A State‐Space Forecasting Approach to Optimal Intertemporal Cross‐Hedging

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Abstract
Cross‐commodity hedging between fishmeal and soybean meal is investigated. The approach uses successively updated out‐of‐sample forecasts to approximate subjective price expectations, and forecast error variance‐covariance matrices to measure risk. Forecasts are generated by state‐space models of vector‐valued time series. In a stationary environment, uncertainty reduces to the difference between the historical autocovariance of the random process and the variance‐covariance of out‐of‐sample forecasts. Results indicate that weakly risk‐averse agents can increase average marketing returns within acceptable risk levels by combining information from price forecasting models with an appropriate hedging strategy.

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