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Volatility Spillovers among Global Stock Markets: Measuring Total and Directional Effects

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Abstract

In this study we construct volatility spillover indexes for some of the major stock market indexes in the world. We use a DCC-GARCH framework for modelling the multivariate relationships of volatility among markets. Extending the framework of Diebold and Yilmaz [2012] we compute spillover indexes directly from the series of returns considering the time-variant structure of their covariance matrices. Our spillover indexes use daily stock market data of Australia, Canada, China, Germany, Japan, the United Kingdom, and the United States, for the period January 2001 to August 2016. We obtain several relevant results. First, total spillovers exhibit substantial time-series variation, being higher in moments of market turbulence. Second, the net position of each country (transmitter or receiver) does not change during the sample period. However, their intensities exhibit important time-variation. Finally, transmission originates in the most developed markets, as expected. Of special relevance, even though the Chinese stock market has grown importantly over time, it is still a net receiver of volatility spillovers.

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  • Santiago Gamba-Santamaria & Jose Eduardo Gomez-Gonzalez & Jorge Luis Hurtado-Guarin & Luis Fernando Melo-Velandia, 2017. "Volatility Spillovers among Global Stock Markets: Measuring Total and Directional Effects," Borradores de Economia 983, Banco de la Republica de Colombia.
  • Handle: RePEc:bdr:borrec:983
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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. Kristin J. Forbes & Roberto Rigobon, 2002. "No Contagion, Only Interdependence: Measuring Stock Market Comovements," Journal of Finance, American Finance Association, vol. 57(5), pages 2223-2261, October.
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    7. Caccioli, Fabio & Shrestha, Munik & Moore, Cristopher & Farmer, J. Doyne, 2014. "Stability analysis of financial contagion due to overlapping portfolios," Journal of Banking & Finance, Elsevier, vol. 46(C), pages 233-245.
    8. Gamba-Santamaria, Santiago & Gomez-Gonzalez, Jose Eduardo & Hurtado-Guarin, Jorge Luis & Melo-Velandia, Luis Fernando, 2017. "Stock market volatility spillovers: Evidence for Latin America," Finance Research Letters, Elsevier, vol. 20(C), pages 207-216.
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    1. 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.

    More about this item

    Keywords

    Volatility spillovers; DCC-GARCH model; Global 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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