Konferenzbeitrag
Multivariate Wishart Stochastic Volatility Models
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Beiträge zur Jahrestagung des Vereins für Socialpolitik 2010: Ökonomie der Familie - Session: Advances in Time Series Analysis ; No. B6-V2
- Klassifikation
-
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
- Thema
-
Markov Switching , MCMC
Multivariate Stochastic Volatility
Particle Filter
Volatility Spillovers
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Gribisch, Bastian
Liesenfeld, Roman
- Ereignis
-
Veröffentlichung
- (wer)
-
Verein für Socialpolitik
- (wo)
-
Frankfurt a. M.
- (wann)
-
2010
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:41 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Konferenzbeitrag
Beteiligte
- Gribisch, Bastian
- Liesenfeld, Roman
- Verein für Socialpolitik
Entstanden
- 2010
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