Dynamics of Commodities Prices: Integer and Fractional Models

• Type: Article in International Scientific Journal
• Date: 2017

Authors

Sergio A. David (Author) Other; J. A. Tenreiro Machado (Author) Other; Lucas R.Trevisan (Author) Other; Cláudio M.C. Inácio Jr. (Author) Other; António Mendes Lopes (Author) FEUP

Journal

Title: Fundamenta Informaticae

Vol. 151

Pages: 389-408

ISSN: 0169-2968

Publisher: IOS PRESS

Indexing

ISI Web of Knowledge - 12 Citations

ISI Web of Science

Scopus - 18 Citations

Other information

Authenticus ID: P-00M-JVG

DOI: 10.3233/fi-2017-1499

Abstract (EN): This paper examines the time series of four important agricultural commodities, namely the soybean, corn, coffee and sugar prices. Time series can exhibit long- range dependence and persistence in their observation. The long memory feature of data is a documented fact and there has been an increasing interest in studying such concepts in the perspective of economics and finance. In this work, we start by analyzing the time series of the four commodities by means of the Fractional Fourier Transform (FrFT) to unveil time- frequency patterns in the data. In a second phase, we apply Auto Regressive Integrated Moving Average (ARIMA) and Auto Regressive Fractionally Integrated Moving Average (ARFIMA) models for obtaining the spot price composition and predict future price. The ARFIMA process is a known class of long memory model, representing a generalization of the ARIMA algorithm. We compare the performances of the ARIMA and the ARFIMA models and we show that the ARFIMA has a superior performance for future price forecasting.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 20