Forecasting the Behavior of Peruvian Coffee Export Prices in International Markets Using Econometric Models, 2010–2025

Coffee export price volatility is a relevant problem for producing countries because it affects commercial planning, contract negotiation, producers’ income stability, and the financial sustainability of the agro-export value chain. In economies that are highly dependent on primary commodities, abrupt fluctuations in international prices increase uncertainty and reduce the ability of economic agents to anticipate market behavior. In this context, the objective of this study was to forecast the behavior of the Peruvian coffee export price during 2025 by comparing econometric and time-series models. The research adopted a quantitative approach with a non-experimental, retrospective, and longitudinal design, using a monthly series for the 2010–2024 period. Seven specifications were estimated: linear model, quadratic model, Holt–Winters exponential smoothing, causal model, lagged model, ARIMA, and GARCH. The results showed that the GARCH (1,1) model achieved the best statistical performance, with the lowest Akaike Information Criterion, a Durbin–Watson statistic close to 2, an R2 higher than that of the alternative models, and no residual autocorrelation. Likewise, the significance of the ARCH and GARCH components confirmed the existence of volatility clustering in the series. The projections for 2025 show a fluctuating trajectory, although with a tendency to stabilize around values close to 10 from March onward. It is concluded that the GARCH (1,1) model is the most appropriate specification for forecasting the Peruvian coffee export price, as it provides a useful tool for export planning, risk management, and decision-making in a context of high uncertainty in the coffee market.