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Structural Dynamic Conditional Correlation

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  • Enzo Weber

Abstract

In the literature of identifcation through autoregressive conditional heteroscedasticity, Weber (2008) developed the structural constant conditional correlation (SCCC) model. Besides determining linear simultaneous in uences between several variables, this model considers interaction in the structural innovations. Even though this allows for common fundamental driving forces, these cannot explain time variation in correlations of observed variables, which still have to rely on causal transmission e ects. In this context, the present paper extends the analysis to structural dynamic conditional correlation (SDCC). The additional fexibility is shown to make an important contribution in the estimation of empirical real-data examples.

Suggested Citation

  • Enzo Weber, 2008. "Structural Dynamic Conditional Correlation," SFB 649 Discussion Papers SFB649DP2008-069, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    • Handle: RePEc:hum:wpaper:sfb649dp2008-069
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    References listed on IDEAS

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    1. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    2. Rigobon, Roberto, 2002. "The curse of non-investment grade countries," Journal of Development Economics, Elsevier, vol. 69(2), pages 423-449, December.
    3. Enzo Weber, 2008. "Structural Constant Conditional Correlation," SFB 649 Discussion Papers SFB649DP2008-015, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    4. Marcus Wagner, 2008. "Links between sustainability-related innovation and sustainability management," SFB 649 Discussion Papers SFB649DP2008-046, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    5. Ernst R. Berndt & Bronwyn H. Hall & Robert E. Hall & Jerry A. Hausman, 1974. "Estimation and Inference in Nonlinear Structural Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 3, number 4, pages 653-665, National Bureau of Economic Research, Inc.
    6. 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.
    7. Wolfgang Härdle & Nikolaus Hautsch & Uta Pigorsch, 2008. "Measuring and Modeling Risk Using High-Frequency Data," SFB 649 Discussion Papers SFB649DP2008-045, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    8. Sentana, Enrique & Fiorentini, Gabriele, 2001. "Identification, estimation and testing of conditionally heteroskedastic factor models," Journal of Econometrics, Elsevier, vol. 102(2), pages 143-164, June.
    9. Roberto Rigobon, 2003. "Identification Through Heteroskedasticity," The Review of Economics and Statistics, MIT Press, vol. 85(4), pages 777-792, November.
    10. Enzo Weber, 2007. "Volatility and Causality in Asia Pacific Financial Markets," SFB 649 Discussion Papers SFB649DP2007-004, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    11. Ross, Stephen A, 1989. " Information and Volatility: The No-Arbitrage Martingale Approach to Timing and Resolution Irrelevancy," Journal of Finance, American Finance Association, vol. 44(1), pages 1-17, March.
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    Cited by:

    1. Weber, Enzo & Zhang, Yanqun, 2012. "Common influences, spillover and integration in Chinese stock markets," Journal of Empirical Finance, Elsevier, vol. 19(3), pages 382-394.

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    More about this item

    Keywords

    Simultaneity; Identifcation; EGARCH; DCC;
    All these keywords.

    JEL classification:

    • 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
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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