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。
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