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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

Journal of Empirical Finance, 2010, vol. 17, issue 3, 460-470

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)
Date: 2010
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Related works:
Working Paper: Long Memory In Stock Market Volatility And The Volatility-in-mean Effect: The Fiegarch-m Model (2009) Downloads
Working Paper: Long Memory in Stock Market Volatility and the Volatility-in-Mean Effect: The FIEGARCH-M Model (2007) Downloads
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