AStA Advances in Statistical Analysis

, Volume 100, Issue 4, pp 443–473 | Cite as

Multivariate Wishart stochastic volatility and changes in regime

Original Paper


This paper generalizes the basic Wishart multivariate stochastic volatility model of Philipov and Glickman (J Bus Econ Stat 24:313–328, 2006) and Asai and McAleer (J Econom 150:182–192, 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 fit to persistent covariance dynamics relative to the basic model.


Multivariate stochastic volatility Dynamic correlations Wishart distribution Markov switching Markov chain Monte Carlo 



The author would like to thank two anonymous referees, the associate editor Roman Liesenfeld and Jan Patrick Hartkopf for their helpful and very constructive comments.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Institute of Econometrics and StatisticsUniversity of CologneCologneGermany

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