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Cited by 422

The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.

Dynamic Conditional Correlation: On Properties and Estimation

Aielli, Gian Piero·2013·Journal of Business & Economic Statistics
DOI 10.1080/07350015.2013.771027
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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 cDCC model. The cDCC model allows for a large system estimator that is heuristically proven to be consistent. Sufficient stationarity conditions for cDCC processes of interest are established. The empirical performances of the DCC and cDCC large system estimators are compared via simulations and applications to real data.

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Metadata

Authors
Aielli, G. P.
Venue
Journal of Business & Economic Statistics
Publisher
Informa UK Limited
Published
2013-07
Volume
31
Issue
3
Pages
282-299
ISSN
  • 0735-0015 (ISSN-L)
  • 1537-2707
DOI
10.1080/07350015.2013.771027
OpenAlex
W2054513759
Cited by
422
References
34
FWCI
60.49
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Dynamic Conditional Correlation: On Properties and Estimation. https://app.cochat.ai/papers/10.1080/07350015.2013.771027
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Aielli, G. P. (2013). Dynamic Conditional Correlation: On Properties and Estimation. Journal of Business & Economic Statistics. https://doi.org/10.1080/07350015.2013.771027

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.