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Structural Constant Conditional Correlation

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Author Info
Enzo Weber

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Abstract

A small strand of recent literature is occupied with identifying simultaneity in multiple equation systems through autoregressive conditional heteroscedasticity. Since this approach assumes that the structural innovations are uncorrelated, any contemporaneous connection of the endogenous variables needs to be exclusively explained by mutual spillover effects. In contrast, this paper allows for instantaneous covariances, which become identifiable by imposing the constraint of structural constant conditional correlation (SCCC). In this, common driving forces can be modelled in addition to simultaneous transmission effects. The new methodology is applied to the Dow Jones and Nasdaq Composite indexes in a small empirical example, illuminating scope and functioning of the SCCC model.

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Publisher Info
Paper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number SFB649DP2008-015.

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Length: 14 pages
Date of creation: Jan 2008
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Handle: RePEc:hum:wpaper:sfb649dp2008-015

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Related research
Keywords: Simultaneity; Identification; EGARCH; CCC;

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Find related papers by JEL classification:
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions
G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. 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. [Downloadable!] (restricted)
  2. 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. [Downloadable!] (restricted)
    Other versions:
  3. E.K. Berndt & B.H. Hall & R.E. Hall, 1974. "Estimation and Inference in Nonlinear Structural Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 3, number 4, pages 103-116 National Bureau of Economic Research, Inc. [Downloadable!]
  4. Roberto Rigobon, 2003. "Identification Through Heteroskedasticity," The Review of Economics and Statistics, MIT Press, vol. 85(4), pages 777-792, 09. [Downloadable!] (restricted)
  5. Rigobon, Roberto, 2002. "The curse of non-investment grade countries," Journal of Development Economics, Elsevier, vol. 69(2), pages 423-449, December. [Downloadable!] (restricted)
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  6. Tim Bollerslev & Jeffrey Wooldridge, 1992. "Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances," Econometric Reviews, Taylor and Francis Journals, vol. 11(2), pages 143-172. [Downloadable!] (restricted)
  7. Enzo Weber, 2007. "Volatility and Causality in Asia Pacific Financial Markets," SFB 649 Discussion Papers SFB649DP2007-004, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany. [Downloadable!]
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Enzo Weber, 2008. "Structural Dynamic Conditional Correlation," SFB 649 Discussion Papers SFB649DP2008-069, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany. [Downloadable!]
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