We study the effect of stochastic feeding costs on animal-based commodities
with particular focus on aquaculture. More specifically, we use soybean futures
to infer on the stochastic behaviour of salmon feed, which we assume to follow
a Schwartz-2-factor model. We compare the decision of harvesting salmon using a
decision rule assuming either deterministic or stochastic feeding costs, i.e.
including feeding cost risk. We identify cases, where accounting for stochastic
feeding costs leads to significant improvements as well as cases where
deterministic feeding costs are a good enough proxy. Nevertheless, in all of
these cases, the newly derived rules show superior performance, while the
additional computational costs are negligible. From a methodological point of
view, we demonstrate how to use Deep-Neural-Networks to infer on the decision
boundary that determines harvesting or continuation, improving on more
classical regression-based and curve-fitting methods. To achieve this we use a
deep classifier, which not only improves on previous results but also scales
well for higher dimensional problems, and in addition mitigates effects due to
model uncertainty, which we identify in this article. effects due to model
uncertainty, which we identify in this article.

我们研究随机饲料成本对动物类商品的影响，尤其关注水产养殖。我们使用大豆期货来推测鲑鱼饲料的随机行为，假设其遵循 Schwartz-2 因子模型。我们比较了在考虑确定性或随机饲料成本（即包括饲料成本风险）的决策规则下进行捕捞鲑鱼的情况。我们确定了在考虑随机饲料成本时能够显著改善的情况，以及确定确定性饲料成本是良好代理的情况。然而，在所有这些情况下，新推导出的规则都表现出优越的性能，而额外的计算成本可以忽略不计。从方法论的角度来看，我们展示了如何使用深度神经网络来推断决策边界，这决定了收获还是继续，改进了传统的基于回归和曲线拟合的方法。为了实现这一点，我们使用了深度分类器，不仅改进了以前的结果，而且在处理高维问题时也具有良好的可扩展性，并且缓解了该文章中识别到的模型不确定性的影响。