Análise da volatidade do preço da saca de soja utilizando cadeias de Markov

Abstract

This study analyzes the soybean price dynamics in Brazil through the application of Markov Chains. Soybean is the main Brazilian agricultural commodity, and its price volatility represents a persistent challenge for producers and managers, influencing decisions regarding storage, hedging, and crop planning. In response, the Markov Chain methodology, which models probabilistic transitions between price variation states, is employed to understand this variability. The research uses daily data from CEPEA, seeking a balance between information detail and methodological rigor for decision-making. The study is justified by the relevance of addressing a central problem for agribusiness and the applicability of a probabilistic model to support commercialization strategies and risk management. The analysis demonstrated the suitability of Markov Chain modeling for the historical soybean price series. The main results revealed that the stability state has the shortest recurrence time, indicating that the system quickly returns to this state. In contrast, states of greater variation show the longest recurrence times, reinforcing that stability periods are more frequent. This Mean Recurrence Time analysis enables the quantification of the risk of large price fluctuations and directs operational management toward optimizing opportunities. The contribution of the study lies in offering instruments that allow the identification of more advantageous selling windows, contributing to profit maximization, risk reduction, and sector stability.