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英文文献：Long Memory in Stock Market Volatility and the Volatility-in-Mean Effect: The FIEGARCH-M Model-股票市场波动的长记忆与平均波动效应:FIEGARCH-M模型
英文文献作者：Bent Jesper Christensen,Morten ?rregaard Nielsen,Jie Zhu
英文文献摘要：
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.
在Bollerslev和Mikkelsen(1996)的收益波动中，我们通过引入一种可能的平均波动效应，对具有长记忆的日股票收益数据的分指数集成GARCH (FIEGARCH)模型进行了扩展。为了避免波动性的长记忆特性延续到回报，我们考虑了在回报方程中过滤的FIEGARCH-in-mean (FIEGARCH-M)效应。对平均波动分量的过滤使得波动中的长记忆和回报中的短记忆共存。我们对1926年至2006年的每日CRSP价值加权加股息股票指数回报序列进行了应用，证明了我们的模型的经验相关性。平均波动率效应显著，FIEGARCH- m模型根据标准标准优于原始的FIEGARCH模型和其他garch类型规范。
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