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Dynamic cross-correlation and dynamic contagion of stock markets: a sliding windows approach with the DCCA correlation coefficient

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Abstract

How stock markets relate to each other is very important because this could have positive effects (such as enhancing economic growth) but also negative effects (possible contagion risks). Considering this issue, this study proposes continuous evaluation of the cross-correlations between markets, applying a sliding windows approach based on the detrended cross-correlation analysis correlation coefficient. Measuring the cross-correlations between the USA and other eight stock markets (the remainder of the G7 plus China and Russia), this allows dynamic analysis of the evolution of cross-correlations and also continuous analysis of the contagion effect. The results show that in the period before the crisis the correlation levels with the US stock market decreased, while post-crisis the results point to a contagion effect. As the proposed approach could be used for continuous monitoring of cross-correlations, this kind of information could be important for the different agents involved in stock markets.

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Acknowledgements

Paulo Ferreira is pleased to acknowledge financial support from Fundação para a Ciência e a Tecnologia under Grant UID/ECO/04007/2019.

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Authors and Affiliations

  1. Faculty of Sciences and Techniques, Cadi Ayyad Univerisity, Marrakech, Morocco

    Oussama Tilfani & My Youssef El Boukfaoui

  2. VALORIZA - Research Center for Endogenous Resource Valorization, Portalegre, Portugal

    Paulo Ferreira

  3. CEFAGE-UE, IIFA, Universidade de Évora, Largo dos Colegiais 2, 7000, Évora, Portugal

    Paulo Ferreira

  4. Instituto Politécnico de Portalegre, Portalegre, Portugal

    Paulo Ferreira

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  1. Oussama Tilfani
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Correspondence to Paulo Ferreira.

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Appendices

Appendix A

See Fig. 6.

Fig. 6
figure 6

Evolution of the detrended cross-correlation coefficient between S&P and the remaining indices, for different time scales, with a windows size of w = 1000

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Appendix B

See Fig. 7.

Fig. 7
figure 7

Significance of the ρDCCA between the S&P and the remaining indices. Dashed lines represent lower and upper critical values, for the respective time scale, for the test of hypotheses H0: ρDCCA = 0 and H1: ρDCCA ≠ 0

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Appendix C

See Fig. 8.

Fig. 8
figure 8

Significance of the ΔρDCCA between the S&P and the remaining indices. Dashed lines represent lower and upper critical values, for the respective time scale, for the test of hypotheses H0: ΔρDCCA = 0 and H1: ΔρDCCA ≠ 0

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Tilfani, O., Ferreira, P. & El Boukfaoui, M.Y. Dynamic cross-correlation and dynamic contagion of stock markets: a sliding windows approach with the DCCA correlation coefficient. Empir Econ 60, 1127–1156 (2021). https://doi.org/10.1007/s00181-019-01806-1

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