FORECASTING OF OIL AND AGRICULTURAL COMMODITY PRICES: VARMA VERSUS ARMA

Mustafa Gülerce () and Gazanfer Ünal
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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

Annals of Financial Economics (AFE), 2017, vol. 12, issue 03, 1-30

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.

Keywords: Wavelet analysis; co-movement; wavelet transform; de-noising; wavelet coherence; discrete and continuous wavelet transform; ARMA (autoregressive moving average); VARMA (vector autoregressive moving average); forecasting (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1142/S2010495217500129

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