A State‐Space Forecasting Approach to Optimal Intertemporal Cross‐Hedging
研究鱼粉和豆粕之间的跨商品对冲，利用状态空间模型生成预测和风险度量，发现弱风险规避者可通过结合价格预测模型与对冲策略提高平均营销回报。
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.