Multivariate Wishart Stochastic Volatility Models

Konferenzbeitrag

We generalize the basic Wishart multivariate stochastic volatility model of Philipov and Glickmann (2006) to encompass regime switching behavior. The latent state variable is driven by a first-order Markov process. In order to estimate the proposed model we use Bayesian Markov Chain Monte Carlo procedures. For the computation of filtered estimates of the latent variances and covariances we rely upon particle filter techniques. The model is applied to five European stock index returns. Our results show that our proposed regime-switching specification substantially improves the estimates of the conditional covariance matrix and the VaR performance relative to the basic model.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Beiträge zur Jahrestagung des Vereins für Socialpolitik 2010: Ökonomie der Familie - Session: Advances in Time Series Analysis ; No. B6-V2

Classification and Topics

• Classification: Wirtschaft ; Bayesian Analysis: General ; Statistical Simulation Methods: General ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
• Subject: Markov Switching , MCMC ; Multivariate Stochastic Volatility ; Particle Filter ; Volatility Spillovers

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Gribisch, Bastian ; Liesenfeld, Roman
• Event: Veröffentlichung
• (who): Verein für Socialpolitik
• (where): Frankfurt a. M.
• (when): 2010

Further information

• Handle: http://hdl.handle.net/10419/37511
• Last update: 10.03.2025, 11:41 AM CET