Multivariate wishart stochastic volatility and changes in regime
AbstractThis 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. --
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Bibliographic InfoPaper provided by Christian-Albrechts-University of Kiel, Department of Economics in its series Economics Working Papers with number 2012-14.
Date of creation: 2012
Date of revision:
Multivariate stochastic volatility; Dynamic correlations; Wishart distribution; Markov switching; Markov chain Monte Carlo;
Find related papers by JEL classification:
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
- G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-12-06 (All new papers)
- NEP-ECM-2012-12-06 (Econometrics)
- NEP-ETS-2012-12-06 (Econometric Time Series)
- NEP-FOR-2012-12-06 (Forecasting)
- NEP-ORE-2012-12-06 (Operations Research)
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