Article ID: | iaor20023536 |
Country: | Netherlands |
Volume: | 139 |
Issue: | 2 |
Start Page Number: | 351 |
End Page Number: | 370 |
Publication Date: | Jun 2002 |
Journal: | European Journal of Operational Research |
Authors: | Christodoulakis George A., Satchell Stephen E. |
Although the time variation of the conditional correlations of asset returns is a well established stylized fact (and of crucial importance for efficient financial decisions) there is no explicit general model available for its estimation and forecasting. In this paper, we propose a bivariate GARCH covariance structure in which conditional variances can follow any GARCH-type process, while conditional correlation is generated by an explicit discrete-time stochastic process, the CorrARCH process. A high order CorrARCH can parsimoniously be represented by a CorGARCH process. The model successfully generates the reported stylized facts, establishes an autocorrelation structure for correlations and thus provides an explicit framework for out-of-sample forecasting. We provide empirical evidence from the G7 Stock Market Indexes.