Multivariate Stochastic Volatility via Wishart Processes - A Continuation
AbstractThis 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.
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Bibliographic InfoPaper provided by Faculty of Economics and Statistics, University of Innsbruck in its series Working Papers with number 2011-19.
Date of creation: Aug 2011
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More information through EDIRC
Bayesian time series; Stochastic covariance; Timevarying correlation; Markov Chain Monte Carlo;
Find related papers by JEL classification:
- C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-10-09 (All new papers)
- NEP-ECM-2011-10-09 (Econometrics)
- NEP-ETS-2011-10-09 (Econometric Time Series)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Philipov, Alexander & Glickman, Mark E., 2006. "Multivariate Stochastic Volatility via Wishart Processes," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 313-328, July.
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