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.
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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.
| Type: | Article |
|---|---|
| Title: | On efficient Bayesian inference for models with stochastic volatility |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.ecosta.2016.08.002 |
| Publisher version: | https://doi.org/10.1016/j.ecosta.2016.08.002 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Stochastic volatility, Bayesian methods, Markov chain Monte Carlo, Mixture offset representation |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10068277 |
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