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Volatility and autocorrelation in major European stock markets

Author

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  • G. Geoffrey Booth
  • Gregory Koutmos

Abstract

This paper models index stock returns for four major European stock markets as conditionally heteroskedastic processes with time dependent serial correlation. The evidence suggests that current returns in these markets are nonlinearly dependent on their past history. The dependence is strong during calm periods and weak during volatile periods and manifests itself as an inverse relationship between first order autocorrelations and volatility. While this relationship is statistically significant in daily returns, it is absent from weekly returns. Additional tests reveal that the nonlinear specification used by LeBaron (1992) is not necessarily the most adequate representation of the short-term dynamics of stock index returns.

Suggested Citation

  • G. Geoffrey Booth & Gregory Koutmos, 1998. "Volatility and autocorrelation in major European stock markets," The European Journal of Finance, Taylor & Francis Journals, vol. 4(1), pages 61-74.
    • Handle: RePEc:taf:eurjfi:v:4:y:1998:i:1:p:61-74
    DOI: 10.1080/13518479800000003
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    Citations

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    Cited by:

    1. Sensoy, Ahmet & Tabak, Benjamin M., 2015. "Time-varying long term memory in the European Union stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 147-158.
    2. Gebka, Bartosz & Henke, Harald & Bohl, Martin T., 2006. "Institutional trading and stock return autocorrelation: Empirical evidence on Polish pension fund investors' behavior," Global Finance Journal, Elsevier, vol. 16(3), pages 233-244, March.
    3. Mensi, Walid & Tiwari, Aviral Kumar & Al-Yahyaee, Khamis Hamed, 2019. "An analysis of the weak form efficiency, multifractality and long memory of global, regional and European stock markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 72(C), pages 168-177.
    4. A. Sensoy & Benjamin M. Tabak, 2013. "How much random does European Union walk? A time-varying long memory analysis," Working Papers Series 342, Central Bank of Brazil, Research Department.

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