Multivariate Wishart stochastic volatility and changes in regime

被引：3

作者：

Gribisch, Bastian ^{[1
]}

机构：

[1] Univ Cologne, Inst Econometr & Stat, Albertus Magnus Pl, D-50923 Cologne, Germany

来源：

ASTA-ADVANCES IN STATISTICAL ANALYSIS | 2016年 / 100卷 / 04期

关键词：

Multivariate stochastic volatility; Dynamic correlations; Wishart distribution; Markov switching; Markov chain Monte Carlo; AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICITY; MARGINAL LIKELIHOOD; SPILLOVERS; VARIANCE; MODEL; INTERDEPENDENCE; CONTAGION; INFERENCE; RATES; TIME;

D O I：

10.1007/s10182-016-0269-9

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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.

引用

页码：443 / 473

页数：31