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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. Andrew Ang & Geert Bekaert, 2002. "International Asset Allocation With Regime Shifts," The Review of Financial Studies, Society for Financial Studies, vol. 15(4), pages 1137-1187.
    2. 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..
    3. 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.
    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. 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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    Cited by:

    1. 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, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 41(2), pages 301-325.
    2. Tolga Cenesizoglu & Jonathan J. Reeves, 2013. "CAPM, Components of Beta and the Cross Section of Expected Returns," CIRANO Working Papers 2013s-09, CIRANO.
    3. José Rangel & Robert Engle, 2012. "The Factor–Spline–GARCH Model for High and Low Frequency Correlations," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 109-124.
    4. 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.
    5. Clark Lundberg, 2019. "Identifying horizon-based heterogeneity in the cross section of portfolio returns," Economics Bulletin, AccessEcon, vol. 39(2), pages 1163-1175.
    6. Busch, Ramona & Koziol, Philipp & Mitrovic, Marc, 2018. "Many a little makes a mickle: Stress testing small and medium-sized German banks," The Quarterly Review of Economics and Finance, Elsevier, vol. 68(C), pages 237-253.
    7. 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.
    8. 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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