ANALYSIS OF THE INFLUENCE OF COVID-19 ON THE PRICES OF BRAZILIAN AGRICULTURAL COMMODITIES USING THE HORIZONTAL VISIBILITY GRAPH METHOD
Agricultural market; Complex networks; Time series; Visibility graphs.
Agricultural commodities are directly associated with agribusiness and play a fundamental role in the Brazilian economy. These goods can be considered more important, as their prices have a significant effect on the population's lives. The objective of this work is to apply complex network theory to characterize the time series of logarithmic returns of sugar and ethanol prices in the pre-COVID-19 pandemic period (2017 - 2020) and post-pandemic period (2020 - 2023). We use the Horizontal Visibility Graph (HVG) method, based on complex network theory, which maps a time series to a network and, with this, it is possible to use graph theory techniques as a way to characterize time series. The software used was VisGraAnalysis, developed in C programming language. We calculated the network indices: clustering coefficient, transitivity, average shortest path length, degree distribution and Kullback-Leibler Divergence, to identify the irreversibility of the series. We observed that the networks generated by HVG present similar structures. We show that, in BRL, the sugar market was more efficient (less predictable), while the ethanol market was less efficient (more predictable). In USD, the sugar market was declared be more efficient in the pre-pandemic period and ethanol in the post-pandemic period. We calculated the Kullback-Leibler Divergence and observed that the degree distributions of the input and output nodes associated with HVG do not coincide and, therefore, the series are not reversible, making it possible to quantify the irreversibility for each period.a