Forecasting and managing price volatility in salmon production: A hybrid system using conformal prediction and dynamic hedging

Risk awareness has become critical for effective, data-driven decision-making, particularly in current volatile business environments. However, as the technological transformation of production systems evolves, forecasting and quantifying risk remain challenging. Such a challenge is especially relevant in food production systems, particularly in aquaculture, an industry characterized by volatility and underdeveloped risk management, despite its potential as a sustainable alternative to fisheries. To respond to this need, this study proposes a hybrid framework for forecasting and adaptively managing price volatility, tailored to the operational context of the salmon production industry. There are, though, both technical and practical challenges: although machine learning methods have proven effective for time series forecasting in many contexts, they often lack actionable measures of uncertainty, and their application in aquaculture remains limited. Thus, we develop a two-step approach, that first applies a forecasting model enhanced with Conformal Prediction, a model-agnostic technique that generates prediction intervals with valid coverage in finite samples. Secondly, we use those prediction intervals to inform an adaptive hedging strategy based on the Dynamic Portfolio Insurance method applied to the estimated production value. Results show that, when applied to the Atlantic salmon industry using actual spot and futures data, the proposed approach effectively mitigates downside risks while preserving upside potential. This way, we unify predictive modeling and risk mitigation in a framework tailored to the sector's operations. This makes short-term price forecasts actionable and, when embedded within classical aquaculture growth simulations, supports volatility-informed adaptive hedging, contributing to more resilient, risk-aware, and data-driven production strategies.