Home > Research > Publications & Outputs > Bayesian inference for non-Gaussian Ornstein–Uh...

Research

Links

Text available via DOI:

View graph of relations

Bayesian inference for non-Gaussian Ornstein–Uhlenbeck stochastic volatility processes.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Published

Standard

Bayesian inference for non-Gaussian Ornstein–Uhlenbeck stochastic volatility processes. / Roberts, Gareth O.; Papaspiliopoulos, Omiros; Dellaportas, Petros.
In: Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 66, No. 2, 05.2004, p. 369-393.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Roberts, GO, Papaspiliopoulos, O & Dellaportas, P 2004, 'Bayesian inference for non-Gaussian Ornstein–Uhlenbeck stochastic volatility processes.', Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 66, no. 2, pp. 369-393. https://doi.org/10.1111/j.1369-7412.2004.05139.x

APA

Roberts, G. O., Papaspiliopoulos, O., & Dellaportas, P. (2004). Bayesian inference for non-Gaussian Ornstein–Uhlenbeck stochastic volatility processes. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 66(2), 369-393. https://doi.org/10.1111/j.1369-7412.2004.05139.x

Vancouver

Roberts GO, Papaspiliopoulos O, Dellaportas P. Bayesian inference for non-Gaussian Ornstein–Uhlenbeck stochastic volatility processes. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2004 May;66(2):369-393. doi: 10.1111/j.1369-7412.2004.05139.x

Author

Roberts, Gareth O. ; Papaspiliopoulos, Omiros ; Dellaportas, Petros. / Bayesian inference for non-Gaussian Ornstein–Uhlenbeck stochastic volatility processes. In: Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2004 ; Vol. 66, No. 2. pp. 369-393.

Bibtex

@article{f986a51b692a44feb7506e0a5ee701d1,
title = "Bayesian inference for non-Gaussian Ornstein–Uhlenbeck stochastic volatility processes.",
abstract = "We develop Markov chain Monte Carlo methodology for Bayesian inference for non-Gaussian Ornstein–Uhlenbeck stochastic volatility processes. The approach introduced involves expressing the unobserved stochastic volatility process in terms of a suitable marked Poisson process. We introduce two specific classes of Metropolis–Hastings algorithms which correspond to different ways of jointly parameterizing the marked point process and the model parameters. The performance of the methods is investigated for different types of simulated data. The approach is extended to consider the case where the volatility process is expressed as a superposition of Ornstein–Uhlenbeck processes. We apply our methodology to the US dollar–Deutschmark exchange rate.",
author = "Roberts, {Gareth O.} and Omiros Papaspiliopoulos and Petros Dellaportas",
year = "2004",
month = may,
doi = "10.1111/j.1369-7412.2004.05139.x",
language = "English",
volume = "66",
pages = "369--393",
journal = "Journal of the Royal Statistical Society: Series B (Statistical Methodology)",
issn = "1369-7412",
publisher = "Wiley-Blackwell",
number = "2",

}

RIS

TY - JOUR

T1 - Bayesian inference for non-Gaussian Ornstein–Uhlenbeck stochastic volatility processes.

AU - Roberts, Gareth O.

AU - Papaspiliopoulos, Omiros

AU - Dellaportas, Petros

PY - 2004/5

Y1 - 2004/5

N2 - We develop Markov chain Monte Carlo methodology for Bayesian inference for non-Gaussian Ornstein–Uhlenbeck stochastic volatility processes. The approach introduced involves expressing the unobserved stochastic volatility process in terms of a suitable marked Poisson process. We introduce two specific classes of Metropolis–Hastings algorithms which correspond to different ways of jointly parameterizing the marked point process and the model parameters. The performance of the methods is investigated for different types of simulated data. The approach is extended to consider the case where the volatility process is expressed as a superposition of Ornstein–Uhlenbeck processes. We apply our methodology to the US dollar–Deutschmark exchange rate.

AB - We develop Markov chain Monte Carlo methodology for Bayesian inference for non-Gaussian Ornstein–Uhlenbeck stochastic volatility processes. The approach introduced involves expressing the unobserved stochastic volatility process in terms of a suitable marked Poisson process. We introduce two specific classes of Metropolis–Hastings algorithms which correspond to different ways of jointly parameterizing the marked point process and the model parameters. The performance of the methods is investigated for different types of simulated data. The approach is extended to consider the case where the volatility process is expressed as a superposition of Ornstein–Uhlenbeck processes. We apply our methodology to the US dollar–Deutschmark exchange rate.

U2 - 10.1111/j.1369-7412.2004.05139.x

DO - 10.1111/j.1369-7412.2004.05139.x

M3 - Journal article

VL - 66

SP - 369

EP - 393

JO - Journal of the Royal Statistical Society: Series B (Statistical Methodology)

JF - Journal of the Royal Statistical Society: Series B (Statistical Methodology)

SN - 1369-7412

IS - 2

ER -