Modelling Volatility and Price Trends of Quinoa Using Box–Jenkins Time Series Techniques

Abstract

Quinoa has emerged as a high-value food grain in global and domestic markets due to its nutri
tional and commercial importance. However, increasing demand, climatic fluctuations, market uncertainties,
and supply chain disruptions have contributed to significant volatility in quinoa prices. Accurate forecasting
of these prices is essential for farmers, traders, policymakers, and agribusiness stakeholders. This study ap
plies the Box–Jenkins (ARIMA/SARIMA) time-series methodology to model the price trends and volatility
of quinoa. Monthly price data (for a period of X years) were analyzed to identify stationarity, autocorrela
tion patterns, seasonal components, and optimal ARIMA parameters. The ARIMA (p,d,q) model was selected
based on AIC/BIC, residual diagnostics, and forecast accuracy tests (MAE, RMSE, MAPE). Results indicated
that the selected model captured the price dynamics effectively, with significant predictability over short-term
horizons. The findings provide evidence that Box–Jenkins methodology is a robust tool for understanding
quinoa price behaviour and can assist market participants in informed decision-making. The yearly forecasts
counsel that, the price of Quinoa crop with a regular deviation of 13 percent error measure with the accuracy
of 96 percent for the forecasted period of 12 months i.e. 2022-2024.