On efficient Bayesian inference for models with stochastic volatility

Griffin, Jim E., Sakaria, Dhirendra Kumar (2016) On efficient Bayesian inference for models with stochastic volatility. Econometrics and Statistics, 3 . pp. 23-33. ISSN 2452-3062. (doi:10.1016/j.ecosta.2016.08.002)

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.

Item Type:	Article
DOI/Identification number:	10.1016/j.ecosta.2016.08.002
Uncontrolled keywords:	Stochastic volatility; Bayesian methods; Markov chain Monte Carlo; Mixture offset representation
Subjects:	Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions:	Faculties > Sciences > School of Mathematics Statistics and Actuarial Science Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User:	Jim Griffin
Date Deposited:	17 Nov 2016 11:04 UTC
Last Modified:	29 May 2019 18:13 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/58710 (The current URI for this page, for reference purposes)
Griffin, Jim E.:	https://orcid.org/0000-0002-4828-7368