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Correlation vs. Causality in Stock Market Comovement

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  • Enzo Weber

Abstract

This paper seeks to disentangle the sources of correlations between high-, mid- and lowcap stock indexes from the German prime standard. In principle, such comovement can arise from direct spillover between the variables or due to common factors. By standard means, these different components are obviously not identifiable. As a solution, the underlying study proposes specifying ARCH-type models for both the idiosyncratic innovations and a common factor, so that the model structure can be identified through heteroscedasticity. The seemingly surprising result that smaller caps have higher influence than larger ones is explained by asymmetric information processing in financial markets. Broad macroeconomic information is shown to enter the common factor rather than the segment-specific shocks.

Suggested Citation

  • Enzo Weber, 2007. "Correlation vs. Causality in Stock Market Comovement," SFB 649 Discussion Papers SFB649DP2007-064, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  • Handle: RePEc:hum:wpaper:sfb649dp2007-064
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    File URL: http://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2007-064.pdf
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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, pages 423-449.
    3. Baur, Dirk & Jung, Robert C., 2006. "Return and volatility linkages between the US and the German stock market," Journal of International Money and Finance, Elsevier, vol. 25(4), pages 598-613, June.
    4. Lo, Andrew W & MacKinlay, A Craig, 1990. "When Are Contrarian Profits Due to Stock Market Overreaction?," Review of Financial Studies, Society for Financial Studies, vol. 3(2), pages 175-205.
    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. 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.
    7. Nelson, Daniel B., 1992. "Filtering and forecasting with misspecified ARCH models I : Getting the right variance with the wrong model," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 61-90.
    8. Roberto Rigobon, 2003. "Identification Through Heteroskedasticity," The Review of Economics and Statistics, MIT Press, vol. 85(4), pages 777-792, November.
    9. Enzo Weber, 2010. "Volatility and causality in Asia Pacific financial markets," Applied Financial Economics, Taylor & Francis Journals, vol. 20(16), pages 1269-1292.
    10. King, Mervyn & Sentana, Enrique & Wadhwani, Sushil, 1994. "Volatility and Links between National Stock Markets," Econometrica, Econometric Society, vol. 62(4), pages 901-933, July.
    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, 2009. "Financial Contagion, Vulnerability and Information Flow: Empirical Identification," University of Regensburg Working Papers in Business, Economics and Management Information Systems 431, University of Regensburg, Department of Economics.

    More about this item

    Keywords

    Identification; Spillover; Common Factor; Structural EGARCH; DAX;

    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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