Some Comments On Fractionally Integration Processes Involving Two Agricultural Commodities

Abstract

This paper investigates time series of soybean and corn, which are two important Brazilian commodities. Long-range dependence or persistence is a behavior seen on times series and currently there is an increasing interest regarding the application of long memory concepts in areas such as economics and finances. A very know type of long memory model is named ARFIMA (Auto Regressive Fractionally Integrated Moving Average) which derives from the ARIMA (Auto Regressive Integrated Moving Average) model. The present work aim to analyze soybeans and corn time series to compose the spot price and forecast future prices for the aforementioned commodities. In order to test the better model for prices prediction, the ARIMA and ARFIMA models were compared. The comparison between the two models has shown that for prices forecasting, ARFIMA model has higher efficiency then ARIMA models.