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