Abstract
This study examines the validity of the random walk hypothesis for some selected soft agricultural commodity prices within the context of heterogeneous market hypothesis and mean reversion hypothesis. The study employs a battery of traditional unit root tests, GARCH-based models and a novel frequency-based wavelet analysis to analyze daily data sourced from 6th of Jan 1986 to 29th Dec 2018. Contrary to other existing studies that employed only traditional time domain unit root tests, our results reveal that soft commodity prices are mean reverting, suggesting the existence of potential excess returns for investors. Overall, our results show that the selected soft commodity series are inefficient when we factored in heteroscedascity and frequency domain into our model. Our study is an improvement on the existing studies as we analyze our data using both time and frequency domain estimates. Besides, unlike other studies that did not offer structural breaks, the current study provides structural break dates with major events in the global socioeconomic space, which are key to identifying the date of bubbles and potential signs of commodity price bubbles. Our findings have some critical implications for investors, policy makers
Highlights
The unit root behavior of agricultural commodity prices is fast attracting the attention of various economic agents, especially market practitioners and policy makers
This stems from the fact that if there is evidence to show that agricultural commodity prices are non-stationary, chances are that any shock to agricultural commodities will be transmitted to other macroeconomic fundamentals
We extend our analysis by calibrating test for heteroscedastic in the model by employing GARCH-based unit root test such as the (Westerlund & Narayan, 2015) hereafter (W&N, 2015) and (Narayan, Liu, & Westerlund, 2016) hereafter N, L & W, 2016)
Summary
The unit root behavior of agricultural commodity prices is fast attracting the attention of various economic agents, especially market practitioners and policy makers. This stems from the fact that if there is evidence to show that agricultural commodity prices are non-stationary, chances are that any shock to agricultural commodities will be transmitted to other macroeconomic fundamentals. Evidence of stationarity implies that future movement in agricultural commodity prices based on historical evidence is predictable. Under this condition, where commodity prices are predictable, it can be argued empirically that agricultural commodity prices are mean reverting (Brooks, Prokopczuk, Wu, 2015; Alexakis, Bagnarosa, & Dowling, 2017; Ganneval, 2016; Lawal, Fidelis, Babajide, Obasaju, Oyetade, Lawal-Adedoyin, Ojeka, and Olaniru, 2018; Barbaglia, Wilms, & Croux, 2016; Biao Guo, 2018; Tse, 2018; Lawal, Somoye, & Babajide, 2018)
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