Dynamic Conditional Correlation: On Properties and Estimation
Gian Piero Aielli
Journal of Business & Economic Statistics, 2013, vol. 31, issue 3, 282-299
Abstract:
This article addresses some of the issues that arise with the Dynamic Conditional Correlation (DCC) model. It is proven that the DCC large system estimator can be inconsistent, and that the traditional interpretation of the DCC correlation parameters can result in misleading conclusions. Here, we suggest a more tractable DCC model, called the c DCC model. The c DCC model allows for a large system estimator that is heuristically proven to be consistent. Sufficient stationarity conditions for c DCC processes of interest are established. The empirical performances of the DCC and c DCC large system estimators are compared via simulations and applications to real data.
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (240)
Downloads: (external link)
http://hdl.handle.net/10.1080/07350015.2013.771027 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlbes:v:31:y:2013:i:3:p:282-299
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UBES20
DOI: 10.1080/07350015.2013.771027
Access Statistics for this article
Journal of Business & Economic Statistics is currently edited by Eric Sampson, Rong Chen and Shakeeb Khan
More articles in Journal of Business & Economic Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().