Inference for Adaptive Time Series Models: Stochastic Volatility and Conditionally Gaussian State Space Form

DOI10.1080/07474930600713275zbMath1113.62094OpenAlexW2131150664MaRDI QIDQ5485104

Charles S. Bos, Neil Shephard

Publication date: 28 August 2006

Published in: Econometric Reviews (Search for Journal in Brave)

Full work available at URL: http://papers.tinbergen.nl/04015.pdf

zbMATH Keywords

stochastic volatilityMarkov chain Monte Carlosimulationsparticle filterstate space form

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05) Numerical analysis or methods applied to Markov chains (65C40)

Related Items

A Survey of Sequential Monte Carlo Methods for Economics and Finance, An effcient exact Bayesian method for state space models with stochastic volatility, Sequential Monte Carlo methods for stochastic volatility models: a review, Efficient Bayesian estimation of multivariate state space models, Parameterisation and efficient MCMC estimation of non-Gaussian state space models, Multivariate Stochastic Volatility: A Review, Parametric estimation of hidden Markov models by least squares type estimation and deconvolution

Uses Software

• SsfPack

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