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Dynamic Conditional Correlation: On Properties and Estimation

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  • Gian Piero Aielli

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.

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  • Gian Piero Aielli, 2013. "Dynamic Conditional Correlation: On Properties and Estimation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(3), pages 282-299, July.
    • Handle: RePEc:taf:jnlbes:v:31:y:2013:i:3:p:282-299
    DOI: 10.1080/07350015.2013.771027
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    References listed on IDEAS

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    1. Pesaran, Bahram & Pesaran, M. Hashem, 2007. "Modelling Volatilities and Conditional Correlations in Futures Markets with a Multivariate t Distribution," IZA Discussion Papers 2906, Institute of Labor Economics (IZA).
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    3. Cavit Pakel & Neil Shephard & Kevin Sheppard & Robert F. Engle, 2021. "Fitting Vast Dimensional Time-Varying Covariance Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(3), pages 652-668, July.
    4. Robert F. Engle & Kevin Sheppard, 2001. "Theoretical and Empirical properties of Dynamic Conditional Correlation Multivariate GARCH," NBER Working Papers 8554, National Bureau of Economic Research, Inc.
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