Block Structure Multivariate Stochastic Volatility Models
35 Pages Posted: 18 Dec 2009
Date Written: December 16, 2009
Abstract
Most multivariate variance models suffer from a common problem, the “curse of dimensionality”. For this reason, most are fitted under strong parametric restrictions that reduce the interpretation and flexibility of the models. Recently, the literature has focused on multivariate models with milder restrictions, whose purpose was to combine the need for interpretability and efficiency faced by model users with the computational problems that may emerge when the number of assets is quite large. We contribute to this strand of the literature proposing a block-type parameterization for multivariate stochastic volatility models.
Keywords: block structures; multivariate stochastic volatility; curse of dimensionality
JEL Classification: C32, C51, C10
Suggested Citation: Suggested Citation
Do you have negative results from your research you’d like to share?
Recommended Papers
-
Measuring and Testing the Impact of News on Volatility
By Robert F. Engle and Victor K. Ng
-
Caviar: Conditional Value at Risk by Quantile Regression
By Simone Manganelli and Robert F. Engle
-
Dynamic Conditional Correlation - a Simple Class of Multivariate GARCH Models
-
Dynamic Conditional Correlation a Simple Class of Multivariate GARCH Models
-
Dynamic Conditional Correlation - a Simple Class of Multivariate GARCH Models
-
Dynamic Conditional Correlation : A Simple Class of Multivariate GARCH Models
-
Asset Pricing with a Factor Arch Covariance Structure: Empirical Estimates for Treasury Bills
By Robert F. Engle, Victor Ng, ...
-
Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH
By Kevin Sheppard and Robert F. Engle
-
Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH
By Robert F. Engle and Kevin Sheppard
-
Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH
By Robert F. Engle and Kevin Sheppard