Dynamics of Commodities Prices: Integer and Fractional Models

• Tipo: Artigo em Revista Científica Internacional
• Data: 2017

Autores

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

Revista

Título: Fundamenta Informaticae

Vol. 151

Páginas: 389-408

ISSN: 0169-2968

Editora: IOS PRESS

Indexação

ISI Web of Knowledge - 12 Citações

ISI Web of Science

Scopus - 18 Citações

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 20