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High and Low Frequency Correlations in Global Equity Markets


  • Robert F. Engle
  • José Gonzalo Rangel


This study models high and low frequency variation in global equity correlations using a comprehensive sample of 43 countries that includes developed and emerging markets, during the period 1995-2008. These two types of variations are modeled following the semi-parametric Factor-Spline-GARCH approach of Rangel and Engle (2008). This framework is extended and modified to incorporate the effect of multiple factors and to address the issue of non-synchronicity in international markets. Our empirical analysis suggests that the slow-moving dynamics of global correlations can be described by the Factor-Spline-GARCH specifications using either weekly or daily data. The analysis shows that the low frequency component of global correlations increased in the current financial turmoil; however, this increase was not equally distributed across countries. The countries that experienced the largest increase in correlations were mainly emerging markets.

Suggested Citation

  • Robert F. Engle & José Gonzalo Rangel, 2009. "High and Low Frequency Correlations in Global Equity Markets," Working Papers 2009-17, Banco de México.
  • Handle: RePEc:bdm:wpaper:2009-17

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    References listed on IDEAS

    1. Ross, Stephen A., 1976. "The arbitrage theory of capital asset pricing," Journal of Economic Theory, Elsevier, vol. 13(3), pages 341-360, December.
    2. Robert F. Engle & Jose Gonzalo Rangel, 2008. "The Spline-GARCH Model for Low-Frequency Volatility and Its Global Macroeconomic Causes," Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1187-1222, May.
    3. Andrew Ang & Geert Bekaert, 2002. "International Asset Allocation With Regime Shifts," Review of Financial Studies, Society for Financial Studies, vol. 15(4), pages 1137-1187.
    4. Dumas, Bernard & Harvey, Campbell R. & Ruiz, Pierre, 2003. "Are correlations of stock returns justified by subsequent changes in national outputs?," Journal of International Money and Finance, Elsevier, vol. 22(6), pages 777-811, November.
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    Cited by:

    1. Contessi, Silvio & De Pace, Pierangelo & Guidolin, Massimo, 2014. "How did the financial crisis alter the correlations of U.S. yield spreads?," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 362-385.
    2. Asako, Kazumi & Liu, Zhentao, 2013. "A statistical model of speculative bubbles, with applications to the stock markets of the United States, Japan, and China," Journal of Banking & Finance, Elsevier, vol. 37(7), pages 2639-2651.
    3. Tolga Cenesizoglu & Jonathan J. Reeves, 2013. "CAPM, Components of Beta and the Cross Section of Expected Returns," CIRANO Working Papers 2013s-09, CIRANO.
    4. Manuel A. Hernandez & Raul Ibarra & Danilo R. Trupkin, 2014. "How far do shocks move across borders? Examining volatility transmission in major agricultural futures markets," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 41(2), pages 301-325.

    More about this item


    Dynamic conditional correlations; high and low frequency variation; global markets; non-synchronicity.;

    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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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