3150/24-BEJ1730

Author

Iguchi, Y. ; Beskos, A. ; Graham, M. / Parameter estimation with increased precision for elliptic and hypo-elliptic diffusions. In: Bernoulli. 2025 ; Vol. 31, No. 1. pp. 333-358.

Bibtex

@article{7b6c8dc3f5ad41b2b61a594d542c0221,
title = "Parameter estimation with increased precision for elliptic and hypo-elliptic diffusions",
abstract = "This work aims at making a comprehensive contribution in the general area of parametric inference for discretely observed diffusion processes. Established approaches for likelihood-based estimation invoke a time-discretisation scheme for the approximation of the intractable transition dynamics of the Stochastic Differential Equation (SDE) model over finite time periods. The scheme is applied for a step-size δ > 0, that is either user-selected or determined by the data. Recent research has highlighted the critical effect of the choice of numerical scheme on the behaviour of derived parameter estimates in the setting of hypo-elliptic SDEs. In brief, in our work, first, we develop two weak second order sampling schemes (to cover both hypo-elliptic and elliptic SDEs) and produce a small time expansion for the density of the schemes to form a proxy for the true intractable SDE transition density. Then, we establish a collection of analytic results for likelihood-based parameter estimates obtained via the formed proxies, thus providing a theoretical framework that showcases advantages from the use of the developed methodology for SDE calibration. We present numerical results from carrying out classical or Bayesian inference, for both elliptic and hypo-elliptic SDEs.",
author = "Y. Iguchi and A. Beskos and M. Graham",
year = "2025",
month = feb,
day = "1",
doi = "10.3150/24-BEJ1730",
language = "English",
volume = "31",
pages = "333--358",
journal = "Bernoulli",
issn = "1350-7265",
publisher = "International Statistical Institute",
number = "1",

}

RIS

TY - JOUR

T1 - Parameter estimation with increased precision for elliptic and hypo-elliptic diffusions

AU - Iguchi, Y.

AU - Beskos, A.

AU - Graham, M.

PY - 2025/2/1

Y1 - 2025/2/1

N2 - This work aims at making a comprehensive contribution in the general area of parametric inference for discretely observed diffusion processes. Established approaches for likelihood-based estimation invoke a time-discretisation scheme for the approximation of the intractable transition dynamics of the Stochastic Differential Equation (SDE) model over finite time periods. The scheme is applied for a step-size δ > 0, that is either user-selected or determined by the data. Recent research has highlighted the critical effect of the choice of numerical scheme on the behaviour of derived parameter estimates in the setting of hypo-elliptic SDEs. In brief, in our work, first, we develop two weak second order sampling schemes (to cover both hypo-elliptic and elliptic SDEs) and produce a small time expansion for the density of the schemes to form a proxy for the true intractable SDE transition density. Then, we establish a collection of analytic results for likelihood-based parameter estimates obtained via the formed proxies, thus providing a theoretical framework that showcases advantages from the use of the developed methodology for SDE calibration. We present numerical results from carrying out classical or Bayesian inference, for both elliptic and hypo-elliptic SDEs.

AB - This work aims at making a comprehensive contribution in the general area of parametric inference for discretely observed diffusion processes. Established approaches for likelihood-based estimation invoke a time-discretisation scheme for the approximation of the intractable transition dynamics of the Stochastic Differential Equation (SDE) model over finite time periods. The scheme is applied for a step-size δ > 0, that is either user-selected or determined by the data. Recent research has highlighted the critical effect of the choice of numerical scheme on the behaviour of derived parameter estimates in the setting of hypo-elliptic SDEs. In brief, in our work, first, we develop two weak second order sampling schemes (to cover both hypo-elliptic and elliptic SDEs) and produce a small time expansion for the density of the schemes to form a proxy for the true intractable SDE transition density. Then, we establish a collection of analytic results for likelihood-based parameter estimates obtained via the formed proxies, thus providing a theoretical framework that showcases advantages from the use of the developed methodology for SDE calibration. We present numerical results from carrying out classical or Bayesian inference, for both elliptic and hypo-elliptic SDEs.

U2 - 10.3150/24-BEJ1730

DO - 10.3150/24-BEJ1730

M3 - Journal article

VL - 31

SP - 333

EP - 358

JO - Bernoulli

JF - Bernoulli

SN - 1350-7265

IS - 1

ER -