An efficient stochastic simulation algorithm for Bayesian unit root testing in stochastic volatility models

DOI10.1007/S10614-011-9252-4zbMath1207.91074OpenAlexW1996812449MaRDI QIDQ630100

Zhongxin Ni, Yong Li, Jie Zhang

Publication date: 17 March 2011

Published in: Computational Economics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10614-011-9252-4

zbMATH Keywords

path samplingBayes factorstochastic volatility modelsunit root testingfinancial times series

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 methods (including Monte Carlo methods) (91G60) Statistical methods; risk measures (91G70)

Uses Software

• WinBUGS
• R2WinBUGS
• BUGS
• tsbridge
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Cites Work

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• ARCH modeling in finance. A review of the theory and empirical evidence
• Monte Carlo methods in Bayesian computation
• BUGS for a Bayesian analysis of stochastic volatility models
• Distribution of the Estimators for Autoregressive Time Series With a Unit Root
• Marginal Likelihood from the Gibbs Output
• The Calculation of Posterior Distributions by Data Augmentation
• Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models
• Computing Bayes Factors by Combining Simulation and Asymptotic Approximations
• Marginal Likelihood From the Metropolis–Hastings Output
• Bayes Factors
• Common Persistence in Conditional Variances
• Structural Equation Modeling
• Simulating normalizing constants: From importance sampling to bridge sampling to path sampling

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