On the Impact of Feeding Cost Risk in Aquaculture Valuation and Decision
  Making
Christian Oliver Ewald Kevin Kamm
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.