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Volatility spillovers among global stock markets: measuring total and directional effects

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 modeling the multivariate relationships of volatility among markets. Extending the framework of Diebold and Yilmaz (Int J Forecast 28(1):57–66, 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 UK, and the USA, for the period April 1996–June 2017. 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. However, the magnitude of net volatility reception has decreased over the last few years.

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Notes

  1. The stock market indexes used are S&P/ASX 200 (AS51), S&P/Toronto Stock Exchange Composite Index (SPTSX), Hang Seng Index (HSI), German Stock Index (DAX), Nikkei-225 stock average (NKY), FTSE 100 Index (UKX) and Dow Jones Industrial Average (INDU), respectively.

  2. Our results are robust to other window sizes (100 and 500) and other forecast horizons (5–10 days).

  3. Table 4 in Appendix A contains specification tests of the DCC-GARCH results of the last rolling estimation.

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Authors and Affiliations

Authors

Corresponding author

Correspondence to Jose Eduardo Gomez-Gonzalez.

Additional information

We thank Bertrand Candelon, anonymous referees, Carmine Tecroci and conference participants at the 2017 Infinity Conference on International Finance for valuable comments and discussions on an earlier version of this paper. Disclaimer: The findings, recommendations, interpretations and conclusions expressed in this paper are those of the authors and not necessarily reflect the view of the Banco de la República or its Board of Directors.

Appendices

Specification tests

See Table 4.

Table 4 Ljung–Box tests on the DCC-GARCH errors (p-values)

Pairwise spillover indexes

See Figs. 4, 5 and 6.

Fig. 4
figure 4

Pairwise spillover index between USA and the rest of the system. Pairwise spillover indexes are the difference between the volatility transmitted from one market to another and the volatility received by that one market from the other. Hence when the index is positive the first market transmits more volatility to the second, whereas it is negative, the second markets send more volatility to the first one

Fig. 5
figure 5

Pairwise spillover index between UK, Germany and the rest of the system. Pairwise spillover indexes are the difference between the volatility transmitted from one market to another and the volatility received by that one market from the other. Hence when the index is positive the first market transmits more volatility to the second, whereas it is negative, the second markets send more volatility to the first one

Fig. 6
figure 6

Pairwise spillover index among the rest of the system. Pairwise spillover indexes are the difference between the volatility transmitted from one market to another and the volatility received by that one market from the other. Hence when the index is positive the first market transmits more volatility to the second, whereas it is negative, the second markets send more volatility to the first one

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Gamba-Santamaria, S., Gomez-Gonzalez, J.E., Hurtado-Guarin, J.L. et al. Volatility spillovers among global stock markets: measuring total and directional effects. Empir Econ 56, 1581–1599 (2019). https://doi.org/10.1007/s00181-017-1406-3

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  • DOI: https://doi.org/10.1007/s00181-017-1406-3

Keywords

  • Volatility spillovers
  • DCC-GARCH model
  • Global stock market linkages
  • Financial crisis

JEL Classification

  • G01
  • G15
  • C32