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Dynamic Conditional Correlation: On Properties and Estimation

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  • Gian Piero Aielli
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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 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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    File URL: http://hdl.handle.net/10.1080/07350015.2013.771027
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    Bibliographic Info

    Article provided by Taylor & Francis Journals in its journal Journal of Business & Economic Statistics.

    Volume (Year): 31 (2013)
    Issue (Month): 3 (July)
    Pages: 282-299

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    Handle: RePEc:taf:jnlbes:v:31:y:2013:i:3:p:282-299

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    Cited by:
    1. Cristina García de la Fuente & Pedro Galeano & Michael P. Wiper, 2014. "Bayesian estimation of a Dynamic Conditional Correlation model with multivariate Skew-Slash innovations," Statistics and Econometrics Working Papers ws141711, Universidad Carlos III, Departamento de Estadística y Econometría.
    2. Christian M. Hafner & Michael McAleer, 2014. "A One Line Derivation of DCC: Application of a Vector Random Coefficient Moving Average Process," Working Papers in Economics 14/19, University of Canterbury, Department of Economics and Finance.
    3. Diego Fresoli & Esther Ruiz, 2014. "The uncertainty of conditional returns, volatilities and correlations in DCC models," Statistics and Econometrics Working Papers ws140202, Universidad Carlos III, Departamento de Estadística y Econometría.
    4. Sensoy, Ahmet, 2013. "Dynamic relationship between precious metals," Resources Policy, Elsevier, vol. 38(4), pages 504-511.
    5. Caporin, Massimiliano & Jimenez-Martin, Juan-Angel & Gonzalez-Serrano, Lydia, 2013. "Currency hedging strategies, strategic benchmarks and the Global and Euro Sovereign financial crises," MPRA Paper 50940, University Library of Munich, Germany, revised 23 Oct 2013.
    6. Jean-David Fermanian & Hassan Malongo, 2014. "On the stationarity of Dynamic Conditional Correlation models," Papers 1405.6905, arXiv.org.
    7. Kris Boudt & Sébastien Laurent & Asger Lunde & Rogier Quaedvlieg, 2014. "Positive Semidefinite Integrated Covariance Estimation, Factorizations and Asynchronicity," CREATES Research Papers 2014-05, School of Economics and Management, University of Aarhus.
    8. Christophe Hurlin & Sebastien Laurent & Rogier Quaedvlieg & Stephan Smeekes, 2013. "Risk Measure Inference," Working Papers halshs-00877279, HAL.
    9. Trancoso, Tiago, 2014. "Emerging markets in the global economic network: Real(ly) decoupling?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 395(C), pages 499-510.
    10. Jean-David Fermanian & Hassan Malongo, 2013. "On the Stationarity of Dynamic Conditional Correlation Models," Working Papers 2013-26, Centre de Recherche en Economie et Statistique.
    11. Christian M. Hafner & and Michael McAleer, 2014. "A One Line Derivation of DCC: Application of a Vector Random Coefficient Moving Average Process," Tinbergen Institute Discussion Papers 14-087/III, Tinbergen Institute.

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