A General Multivariate Threshold GARCH Model for Dynamic Correlations
Journal
Journal of Business and Economic Statistics
ISSN
0735-0015
ISSN-Digital
1537-2707
Type
journal article
Date Issued
2011-01
Author(s)
Abstract
We introduce a new multivariate GARCH model with multivariate thresholds in conditional correlations and develop a two-step
estimation procedure that is feasible in large dimensional applications. Optimal threshold functions are estimated endogenously from the data, and the model conditional covariance matrix is ensured to be positive definite. We study the empirical performance of our model in two applications using US stock and bond market data. In both applications our model has, in terms of statistical and economic significance, higher forecasting power than several other multivariate GARCH models for conditional correlations
estimation procedure that is feasible in large dimensional applications. Optimal threshold functions are estimated endogenously from the data, and the model conditional covariance matrix is ensured to be positive definite. We study the empirical performance of our model in two applications using US stock and bond market data. In both applications our model has, in terms of statistical and economic significance, higher forecasting power than several other multivariate GARCH models for conditional correlations
Language
English
Keywords
Multivariate GARCH models
Dynamic conditional correlations
Tree-structured GARCH models.
HSG Classification
contribution to scientific community
Refereed
Yes
Publisher
Taylor & Francis
Publisher place
Abingdon UK
Volume
29
Number
1
Start page
138
End page
149
Pages
12
Subject(s)
Eprints ID
57801