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Forecasting Of Oil And Agricultural Commodity Prices: Varma Versus Arma

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  • MUSTAFA GÜLERCE

    (Financial Economics Programme, Yeditepe University, İnönü Mah. Kayışdağı Cad. 326, 26 Ağustos Yerleşimi, 34755 Ataşehir, İstanbul, Turkey)

  • GAZANFER ÜNAL

    (Financial Economics Programme, Yeditepe University, İnönü Mah. Kayışdağı Cad. 326, 26 Ağustos Yerleşimi, 34755 Ataşehir, İstanbul, Turkey)

Abstract

The aim of this paper is to show that the estimates made with vector autoregressive–moving-average (ARMA) models based on the coherent time intervals of the multiple time series give more precise results than the univariate case. The previous literature on dynamic correlations (co-movement) in between food and energy prices has mixed results and mainly based on parametric approaches. Therefore, partial wavelet coherence (PWC) and multiple wavelet coherence (MWC) methods are used, respectively, to uncover the coherency simultaneously for time and frequency domains. In our study; world oil, corn, soybeans, wheat and sugar prices are examined instead of the return and volatility relationship between oil and agricultural commodities due to model-free approach of wavelet analysis.

Suggested Citation

  • Mustafa Gülerce & Gazanfer Ünal, 2017. "Forecasting Of Oil And Agricultural Commodity Prices: Varma Versus Arma," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 12(03), pages 1-30, September.
    • Handle: RePEc:wsi:afexxx:v:12:y:2017:i:03:n:s2010495217500129
    DOI: 10.1142/S2010495217500129
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