A deep learning model for gas storage optimization

2021/02/03 by Curin, Nicolas, Kettler, Michael, Kleisinger-Yu, Xi +4 · 2 citations
#65K99 #91G60 #Computational Finance (q-fin.CP) #FOS: Economics and business

paper · doi:10.48550/arxiv.2102.01980

Abstract

To the best of our knowledge, the application of deep learning in the field of quantitative risk management is still a relatively recent phenomenon. In this article, we utilize techniques inspired by reinforcement learning in order to optimize the operation plans of underground natural gas storage facilities. We provide a theoretical framework and assess the performance of the proposed method numerically in comparison to a state-of-the-art least-squares Monte-Carlo approach. Due to the inherent intricacy originating from the high-dimensional forward market as well as the numerous constraints and frictions, the optimization exercise can hardly be tackled by means of traditional techniques.

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• Natural-gas storage modelling by deep reinforcement learning
• Deep Hedging Under Non-Convexity: Limitations and a Case for AlphaZero

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