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Ten Things You Should Know About the Dynamic Conditional Correlation Representation

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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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  • Massimiliano Caporin & Michael McAleer, 2013. "Ten Things You Should Know About the Dynamic Conditional Correlation Representation," Documentos de Trabajo del ICAE 2013-21, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
  • Handle: RePEc:ucm:doicae:1321 Note: The authors most are grateful to two referees for very helpful comments and suggestions. For financial support, the second author wishes to acknowledge the Australian Research Council, National Science Council, Taiwan, and the Japan Society for the Promotion of Science. An earlier version of this paper was distributed as “Ten Things You Should Know About DCC”.
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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. Soetevent, Adriaan R. & Bao, Te & Schippers, Anouk L., 2016. "A commercial gift for charity," Research Report 16002-EEF, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    7. Chia-Lin Chang & Yiying Li & Michael McAleer, 2015. "Volatility Spillovers between Energy and Agricultural Markets: A Critical Appraisal of Theory and Practice," Tinbergen Institute Discussion Papers 15-077/III, Tinbergen Institute.
    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, 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," Tinbergen Institute Discussion Papers 14-087/III, Tinbergen Institute.
    11. 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.
    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, 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. Chia-Lin Chang & Yiying Li & Michael McAleer, 2015. "Volatility Spillovers between Energy and Agricultural Markets: A Critical Appraisal of Theory and Practice," Tinbergen Institute Discussion Papers 15-077/III, Tinbergen Institute.
    24. 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.
    25. Jean-David Fermanian & Hassan Malongo, 2014. "On the stationarity of Dynamic Conditional Correlation models," Papers 1405.6905, arXiv.org, revised Mar 2016.

    More about this item

    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.;

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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