eprintid: 10068277 rev_number: 25 eprint_status: archive userid: 608 dir: disk0/10/06/82/77 datestamp: 2019-02-26 14:59:40 lastmod: 2020-02-12 18:56:46 status_changed: 2019-02-26 14:59:40 type: article metadata_visibility: show creators_name: Sakaria, DK creators_name: Griffin, JE title: On efficient Bayesian inference for models with stochastic volatility ispublished: pub divisions: UCL divisions: A01 divisions: B04 divisions: C06 divisions: F61 keywords: Stochastic volatility, Bayesian methods, Markov chain Monte Carlo, Mixture offset representation note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. abstract: An efficient method for Bayesian inference in stochastic volatility models uses a linear state space representation to define a Gibbs sampler in which the volatilities are jointly updated. This method involves the choice of an offset parameter and we illustrate how its choice can have an important effect on the posterior inference. A Metropolis–Hastings algorithm is developed to robustify this approach to choice of the offset parameter. The method is illustrated on simulated data with known parameters, the daily log returns of the Eurostoxx index and a Bayesian vector autoregressive model with stochastic volatility. date: 2017-07 date_type: published official_url: https://doi.org/10.1016/j.ecosta.2016.08.002 oa_status: green full_text_type: other language: eng primo: open primo_central: open_green article_type_text: Journal Article verified: verified_manual elements_id: 1629275 doi: 10.1016/j.ecosta.2016.08.002 language_elements: English lyricists_name: Griffin, James lyricists_id: JEGRI73 actors_name: Waragoda Vitharana, Nimal actors_id: NWARR44 actors_role: owner full_text_status: public publication: Econometrics and Statistics volume: 3 pagerange: 23-33 issn: 2452-3062 citation: Sakaria, DK; Griffin, JE; (2017) On efficient Bayesian inference for models with stochastic volatility. Econometrics and Statistics , 3 pp. 23-33. 10.1016/j.ecosta.2016.08.002 <https://doi.org/10.1016/j.ecosta.2016.08.002>. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10068277/1/Griffin_On%20efficient%20Bayesian%20inference%20for%20models%20with%20stochastic%20volatility_AAM.pdf