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Wavelet multiple correlation and cross-correlation: A multiscale analysis of Eurozone stock markets

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  • Fernández-Macho, Javier

Abstract

Statistical studies that consider multiscale relationships among several variables use wavelet correlations and cross-correlations between pairs of variables. This procedure needs to calculate and compare a large number of wavelet statistics. The analysis can then be rather confusing and even frustrating since it may fail to indicate clearly the multiscale overall relationship that might exist among the variables. This paper presents two new statistical tools that help to determine the overall correlation for the whole multivariate set on a scale-by-scale basis. This is illustrated in the analysis of a multivariate set of daily Eurozone stock market returns during a recent period. Wavelet multiple correlation analysis reveals the existence of a nearly exact linear relationship for periods longer than the year, which can be interpreted as perfect integration of these Euro stock markets at the longest time scales. It also shows that small inconsistencies between Euro markets seem to be just short within-year discrepancies possibly due to the interaction of different agents with different trading horizons.

Suggested Citation

  • Fernández-Macho, Javier, 2012. "Wavelet multiple correlation and cross-correlation: A multiscale analysis of Eurozone stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1097-1104.
  • Handle: RePEc:eee:phsmap:v:391:y:2012:i:4:p:1097-1104 DOI: 10.1016/j.physa.2011.11.002
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    References listed on IDEAS

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    1. Gallegati, Marco, 2008. "Wavelet analysis of stock returns and aggregate economic activity," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3061-3074, February.
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    Cited by:

    1. Berger, Theo, 2015. "A wavelet based approach to measure and manage contagion at different time scales," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 338-350.
    2. repec:eee:eneeco:v:64:y:2017:i:c:p:105-117 is not listed on IDEAS
    3. Gourène, Grakolet Arnold Zamereith & Mendy, Pierre, 2014. "Beginning an African Stock Markets Integration? A Wavelet Analysis," MPRA Paper 76048, University Library of Munich, Germany.
    4. Kaijian He & Rui Zha & Jun Wu & Kin Keung Lai, 2016. "Multivariate EMD-Based Modeling and Forecasting of Crude Oil Price," Sustainability, MDPI, Open Access Journal, vol. 8(4), pages 1-11, April.
    5. Huang, Shupei & An, Haizhong & Gao, Xiangyun & Huang, Xuan, 2015. "Identifying the multiscale impacts of crude oil price shocks on the stock market in China at the sector level," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 434(C), pages 13-24.
    6. repec:agr:journl:v:4(613):y:2017:i:4(613):p:75-88 is not listed on IDEAS
    7. Chakrabarty, Anindya & De, Anupam & Gunasekaran, Angappa & Dubey, Rameshwar, 2015. "Investment horizon heterogeneity and wavelet: Overview and further research directions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 45-61.
    8. Fousekis, Panos & Grigoriadis, Vasilis, 2016. "Spatial price dependence by time scale: Empirical evidence from the international butter markets," Economic Modelling, Elsevier, vol. 54(C), pages 195-204.

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