Prediction based on low-frequency financial data is long-term,depending on the overall economic environment,which is difficult to form accurate prediction.Because of the non-stationary,nonlinear and unique calendar effect of the financial high-frequency data,the traditional ARMA measuring method cannot get a satisfied analytical effect.We introduce a forecast method based on wavelet multi-resolution analysis,which can divide the yield data into high frequency part (periodicity) and low frequency part (tendency).We can reconstruct the separated sequence and make ARMA models.Results show wavelet multi-resolution analysis can filter the intraday effect well.Due to the unique characteristic of the stock index futures,the decomposed layer is 3.The empirical research shows that this reconstruct model improves the prediction precision.
stock index futureswavelet analysisARMA modelpredictiondivide and reconstruct
NETL
NSTL
万方数据
维普
