Date of Award
8-2020
Degree Type
Report
Degree Name
Master of Science (MS)
Department
Economics and Finance
Committee Chair(s)
Tyler Brough
Committee
Tyler Brough
Committee
Nicholas Flann
Committee
Pedram Jahangiry
Abstract
This paper focuses on oil hedging using near month crude oil futures. Hedging may allow a firm to reduce risks and focus on areas of comparative advantage. Hedging requires a firm to estimate ex-ante the correct hedge ratio. The portfolio optimization framework allows for OLS to be applied to the estimation of a hedge ratio. Reinforcement Learning is another method available to hedgers to estimate a hedge ratio. Three strategies using econometric tools and one using Reinforcement Learning are estimated and tested against 2019 oil price data.
Recommended Citation
Bullard, Evan, "Reinforcement Learning for Dynamic Futures Hedging" (2020). All Graduate Plan B and other Reports, Spring 1920 to Spring 2023. 1479.
https://digitalcommons.usu.edu/gradreports/1479
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