Forecasting High-Frequency Long Memory Series with Long Periods Using the SARFIMA Model

Handong Li, Xunyu Ye

School of Government, Beijing Normal University, Beijing, China.

DOI: 10.4236/ojs.2015.51009                

Abstract

This paper evaluates the efficiency of the SARFIMA model at forecasting high-frequency long memory series with especially long periods. Three other models, the ARFIMA, ARMA and PAR models, are also included to compare their forecasting performances with that of the SARFIMA model. For the artificial SARFIMA series, if the correct parameters are used for estimating and forecasting, the model performs as well as the other three models. However, if the parameters obtained by the WHI estimation are used, the performance of the SARFIMA model falls far behind that of the other models. For the empirical intraday volume series, the SARFIMA model produces the worst performance of all of the models, and the ARFIMA model performs best. The ARMA and PAR models perform very well both for the artificial series and for the intraday volume series. This result indicates that short memory models are competent in forecasting periodic long memory series.

Keywords

High-Frequency Financial Series, Long Memory, Long Periods, SARFIMA, Monte Carlo Simulation, Intraday Volume

Conflicts of Interest

The authors declare no conflicts of interest.

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