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BIBTEX RIS

Dynamic Conditional Correlation: On Properties and Estimation

2013; Taylor & Francis; Volume: 31; Issue: 3 Linguagem: Inglês

10.1080/07350015.2013.771027

ISSN

1537-2707

Autores

Gian Piero Aielli,

Tópico(s)

Statistical Methods and Inference

Resumo

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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