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Dynamic Conditional Correlation: On properties and estimation

Author

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  • Gian Piero Aielli

    () (University of Padova)

Abstract

We address some issues that arise with the Dynamic Conditional Correlation (DCC) model. We prove that the DCC large system estimator (DCC estimator) can be inconsistent, and that the traditional interpretation of the DCC correlation parameters can lead to misleading conclusions. We then suggest a more tractable dynamic conditional correlation model (cDCC model). A related large system estimator (cDCC estimator) is described and heuristically proven to be consistent. Sufficient stationarity conditions for cDCC processes of interest, including the covariance-return process, are established. The DCC and cDCC estimators are compared by means of applications to simulated and real data.

Suggested Citation

  • Gian Piero Aielli, 2011. "Dynamic Conditional Correlation: On properties and estimation," "Marco Fanno" Working Papers 0142, Dipartimento di Scienze Economiche "Marco Fanno".
  • Handle: RePEc:pad:wpaper:0142
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    References listed on IDEAS

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    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. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    3. Pelletier, Denis, 2006. "Regime switching for dynamic correlations," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 445-473.
    4. Amisano, Gianni & Giacomini, Raffaella, 2007. "Comparing Density Forecasts via Weighted Likelihood Ratio Tests," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 177-190, April.
    5. Engle, Robert & Colacito, Riccardo, 2006. "Testing and Valuing Dynamic Correlations for Asset Allocation," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 238-253, April.
    6. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    7. Annastiina Silvennoinen & Timo Teräsvirta, 2005. "Multivariate Autoregressive Conditional Heteroskedasticity with Smooth Transitions in Conditional Correlations," Research Paper Series 168, Quantitative Finance Research Centre, University of Technology, Sydney.
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    Cited by:

    1. Hendrych, R. & Cipra, T., 2016. "On conditional covariance modelling: An approach using state space models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 304-317.
    2. Prono, Todd, 2015. "Market proxies as factors in linear asset pricing models: Still living with the roll critique," Journal of Empirical Finance, Elsevier, vol. 31(C), pages 36-53.
    3. Christoffersen, Peter & Errunza, Vihang & Jacobs, Kris & Jin, Xisong, 2014. "Correlation dynamics and international diversification benefits," International Journal of Forecasting, Elsevier, vol. 30(3), pages 807-824.
    4. Creti, Anna & Joëts, Marc & Mignon, Valérie, 2013. "On the links between stock and commodity markets' volatility," Energy Economics, Elsevier, vol. 37(C), pages 16-28.
    5. Aslanidis, Nektarios & Casas, Isabel, 2013. "Nonparametric correlation models for portfolio allocation," Journal of Banking & Finance, Elsevier, vol. 37(7), pages 2268-2283.
    6. Roxana Halbleib & Valeri Voev, 2016. "Forecasting Covariance Matrices: A Mixed Approach," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 14(2), pages 383-417.

    More about this item

    Keywords

    Multivariate GARCH Model; Quasi-Maximum-Likelihood; Two-step Estimation; Integrated Correlation; Generalized Profile Likelihood.;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: 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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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