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Volatility and autocorrelation in major European stock markets

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  • G. Geoffrey Booth
  • Gregory Koutmos
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    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.

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    Bibliographic Info

    Article provided by Taylor & Francis Journals in its journal The European Journal of Finance.

    Volume (Year): 4 (1998)
    Issue (Month): 1 ()
    Pages: 61-74

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    Handle: RePEc:taf:eurjfi:v:4:y:1998:i:1:p:61-74

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    Related research

    Keywords: EAR-EGARCH model; volatility; autocorrelation; European stock markets;

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    Cited by:
    1. A. Sensoy & Benjamin Miranda 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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