Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/73506
Authors: 
Rinnergschwentner, Wolfgang
Tappeiner, Gottfried
Walde, Janette F.
Year of Publication: 
2011
Series/Report no.: 
Working Papers in Economics and Statistics 2011-19
Abstract: 
This paper picks up on a model developed by Philipov and Glickman (2006) for modeling multivariate stochastic volatility via Wishart processes. MCMC simulation from the posterior distribution is employed to fit the model. However, erroneous mathematical transformations in the full conditionals cause false implementation of the approach. We adjust the model, upgrade the analysis and investigate the statistical properties of the estimators using an extensive Monte Carlo study. Employing a Gibbs sampler in combination with a Metropolis Hastings algorithm inference for the time-dependent covariance matrix is feasible with appropriate statistical properties.
Subjects: 
Bayesian time series
Stochastic covariance
Timevarying correlation
Markov Chain Monte Carlo
JEL: 
C01
C11
C58
C63
Document Type: 
Working Paper

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