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Ten Things you should know about the Dynamic Conditional Correlation Representation

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  • Massimiliano Caporin

    (University of Padova, Italy)

  • Michael McAleer

    (Erasmus University Rotterdam, The Netherlands, Complutense University of Madrid, Spain, and Kyoto University, Japan)

Abstract

The purpose of the paper is to discuss ten things potential users should know about the limits of the Dynamic Conditional Correlation (DCC) representation for estimating and forecasting time-varying conditional correlations. The reasons given for caution about the use of DCC include the following: DCC represents the dynamic conditional covariances of the standardized residuals, and hence does not yield dynamic conditional correlations; DCC is stated rather than derived; DCC has no moments; DCC does not have testable regularity conditions; DCC yields inconsistent two step estimators; DCC has no asymptotic properties; DCC is not a special case of GARCC, which has testable regularity conditions and standard asymptotic properties; DCC is not dynamic empirically as the effect of news is typically extremely small; DCC cannot be distinguished empirically from diagonal BEKK in small systems; and DCC may be a useful filter or a diagnostic check, but it is not a model.

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Bibliographic Info

Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 13-078/III.

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Date of creation: 10 Jun 2013
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Handle: RePEc:dgr:uvatin:20130078

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Web page: http://www.tinbergen.nl

Related research

Keywords: DCC representation; BEKK; GARCC; stated representation; derived model; conditional covariances; conditional correlations; regularity conditions; moments; two step estimators; assumed properties; asymptotic properties; filter; diagnostic check;

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References

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  1. Massimiliano Caporin & Michael McAleer, 2010. "Do We Really Need Both BEKK and DCC? A Tale of Two Multivariate GARCH Models," Working Papers in Economics, University of Canterbury, Department of Economics and Finance 10/06, University of Canterbury, Department of Economics and Finance.
  2. Sheppard, Kevin & Cappiello, Lorenzo & Engle, Robert F., 2003. "Asymmetric dynamics in the correlations of global equity and bond returns," Working Paper Series, European Central Bank 0204, European Central Bank.
  3. Monica Billio & Massimiliano Caporin & Michele Gobbo, 2006. "Flexible Dynamic Conditional Correlation multivariate GARCH models for asset allocation," Applied Financial Economics Letters, Taylor and Francis Journals, Taylor and Francis Journals, vol. 2(2), pages 123-130, March.
  4. Maria Kasch & Massimiliano Caporin, 2013. "Volatility Threshold Dynamic Conditional Correlations: An International Analysis," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 11(4), pages 706-742, September.
  5. Massimiliano Caporin & Michael McAleer, 2008. "Scalar BEKK and indirect DCC," Journal of Forecasting, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 27(6), pages 537-549.
  6. Christian Hafner & Philip Hans Franses, 2009. "A Generalized Dynamic Conditional Correlation Model: Simulation and Application to Many Assets," Econometric Reviews, Taylor & Francis Journals, Taylor & Francis Journals, vol. 28(6), pages 612-631.
  7. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, Elsevier, vol. 31(3), pages 307-327, April.
  8. Sébastien Laurent & Luc Bauwens & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 21(1), pages 79-109.
  9. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, Cambridge University Press, vol. 11(01), pages 122-150, February.
  10. Roengchai Tansuchat & Chia-Lin Chang & Michael McAleer, 2010. "Crude Oil Hedging Strategies Using Dynamic Multivariate GARCH," CIRJE F-Series, CIRJE, Faculty of Economics, University of Tokyo CIRJE-F-704, CIRJE, Faculty of Economics, University of Tokyo.
  11. Shawkat Hammoudeh & Tengdong Liu & Chia-Lin Chang & Michael McAleer, 2011. "Risk Spillovers in Oil-Related CDS, Stock and Credit Markets," Working Papers in Economics, University of Canterbury, Department of Economics and Finance 11/17, University of Canterbury, Department of Economics and Finance.
  12. Robert Engle & Neil Shephard & Kevin Shepphard, 2008. "Fitting vast dimensional time-varying covariance models," OFRC Working Papers Series, Oxford Financial Research Centre 2008fe30, Oxford Financial Research Centre.
  13. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, Econometric Society, vol. 50(4), pages 987-1007, July.
  14. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 20(3), pages 339-50, July.
  15. Colacito, Riccardo & Engle, Robert F. & Ghysels, Eric, 2011. "A component model for dynamic correlations," Journal of Econometrics, Elsevier, Elsevier, vol. 164(1), pages 45-59, September.
  16. Matteo Manera & Alessandro Lanza & Michael McAleer, 2004. "Modelling Dynamic Conditional Correlations in WTI Oil Forward and Futures Returns," Working Papers, Fondazione Eni Enrico Mattei 2004.72, Fondazione Eni Enrico Mattei.
  17. McAleer, Michael & Chan, Felix & Hoti, Suhejla & Lieberman, Offer, 2008. "Generalized Autoregressive Conditional Correlation," Econometric Theory, Cambridge University Press, Cambridge University Press, vol. 24(06), pages 1554-1583, December.
  18. McAleer, Michael, 2005. "Automated Inference And Learning In Modeling Financial Volatility," Econometric Theory, Cambridge University Press, Cambridge University Press, vol. 21(01), pages 232-261, February.
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Cited by:
  1. Chang, C-L. & Hsu, H-K. & McAleer, M.J., 2014. "A Tourism Conditions Index," Econometric Institute Research Papers, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute EI 2014-04, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  2. Jean-David Fermanian & Hassan Malongo, 2014. "On the stationarity of Dynamic Conditional Correlation models," Papers 1405.6905, arXiv.org.
  3. 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, Tinbergen Institute 14-087/III, Tinbergen Institute.
  4. McAleer, M.J., 2014. "Discussion of “Principal Volatility Component Analysis” by Yu-Pin Hu and Ruey Tsay," Econometric Institute Research Papers, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute EI 2014-06, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  5. Jean-David Fermanian & Hassan Malongo, 2013. "On the Stationarity of Dynamic Conditional Correlation Models," Working Papers, Centre de Recherche en Economie et Statistique 2013-26, Centre de Recherche en Economie et Statistique.
  6. 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, University of Canterbury, Department of Economics and Finance 14/19, University of Canterbury, Department of Economics and Finance.

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