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Long Memory in Stock Market Volatility and the Volatility-in-Mean Effect: The FIEGARCH-M Model

Bent Jesper Christensen (), Morten Nielsen () and Jie Zhu

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

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.

Keywords: FIEGARCH; financial leverage; GARCH; long memory; risk-return tradeoff; stock returns; volatility feedback (search for similar items in EconPapers)
JEL-codes: C22 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-fmk
Date: 2007-06-12
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Related works:
Journal Article: Long memory in stock market volatility and the volatility-in-mean effect: The FIEGARCH-M Model (2010) Downloads
Working Paper: Long Memory In Stock Market Volatility And The Volatility-in-mean Effect: The Fiegarch-m Model (2009) Downloads
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