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Ten Things You Should Know about the Dynamic Conditional Correlation Representation


  • Massimiliano Caporin

    () (Department of Economics and Management "Marco Fanno", University of Padova, Via del Santo 33, 35123 Padova, Italy)

  • Michael McAleer

    () (Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam and Tinbergen Institute, 3000 DR Rotterdam, Netherlands
    Department of Quantitative Economics, Complutense University of Madrid, 28223 Pozuelo de Alarcón, Madrid, Spain
    Institute of Economic Research, Kyoto University, Kyoto 606-8501, Japan)


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 Generalized Autoregressive Conditional Correlation (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 Baba, Engle, Kraft and Kroner (BEKK) in small systems; and DCC may be a useful filter or a diagnostic check, but it is not a model.

Suggested Citation

  • Massimiliano Caporin & Michael McAleer, 2013. "Ten Things You Should Know about the Dynamic Conditional Correlation Representation," Econometrics, MDPI, Open Access Journal, vol. 1(1), pages 1-12, June.
  • Handle: RePEc:gam:jecnmx:v:1:y:2013:i:1:p:115-126:d:26620

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    References listed on IDEAS

    1. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(01), pages 122-150, February.
    2. Hammoudeh, Shawkat & Liu, Tengdong & Chang, Chia-Lin & McAleer, Michael, 2013. "Risk spillovers in oil-related CDS, stock and credit markets," Energy Economics, Elsevier, vol. 36(C), pages 526-535.
    3. Massimiliano Caporin & Michael McAleer, 2008. "Scalar BEKK and indirect DCC," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(6), pages 537-549.
    4. Massimiliano Caporin & Michael McAleer, 2012. "Do We Really Need Both Bekk And Dcc? A Tale Of Two Multivariate Garch Models," Journal of Economic Surveys, Wiley Blackwell, vol. 26(4), pages 736-751, September.
    5. Sébastien Laurent & Luc Bauwens & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109.
    6. Lorenzo Cappiello & Robert F. Engle & Kevin Sheppard, 2006. "Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 4(4), pages 537-572.
    7. Christian Hafner & Philip Hans Franses, 2009. "A Generalized Dynamic Conditional Correlation Model: Simulation and Application to Many Assets," Econometric Reviews, Taylor & Francis Journals, vol. 28(6), pages 612-631.
    8. Chang, Chia-Lin & McAleer, Michael & Tansuchat, Roengchai, 2011. "Crude oil hedging strategies using dynamic multivariate GARCH," Energy Economics, Elsevier, vol. 33(5), pages 912-923, September.
    9. 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.
    10. Robert Engle & Neil Shephard & Kevin Shepphard, 2008. "Fitting vast dimensional time-varying covariance models," OFRC Working Papers Series 2008fe30, Oxford Financial Research Centre.
    11. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    12. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    13. Lanza, Alessandro & Manera, Matteo & McAleer, Michael, 2006. "Modeling dynamic conditional correlations in WTI oil forward and futures returns," Finance Research Letters, Elsevier, vol. 3(2), pages 114-132, June.
    14. 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.
    15. Colacito, Riccardo & Engle, Robert F. & Ghysels, Eric, 2011. "A component model for dynamic correlations," Journal of Econometrics, Elsevier, vol. 164(1), pages 45-59, September.
    16. McAleer, Michael, 2005. "Automated Inference And Learning In Modeling Financial Volatility," Econometric Theory, Cambridge University Press, vol. 21(01), pages 232-261, February.
    17. 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, vol. 2(2), pages 123-130, March.
    18. McAleer, Michael & Chan, Felix & Hoti, Suhejla & Lieberman, Offer, 2008. "Generalized Autoregressive Conditional Correlation," Econometric Theory, Cambridge University Press, vol. 24(06), pages 1554-1583, December.
    19. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    Cited by:

    1. Chia-Lin Chang & Chia-Ping Liu & Michael McAleer, 2016. "Volatility Spillovers for Spot, Futures, and ETF Prices in Energy and Agriculture," Tinbergen Institute Discussion Papers 16-046/III, Tinbergen Institute.
    2. Chang, C-L. & Hsieh, T-L. & McAleer, M.J., 2016. "How are VIX and Stock Index ETF Related?," Econometric Institute Research Papers EI2016-07, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    3. Chia-Lin Chang & Hui-Kuang Hsu & Michael McAleer, 2014. "A Tourism Conditions Index," Tinbergen Institute Discussion Papers 14-007/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 EI 2014-06, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    5. Michael mcAleer, 2017. "Stationarity and Invertibility of a Dynamic Correlation Matrix," Tinbergen Institute Discussion Papers 17-082/III, Tinbergen Institute.
    6. Chia-Lin Chang & Tai-Lin Hsieh & Michael McAleer, 2016. "Connecting VIX and Stock Index ETF," Tinbergen Institute Discussion Papers 16-010/III, Tinbergen Institute, revised 23 Jan 2017.
    7. Chia-Lin Chang & Yiying Li & Michael McAleer, 2015. "Volatility Spillovers Between Energy and Agricultural Markets: A Critical Appraisal of Theory and Practice," Documentos de Trabajo del ICAE 2015-08, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    8. Perego, Erica R. & Vermeulen, Wessel N., 2016. "Macro-economic determinants of European stock and government bond correlations: A tale of two regions," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 214-232.
    9. Jean-David Fermanian & Hassan Malongo, 2013. "On the Stationarity of Dynamic Conditional Correlation Models," Working Papers 2013-26, Center for Research in Economics and Statistics.
    10. 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.
    11. repec:kap:openec:v:29:y:2018:i:1:d:10.1007_s11079-017-9472-x is not listed on IDEAS
    12. Asai, Manabu & Caporin, Massimiliano & McAleer, Michael, 2015. "Forecasting Value-at-Risk using block structure multivariate stochastic volatility models," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 40-50.
    13. Fresoli, Diego E. & Ruiz, Esther, 2016. "The uncertainty of conditional returns, volatilities and correlations in DCC models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 170-185.
    14. Takashi Isogai, 2015. "An Empirical Study of the Dynamic Correlation of Japanese Stock Returns," Bank of Japan Working Paper Series 15-E-7, Bank of Japan.
    15. Chia-Lin Chang & Michael McAleer & Jiarong Tian, 2016. "Modelling and Testing Volatility Spillovers in Oil and Financial Markets for USA, UK and China," Tinbergen Institute Discussion Papers 16-053/III, Tinbergen Institute.
    16. repec:eee:reveco:v:51:y:2017:i:c:p:193-213 is not listed on IDEAS
    17. Pan, Zhiyuan & Wang, Yudong & Yang, Li, 2014. "Hedging crude oil using refined product: A regime switching asymmetric DCC approach," Energy Economics, Elsevier, vol. 46(C), pages 472-484.
    18. Mehmet Balcilar & Riza Demirer & Rangan Gupta, 2017. "Do Sustainable Stocks Offer Diversification Benefits for Conventional Portfolios? An Empirical Analysis of Risk Spillovers and Dynamic Correlations," Sustainability, MDPI, Open Access Journal, vol. 9(10), pages 1-18, October.
    19. Piao, Xiaorui & Mei, Bin & Xue, Yuan, 2016. "Comparing the financial performance of timber REITs and other REITs," Forest Policy and Economics, Elsevier, vol. 72(C), pages 115-121.
    20. Tsouknidis, Dimitris A., 2016. "Dynamic volatility spillovers across shipping freight markets," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 91(C), pages 90-111.
    21. Paolella, Marc S. & Polak, Paweł, 2015. "ALRIGHT: Asymmetric LaRge-scale (I)GARCH with Hetero-Tails," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 282-297.
    22. Gospodinov, Nikolay, 2017. "Asset Co-movements: Features and Challenges," FRB Atlanta Working Paper 2017-11, Federal Reserve Bank of Atlanta.
    23. Gómez-Puig, Marta & Sosvilla-Rivero, Simón, 2016. "Causes and hazards of the euro area sovereign debt crisis: Pure and fundamentals-based contagion," Economic Modelling, Elsevier, vol. 56(C), pages 133-147.
    24. Jean-David Fermanian & Hassan Malongo, 2014. "On the stationarity of Dynamic Conditional Correlation models," Papers 1405.6905,, revised Mar 2016.

    More about this item


    DCC representation; BEKK; GARCC; stated representation; derived model; conditional correlations; two step estimators; assumed asymptotic properties; filter;

    JEL classification:

    • B23 - Schools of Economic Thought and Methodology - - History of Economic Thought since 1925 - - - Econometrics; Quantitative and Mathematical Studies
    • C - Mathematical and Quantitative Methods
    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs


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