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Stock Market Volatility Spillovers: Evidence for Latin America

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Abstract

We extend the framework of Diebold and Yilmaz [2009] and Diebold and Yilmaz [2012] and construct volatility spillover indexes using a DCC-GARCH framework to model the multivariate relationships of volatility among assets. We compute spillover indexes directly from the series of asset returns and recognize the time-variant nature of the covariance matrix. Our approach allows for a better understanding of the movements of financial returns within a framework of volatility spillovers. We apply our method to stock market indexes of the United States and four Latin American countries. Our results show that Brazil is a net volatility transmitter for most of the sample period, while Chile, Colombia and Mexico are net receivers. The total spillover index is substantially higher between 2008Q3 and 2012Q2, and shock transmission from the United States to Latin America substantially increased around the Lehman Brothers’ episode.

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  • Santiago Gamba-Santamaria & Jose Eduardo Gomez-Gonzalez & Luis Fernando Melo-Velandia & Jorge Luis Hurtado-Guarin, 2016. "Stock Market Volatility Spillovers: Evidence for Latin America," Borradores de Economia 943, Banco de la Republica de Colombia.
  • Handle: RePEc:bdr:borrec:943
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    1. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    2. FrancisX. Diebold & Kamil Yilmaz, 2009. "Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets," Economic Journal, Royal Economic Society, vol. 119(534), pages 158-171, January.
    3. Wang, Gang-Jin & Xie, Chi & Jiang, Zhi-Qiang & Stanley, H. Eugene, 2016. "Extreme risk spillover effects in world gold markets and the global financial crisis," International Review of Economics & Finance, Elsevier, vol. 46(C), pages 55-77.
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    5. Loaiza-Maya, Rubén Albeiro & Gómez-González, José Eduardo & Melo-Velandia, Luis Fernando, 2015. "Exchange rate contagion in Latin America," Research in International Business and Finance, Elsevier, vol. 34(C), pages 355-367.
    6. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
    7. Prachi Mishra & Kenji Moriyama & Papa M N'Diaye & Lam Nguyen, 2014. "Impact of Fed Tapering Announcements on Emerging Markets," IMF Working Papers 14/109, International Monetary Fund.
    8. Sheng-Yung Yang, 2005. "A DCC analysis of international stock market correlations: the role of Japan on the Asian Four Tigers," Applied Financial Economics Letters, Taylor and Francis Journals, vol. 1(2), pages 89-93, March.
    9. Chiang, Thomas C. & Jeon, Bang Nam & Li, Huimin, 2007. "Dynamic correlation analysis of financial contagion: Evidence from Asian markets," Journal of International Money and Finance, Elsevier, vol. 26(7), pages 1206-1228, November.
    10. Newey, Whitney K & West, Kenneth D, 1987. "Hypothesis Testing with Efficient Method of Moments Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 28(3), pages 777-787, October.
    11. Wang, Gang-Jin & Xie, Chi & Jiang, Zhi-Qiang & Eugene Stanley, H., 2016. "Who are the net senders and recipients of volatility spillovers in China’s financial markets?," Finance Research Letters, Elsevier, vol. 18(C), pages 255-262.
    12. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
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    1. repec:eee:quaeco:v:68:y:2018:i:c:p:10-22 is not listed on IDEAS
    2. repec:gam:jsusta:v:10:y:2018:i:11:p:4113-:d:181650 is not listed on IDEAS
    3. repec:gam:jsusta:v:10:y:2018:i:10:p:3389-:d:171598 is not listed on IDEAS
    4. Jose Eduardo Gomez-Gonzalez & Jorge Hirs-Garzon, 2017. "Uncovering the time-varying nature of causality between oil prices and stock market returns: A multi-country study," Borradores de Economia 1009, Banco de la Republica de Colombia.
    5. Sandoval Paucar, Giovanny, 2018. "Efectos de desbordamiento sobre los mercados financieros de Colombia. Identificación a través de la heterocedasticidad
      [Spillovers effects on financial markets of Colombia. Identification through h
      ," MPRA Paper 90422, University Library of Munich, Germany.
    6. Jose Eduardo Gomez-Gonzalez & Jorge Hirs-Garzon & Sebastian Sanin-Restrepo, 2018. "Dynamic relations between oil and stock markets: Volatility spillovers, networks and causality," Borradores de Economia 1051, Banco de la Republica de Colombia.
    7. repec:gam:jjrfmx:v:12:y:2019:i:2:p:94-:d:237782 is not listed on IDEAS
    8. repec:eme:ijmfpp:ijmf-02-2017-0018 is not listed on IDEAS
    9. repec:eee:quaeco:v:71:y:2019:i:c:p:79-94 is not listed on IDEAS
    10. repec:spr:empeco:v:56:y:2019:i:5:d:10.1007_s00181-017-1406-3 is not listed on IDEAS
    11. repec:eee:finmar:v:43:y:2019:i:c:p:78-95 is not listed on IDEAS
    12. Santiago Gamba-Santamaria & Jose Eduardo Gomez-Gonzalez & Jorge Luis Hurtado-Guarin & Luis Fernando Melo-Velandia, 2019. "Volatility spillovers among global stock markets: measuring total and directional effects," Empirical Economics, Springer, vol. 56(5), pages 1581-1599, May.

    More about this item

    Keywords

    Volatility spillovers; DCC-GARCH model; Stock market linkages; financial crisis;

    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • 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

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