To solve the higher peakand fat tail phenomenon, immediate memory and asymmetric features, this paper formulate the volatility model of exchange rate returns using the ARFIMA-EGARCH-M model.
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.
Furthermore, the peak position can be located more accurately andtailpeak can be reconstructed successfully through the improved algorithm and the improved algorithm is robust to noise.