An effcient exact Bayesian method For state space models with stochastic volatility
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Yu-Fan Huang
Abstract
This paper introduces a Bayesian MCMC method, referred to as a marginalized mixture sampler, for state space models whose disturbances follow stochastic volatility processes. The marginalized mixture sampler is based on a mixture-normal approximation of the log-χ 2 distribution, but it is implemented without the need to simulate the mixture indicator variable. The key innovation is to use the filter ing scheme developed by Kim (Kim C.-J. 1994. “Dynamic Linear Models with Markov-Switching.” Journal of Econometrics 60: 1–22.) and the forward-filtering backward-sampling algorithm to generate a proposal series of the latent stochastic volatility process. The proposal series is then accepted according to the Metropolis-Hastings acceptance probability. The new sampler is examined within an unobserved component model and a time-varying parameter vector autoregressive model, and it reduces substantially the correlations between MCMC draws.
Funding source: National Natural Science Foundation of China
Award Identifier / Grant number: 71601129
Funding statement: National Natural Science Foundation of China, Funder Id: http://dx.doi.org/10.13039/501100001809, Grant Number: 71601129.
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Supplementary Material
The online version of this article offers supplementary material (DOI: https://doi.org/10.1515/snde-2018-0098).
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Articles in the same Issue
- Frontmatter
- Research Articles
- Stochastic model specification in Markov switching vector error correction models
- An effcient exact Bayesian method For state space models with stochastic volatility
- Application of grey relational analysis and artificial neural networks on currency exchange-traded notes (ETNs)
- A Strategy for the Use of the Cross Recurrence Quantification Analysis
- Macroeconomic uncertainty and forecasting macroeconomic aggregates
- Identifying asymmetric responses of sectoral equities to oil price shocks in a NARDL model
- Dependence Modelling in Insurance via Copulas with Skewed Generalised Hyperbolic Marginals
Articles in the same Issue
- Frontmatter
- Research Articles
- Stochastic model specification in Markov switching vector error correction models
- An effcient exact Bayesian method For state space models with stochastic volatility
- Application of grey relational analysis and artificial neural networks on currency exchange-traded notes (ETNs)
- A Strategy for the Use of the Cross Recurrence Quantification Analysis
- Macroeconomic uncertainty and forecasting macroeconomic aggregates
- Identifying asymmetric responses of sectoral equities to oil price shocks in a NARDL model
- Dependence Modelling in Insurance via Copulas with Skewed Generalised Hyperbolic Marginals
