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Interdependence Between Some Major European Stock Markets - A Wavelet Lead/Lag Analysis

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  • Silvo Dajčman

Abstract

This paper investigates multiscale interdependence between the stock markets of Germany, Austria, France, and the United Kingdom. Wavelet energy additive decomposition was analyzed to investigate which scales capture the most energy (volatility), whereas a wavelet cross-correlation estimator was used to analyze comovement and lead/lag relationship between stock markets' return dynamics on a scale-by-scale basis. The main findings of the paper are as follows. First, major financial market crises had a significant impact on return volatility of investigated stock markets. Among them, the global financial crisis of 2007-2008 had the greatest and the most durable impact. Second, the lowest scale (associated with stock markets' return dynamics over a 2-4 days horizon) and the second lowest scale (associated with stock markets' return dynamics over 4-8 days horizon) MODWT (maximal overlap discrete wavelet transform) decompositions of stock markets' returns captured the greatest share (together about 70-80%) of indices' returns volatility. Third, comovement between stock market returns is a scale-dependent phenomenon. Fourth, a strong comovement between stock market returns of Germany, France, and the United Kingdom exists at all scales, while the Austrian stock market is less correlated with the three biggest stock markets in Europe. Fifth, the dynamics of stock market returns seems to be well time-synchronized at daily (raw returns) and the lowest scale (scale ) return decomposition as most of the return innovations are transmitted between stock markets intraday. Sixth, at the highest investigated scale (associated with stock markets' return dynamics over a 64-128 days horizon), significant leads and lags between dynamics of stock markets' returns were detected. The time-synchronization of the stock markets' return dynamics for investments of 64 to 128 days horizon is less perfect than for investments of shorter investment horizons.

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  • Silvo Dajčman, 2013. "Interdependence Between Some Major European Stock Markets - A Wavelet Lead/Lag Analysis," Prague Economic Papers, Prague University of Economics and Business, vol. 2013(1), pages 28-49.
    • Handle: RePEc:prg:jnlpep:v:2013:y:2013:i:1:id:439:p:28-49
    DOI: 10.18267/j.pep.439
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    5. Olivier Niyitegeka & Dev D Tewari, 2019. "Modelling Financial Contagion in the South African Equity Markets Following the Subprime Crisis," Journal of Economics and Behavioral Studies, AMH International, vol. 10(6), pages 164-176.
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    7. P. Lakshmi & M. Thenmozhi, 2018. "Impact of foreign institutional investor trades in Indian equity and debt market: a three-dimensional analysis," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 45(3), pages 225-233, September.
    8. Jia, Xiaoliang & An, Haizhong & Sun, Xiaoqi & Huang, Xuan & Gao, Xiangyun, 2016. "Finding the multipath propagation of multivariable crude oil prices using a wavelet-based network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 331-344.
    9. Wen, Danyan & Wang, Gang-Jin & Ma, Chaoqun & Wang, Yudong, 2019. "Risk spillovers between oil and stock markets: A VAR for VaR analysis," Energy Economics, Elsevier, vol. 80(C), pages 524-535.
    10. Beccar-Varela, Maria P. & Mariani, Maria C. & Tweneboah, Osei K. & Florescu, Ionut, 2017. "Analysis of the Lehman Brothers collapse and the Flash Crash event by applying wavelets methodologies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 474(C), pages 162-171.
    11. Jasmina Ðuraškovic & Slavica Manic & Dejan Živkov, 2019. "Multiscale Volatility Transmission and Portfolio Construction Between the Baltic Stock Markets," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 69(2), pages 211-235, April.
    12. Selcuk Bayraci & Sercan Demiralay & Hatice Gaye Gencer, 2018. "Stock†Bond Co†Movements And Flight†To†Quality In G7 Countries: A Time†Frequency Analysis," Bulletin of Economic Research, Wiley Blackwell, vol. 70(1), pages 29-49, January.
    13. Yu, Hui & Ding, Yinghui & Sun, Qingru & Gao, Xiangyun & Jia, Xiaoliang & Wang, Xinya & Guo, Sui, 2021. "Multi-scale comovement of the dynamic correlations between copper futures and spot prices," Resources Policy, Elsevier, vol. 70(C).
    14. Atskanov, Isuf, 2015. "Dynamic optimization of an investment portfolio on European stock markets using pair copulas," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 40(4), pages 84-105.
    15. Sun, Qingru & Gao, Xiangyun & An, Haizhong & Guo, Sui & Liu, Xueyong & Wang, Ze, 2021. "Which time-frequency domain dominates spillover in the Chinese energy stock market?," International Review of Financial Analysis, Elsevier, vol. 73(C).

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    More about this item

    Keywords

    stock markets; wavelet analysis; wavelet cross-correlation; return spillovers;
    All these keywords.

    JEL classification:

    • F36 - International Economics - - International Finance - - - Financial Aspects of Economic Integration
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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