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High and Low Frequency Correlations in Global Equity Markets

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  • Engle Robert F.
  • Rangel José Gonzalo

Abstract

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

  • Engle Robert F. & Rangel José Gonzalo, 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

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    1. Stephen A. Ross, 2013. "The Arbitrage Theory of Capital Asset Pricing," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 1, pages 11-30, World Scientific Publishing Co. Pte. Ltd..
    2. Robert F. Engle & Jose Gonzalo Rangel, 2008. "The Spline-GARCH Model for Low-Frequency Volatility and Its Global Macroeconomic Causes," The Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1187-1222, May.
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    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.
    5. Cavit Pakel & Neil Shephard & Kevin Sheppard & Robert F. Engle, 2021. "Fitting Vast Dimensional Time-Varying Covariance Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(3), pages 652-668, July.
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    Cited by:

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    4. 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.
    5. Busch, Ramona & Koziol, Philipp & Mitrovic, Marc, 2015. "Many a little makes a mickle: Macro portfolio stress test for small and medium-sized German banks," Discussion Papers 23/2015, Deutsche Bundesbank.
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    More about this item

    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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