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Driven by the good results obtained in computer vision, deep generativemethods for time series have been the subject of particular attention in recentyears, particularly from the financial industry. In this article, we focus oncommodity markets and test four state-of-the-art generative methods, namelyTime Series Generative Adversarial Network (GAN) Yoon et al. [2019], CausalOptimal Transport GAN Xu et al. [2020], Signature GAN Ni et al. [2020] and theconditional Euler generator Remlinger et al. [2021], are adapted and tested oncommodity time series. A first series of experiments deals with the jointgeneration of historical time series on commodities. A second set deals withdeep hedging of commodity options trained on he generated time series. This usecase illustrates a purely data-driven approach to risk hedging.

Deep Generators on Commodity Markets; application to Deep Hedging