Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/66205 
Year of Publication: 
2012
Series/Report no.: 
Economics Working Paper No. 2012-14
Publisher: 
Kiel University, Department of Economics, Kiel
Abstract: 
This paper generalizes the basic Wishart multivariate stochastic volatility model of Philipov and Glickman (2006) and Asai and McAleer (2009) to encompass regime switching behavior. The latent state variable is driven by a first-order Markov process. The model allows for state-dependent (co)variance and correlation levels and state-dependent volatility spillover effects. Parameter estimates are obtained using Bayesian Markov Chain Monte Carlo procedures and filtered estimates of the latent variances and covariances are generated by particle filter techniques. The model is applied to five European stock index return series. The results show that the proposed regime-switching specification substantially improves the in-sample fit and the VaR forecasting performance relative to the basic model.
Subjects: 
Multivariate stochastic volatility
Dynamic correlations
Wishart distribution
Markov switching
Markov chain Monte Carlo
JEL: 
C32
C58
G17
Document Type: 
Working Paper

Files in This Item:
File
Size
880.61 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.