Date of Award


Degree Type


Degree Name

Master of Science (MS)


Economics and Finance

First Advisor

Tyler Brough

Second Advisor

Ben Blau

Third Advisor

Ryan Whitby


This project used historical spot and futures price data of two fairly closely correlated commodities to simulate many possible spot and futures price paths that could have occurred over a given time frame. These simulated prices were then used to test various commodity storage strategies available through futures contracts. This paper explains how results, including values such as the mean terminal cumulative profit and the standard deviation of the mean terminal cumulative profits for each strategy, can be interpreted to help determine a local company’s optimal storage strategy.

This paper specifically provides a way for a local company to find this optimal storage solution for 3 products they sell. When this company runs the given Python computer code files using their historical inventory level data and historical sales data, they can follow the example analysis in this paper to help decide how their company should utilize futures contracts to store each of the 3 commodities.

This project assumes that the company periodically adjusts the number of futures contracts they use based on the company’s inventory level of a commodity. This would require the inventory level and the number of futures positions to be monitored by the company. This management of tracking inventory and adjusting the number of futures contracts being used could take both extra time and extra money to pay employees for that time, which presents two potential challenges for the implication of the results of this project. However, this project could serve as an important aid to this company, because it may help them obtain higher profits. This helps not only the company’s bottom line, but also the employees working for this company and the community in which the company is located.

Available for download on Tuesday, December 13, 2022