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

Author

Listed:
  • Bent Jesper Christensen

    () (University of Aarhus and CREATES)

  • Jie Zhu

    () (University of Aarhus and CREATES)

  • Morten Ørregaard Nielsen

    () (Queen's University and CREATES)

Abstract

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.

Suggested Citation

  • Bent Jesper Christensen & Jie Zhu & Morten Ørregaard Nielsen, 2009. "Long memory in stock market volatility and the volatility-in-mean effect: the FIEGARCH-M model," Working Papers 1207, Queen's University, Department of Economics.
  • Handle: RePEc:qed:wpaper:1207
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    FIEGARCH; financial leverage; GARCH; long memory; risk-return tradeoff; stock returns; volatility feedback;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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