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In this paper we exploit the wavelet analysis approach to investigateoil-food price correlation and its determinants in the domains of time andfrequency. Wavelet analysis is able to differentiate high frequency from lowfrequency movements which correspond, respectively, to short and long rundynamics. We show that the significant local correlation between food and oilis only apparent and this is mainly due both to the activity of commodity indexinvestments and, to a lesser extent, to a growing demand from emergingeconomies. Moreover, the activity of commodity index investments gives evidenceof the overall financialisation process. In addition, we employ wavelet entropyto assess the predictability of the time series under consideration atdifferent frequencies. We find that some variables share a similarpredictability structure with food and oil. These variables are the ones thatmove the most along with oil and food. We also introduce a novel measure, theCross Wavelet Energy Entropy Measure (CWEEM), based on wavelet transformationand information entropy, with the aim of quantifying the intrinsicpredictability of food and oil given demand from emerging economies, commodityindex investments, financial stress, and global economic activity. The resultsshow that these dynamics are best predicted by global economic activity at allfrequencies and by demand from emerging economies and commodity indexinvestments at high frequencies only.

Wavelet analysis and energy-based measures for oil-food price relationship as a footprint of financialisation effect