To solve the higher peak and fat tail phenomenon, immediate memory and asymmetric features, this paper formulate the volatility model of exchange rate returns using the ARFIMA-EGARCH-M model.
为了解决汇率收益率波动中的“尖峰厚尾”、中期记忆和非对称特征，提出了利用ARFIMA - EGARCH - M模型建立汇率收益率波动模型。
Through the analysis of copper time series' characteristics, we found that copper yield rate time series had peak fat-tail characteristic, volatility clustering characteristic and obvious ARCH effect.
通过对沪铜收益率时间序列特征的分析发现，沪铜收益率时间序列存在尖峰厚尾性和波动集群性，并具有明显的ARCH效应。
Furthermore, the peak position can be located more accurately and tail peak can be reconstructed successfully through the improved algorithm and the improved algorithm is robust to noise.
同时，改后算法能够准确定位波峰，完成尾峰重建，具有较强的抗噪性。
Furthermore, the peak position can be located more accurately and tail peak can be reconstructed successfully through the improved algorithm and the improved algorithm is robust to noise.
同时，改后算法能够准确定位波峰，完成尾峰重建，具有较强的抗噪性。
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