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Long memory in stock market volatility and the volatility-in-mean effect: the FIEGARCH-M model

  • Bent Jesper Christensen

    ()

    (University of Aarhus and CREATES)

  • Jie Zhu

    ()

    (University of Aarhus and CREATES)

  • Morten Ørregaard Nielsen

    ()

    (Queen's University and CREATES)

We extend the fractionally integrated exponential GARCH (FIEGARCH) model for daily stock return data with long memory in return volatility of Bollerslev and Mikkelsen (1996) by introducing a possible volatility-in-mean effect. To avoid that the long memory property of volatility carries over to returns, we consider a filtered FIEGARCH-in-mean (FIEGARCH-M) effect in the return equation. The filtering of the volatility-in-mean component thus allows the co-existence of long memory in volatility and short memory in returns. We present an application to the daily CRSP value-weighted cum-dividend stock index return series from 1926 through 2006 which documents the empirical relevance of our model. The volatility-in-mean effect is significant, and the FIEGARCH-M model outperforms the original FIEGARCH model and alternative GARCH-type specifications according to standard criteria.

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File URL: http://qed.econ.queensu.ca/working_papers/papers/qed_wp_1207.pdf
File Function: First version 2009
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Paper provided by Queen's University, Department of Economics in its series Working Papers with number 1207.

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Length: 22 pages
Date of creation: Jun 2009
Date of revision:
Handle: RePEc:qed:wpaper:1207
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